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Melanoma: Clinical features and diagnosis

Melanoma: Clinical features and diagnosis
Literature review current through: Jan 2024.
This topic last updated: Oct 04, 2023.

INTRODUCTION — Melanoma is the most serious form of skin cancer [1]. In the United States, it is the fifth most common cancer in males and females [2]; its incidence increases with age. As survival rates for people with melanoma depend on the stage of the disease at the time of diagnosis, early diagnosis is crucial to improve patient outcome and save lives. Although most melanomas are detected by patients themselves [3,4], clinician detection is associated with thinner, more curable tumors [5-8]. Most patients with thin, invasive melanoma (Breslow thickness ≤1 mm) can expect prolonged disease-free survival and likely cure following treatment [9].

This topic will discuss the clinical features and diagnosis of cutaneous melanoma. The principles and rationale of screening and early detection of melanoma are discussed separately, as are the histopathologic features, initial management, and staging of melanoma. The clinical features, diagnosis, and management of mucosal melanoma, ocular melanoma, and melanoma in children are also discussed separately.

(See "Screening for melanoma in adults and adolescents".)

(See "Pathologic characteristics of melanoma".)

(See "Surgical management of primary cutaneous melanoma or melanoma at other unusual sites".)

(See "Tumor, node, metastasis (TNM) staging system and other prognostic factors in cutaneous melanoma".)

(See "Locoregional mucosal melanoma: Epidemiology, clinical diagnosis, and treatment".)

(See "Initial management of uveal and conjunctival melanomas".)

(See "Melanoma in children".)

MELANOMA SUBTYPES AND ASSOCIATED FEATURES

Overview — The current taxonomy of cutaneous melanoma is influenced by site of origin (epithelium associated versus nonepithelium associated), role of cumulative sun damage (CSD; high CSD related, low CSD related, or non-CSD related), mole phenotype (high versus low nevus count), and frequency of BRAF, NRAS, and other relevant mutations [1]. This has resulted in the revised 2018 World Health Organization (WHO) classification of skin tumors, which includes low CSD melanoma, high CSD melanoma, desmoplastic melanoma, Spitz melanoma, acral melanoma, mucosal melanoma, melanoma arising in congenital nevus, melanoma arising in blue nevus, and uveal melanoma (table 1) [10].

While histopathologic reporting of melanoma subtype is considered optional per international melanoma pathology guidelines, previously described clinicopathologic subtypes may by useful for recognition and diagnosis by clinicians [11-14]. According to the traditional morphologic classification of melanoma, there are four major subtypes of invasive cutaneous melanoma: superficial spreading (picture 1), nodular melanoma (picture 2), lentigo maligna (picture 5D), and acral lentiginous (picture 6C).

Most melanomas arise as superficial tumors that are confined to the epidermis, where they may remain for several to many years. During this stage, known as the horizontal or "radial" growth phase, the melanoma is in most cases curable by surgical excision alone. (See "Pathologic characteristics of melanoma", section on 'Growth phases of melanoma'.)

Melanomas that infiltrate into the dermis are considered to be in a "vertical" growth phase and have metastatic potential. Nodular melanomas have no identifiable radial growth or in situ phase and appear to enter the vertical growth phase from their inception, resulting in thicker tumors at diagnosis. The probability of metastases with invasive, vertical growth-phase melanoma is most strongly predicted by measuring the thickness of the tumor (ie, Breslow depth), in millimeters, from the granular cell layer of the epidermis (or overlying area of ulceration) to the deepest malignant cell in the dermis or subcutaneous fat [15].

Superficial spreading melanoma — Superficial spreading melanoma is the most common histologic subtype, accounting for approximately 70 percent of all melanomas [10,16]. Over 60 percent of superficial spreading melanomas are diagnosed as thin, highly curable tumors that are ≤1 mm in thickness [17]. Approximately two-thirds of all melanomas arise de novo without an associated nevus [18], though superficial spreading melanoma is the subtype most likely to be associated with a pre-existing nevus. Superficial spreading melanoma can occur in any anatomic location but has a predilection for the back in males and females and the lower extremities in females.

Superficial spreading melanoma typically presents as a variably pigmented macule or thin plaque with an irregular border, ranging from a few millimeters to several centimeters in diameter (picture 3A-D). Lesions may have multiple shades of brown, red, blue, black, gray, and white. The pathologic features of superficial spreading melanoma are described separately. (See "Pathologic characteristics of melanoma", section on 'Superficial spreading melanoma'.)

Nodular melanoma — Nodular melanoma is the second most common type, accounting for 15 to 30 percent of all melanomas [16]. They may appear as darkly pigmented, pedunculated, or polypoid papules or nodules but frequently present with uniform color or amelanotic/pink hue, symmetric borders, and a relatively small diameter, making early detection difficult (picture 4A-C) [19-22]. While the majority of superficial spreading melanomas and lentigo maligna melanomas are diagnosed at less than 1 mm thickness, most nodular melanomas are thicker than 2 mm at the time of the diagnosis. Likewise, over 50 percent of melanomas greater than 2 mm in thickness are the nodular subtype [22].

The pathologic features of nodular melanoma are discussed separately. (See "Pathologic characteristics of melanoma", section on 'Nodular melanoma'.)

Lentigo maligna melanoma — Lentigo maligna melanoma most commonly arises in chronically sun-damaged areas of the skin in older individuals and begins as a tan or brown macule [23]. The lesion gradually enlarges over years and may develop darker, asymmetric foci of pigmentation, color variegation, and raised areas that signify vertical growth within the precursor in situ melanoma, which is termed "lentigo maligna" (picture 5A-D). Lentigo maligna melanoma accounts for 10 to 15 percent of all melanomas, although the incidence is rising in the United States [16,24], particularly in older individuals. The pathologic features of lentigo maligna melanoma are discussed separately. (See "Lentigo maligna: Clinical manifestations, diagnosis, and management" and "Pathologic characteristics of melanoma", section on 'Lentigo maligna melanoma'.)

Acral lentiginous melanoma — The acral lentiginous subtype accounts for less than 5 percent of all melanomas [16]. However, it accounts for approximately 75 percent of melanomas among non-White individuals, who are at lower risk for more sun-related melanoma subtypes [25].

Acral lentiginous melanomas arise most commonly on palmar, plantar, and subungual surfaces (beneath the nail plate). Acral lentiginous melanomas first appear as dark brown to black, irregularly pigmented macules or patches (picture 6A-C) [26], with raised areas, ulceration, bleeding, and/or larger diameter generally signifying deeper invasion in the dermis. Occasionally, acral melanoma can present as amelanotic or hypomelanotic lesions mimicking benign diseases, such as warts, calluses, tinea pedis, nonhealing ulcers, or ingrown toenails [27-29].

Subungual melanoma arises from the nail matrix and usually presents as a longitudinal, brown or black band in the fingernail or toenail, with or without nail dystrophy (picture 7A-D). Subungual melanoma may present as a mass below the nail plate (with or without pigmentation) with ulceration and nail plate destruction. It can also mimic conditions such as onychomycosis or paronychia, which leads to delay in diagnosis [30]. Since pigmented, longitudinal bands within the nail plate can be observed in a number of benign nail conditions, dermatology specialist consultation is often necessary for diagnosis [31]. (See "Longitudinal melanonychia" and "Dermoscopy of nail pigmentations", section on 'Melanoma'.)

The pathologic features of acral lentiginous melanoma are discussed separately. (See "Pathologic characteristics of melanoma", section on 'Acral lentiginous melanoma'.)

Uncommon variants

Amelanotic melanoma — All melanoma subtypes may present as amelanotic or hypomelanotic lesions clinically, though this is most commonly observed with nodular and desmoplastic subtypes. Although amelanotic melanoma is less common than clinically pigmented melanoma, representing approximately 2 to 10 percent of cases, it poses serious diagnostic challenges for patients and clinicians alike [32]. Lesions may present as pink or red macules, papules, or nodules, often with well-defined borders (picture 4A, 4D) [33-36]. Some tumors may present with a subtle, light-brown pigmentation. Amelanotic melanomas are often clinically confused with benign lesions (eg, dermal nevus, inflamed seborrheic keratosis, ruptured hair follicle or cyst, hemangioma, pyogenic granuloma), often leading to considerable delay in the diagnosis and potential worse prognosis.

Spitzoid melanoma — The term "spitzoid melanoma" has been used to indicate a subset of melanomas that have a morphologic resemblance to Spitz tumors, both clinically and histologically [37]. These lesions usually present as growing papules or nodules that can be red (amelanotic) or have a brown, black, or blue color. Although spitzoid melanomas generally have more severe histologic atypia than atypical Spitz tumors, histologic differentiation of these lesions may be challenging, often requiring additional molecular tests (table 2). (See "Spitz nevus and atypical Spitz tumors".)

Desmoplastic melanoma — Desmoplastic melanoma is a rare but histologically and clinically distinct variant of melanoma [38]. It presents as a slowly growing plaque, nodule, or scar-like growth, and it is usually amelanotic and located in chronically sun-exposed areas of older patients. Desmoplastic melanoma may clinically simulate a scar, other benign process, or nonmelanoma skin cancer (eg, basal cell or squamous cell carcinoma) and so tends to be diagnosed when the tumor is thicker [39].

Pigment synthesizing (animal-type) melanoma — Pigment synthesizing (animal-type) melanoma (also termed "melanocytoma") is a rare subtype of melanoma comprised of heavily pigmented, dermal, epithelioid and spindled melanocytes [40,41]. It presents as a blue-black or blue, slow-growing nodule most frequently located on the extremities and, less commonly, on the head/neck and trunk. Animal-type melanoma is considered an indolent type of melanoma, with a low incidence of metastasis and low mortality rate, despite a high frequency of positive sentinel lymph nodes. The histopathologic diagnosis may be difficult due to overlapping features with other dermal melanocytic proliferations, such as common blue nevus, cellular blue nevus, malignant blue nevus, Spitz nevus, and atypical Spitz tumors. (See "Acquired melanocytic nevi (moles)" and "Spitz nevus and atypical Spitz tumors".)

CLINICAL DIAGNOSIS — The clinical recognition of melanoma and, in particular, of early melanoma may be challenging, even for the most experienced dermatologist.

It has been estimated that the sensitivity of the clinical diagnosis of experienced dermatologists by the naked eye is approximately 70 percent [42]. However, the use of diagnostic aids such as dermoscopy, which requires some training, may greatly improve the sensitivity and specificity of the clinical diagnosis [43]. (See 'Dermoscopy' below.)

History and risk factors — Key questions that should be asked to patients presenting with a lesion that is of concern or for a general examination of their nevi include:

When was the lesion (or a change in a pre-existing lesion) first noticed?

Has the lesion changed over time in size, shape, color, and/or symptoms (eg, bleeding, itch)?

Does the patient have a personal or family history of melanoma or other skin cancers?

Does the patient have a history of excessive sun exposure and/or tanning bed use?

Did the patient suffer severe sunburns during their childhood or teenage years?

Does the patient have a cancer-prone syndrome (eg, familial atypical mole and melanoma syndrome or xeroderma pigmentosum)?

Is the patient immunosuppressed?

The patient's phenotypic features associated with an increased risk of melanoma should also be assessed (see "Melanoma: Epidemiology and risk factors", section on 'Phenotypic traits'). They include:

Light-complexioned phototype

Red or blond hair

Light eye color

Presence of a large number (>50) of melanocytic nevi (common nevi)

Presence of large or atypical melanocytic nevi (benign nevi that clinically share some of the features of melanoma, such as large diameter, irregular borders, and multiple colors) (see "Atypical (dysplastic) nevi")

Although the presence of a large number of common nevi is a strong risk factor for cutaneous melanoma, the majority of melanomas arise de novo. A 2017 meta-analysis of 38 studies including over 20,000 melanomas found that only 29 percent were nevus associated, with the rest arising de novo [18].

Skin examination — The skin examination should be conducted under optimal lighting and include the entire body surface. Clinicians assess the probability that a pigmented lesion is a melanoma using a complex cognitive process that includes a combination of the following three steps [44,45]:

Visual analysis and pattern recognition – Visual analysis and pattern recognition typically assesses whether a given pigmented lesion has one or more features that may suggest melanoma, including asymmetry, irregular borders, variegated color, and diameter >6 mm. These features have been included in the widely adopted ABCDE (asymmetry, border irregularity, color variegation, diameter >6 mm, evolution) checklist, a clinical prediction rule that was devised to help clinicians and laypeople identify suspicious lesions [46]. (See 'ABCDE criteria' below.)

Comparative analysis of nevus patterns in an individual patient – The intrapatient comparative analysis uses the so-called "ugly duckling" sign, which refers to the presence of a single lesion that does not match the patient's nevus phenotype (the so-called "signature nevus") [47]. (See 'The 'ugly duckling' sign' below.)

Dynamic analysis – A history of change in size, color, or shape of a pre-existing melanocytic lesion (the "E" for "evolution" in the ABCDE checklist) or a history of a "new" lesion (which may be pink [amelanotic]) that is growing are the most important clinical criteria for the diagnosis of melanoma. A change may have been noted by the patient or documented by comparison of serial clinical or dermoscopic images. (See 'ABCDE criteria' below and ''Pink lesions'' below.)

The diagnostic accuracy of visual inspection was examined in a systematic review and meta-analysis of 49 studies performed in dermatology settings that reported accuracy data for the diagnosis of melanoma based on over 34,000 lesions, including 2500 melanomas [48]. In a subset of six studies of in-person evaluation by visual inspection of participants presenting for the first time with a suspicious, pigmented lesion, the summary sensitivity and specificity were 92.4 percent (95% CI 26.2-99.8 percent) and 79.7 percent (95% CI 73.7-84.7 percent), respectively. However, the included studies were highly heterogeneous, as shown by the wide confidence interval, in particular for sensitivity. No difference in diagnostic accuracy was noted in studies reporting the use of popular algorithms (eg, ABCDE, seven-point checklist) or in studies including participants with prior testing referred for specialist evaluation.

Clinical prediction rules — The "ugly duckling" sign, the ABCDE rule of melanoma, and the Glasgow revised seven-point checklist can help identify melanoma. The use of these criteria by primary care clinicians may improve their ability to detect lesions suspicious for melanoma at an early stage and inform the decision to refer for dermatology consultation [49-51]. (See 'Indications for referral' below.)

The 'ugly duckling' sign — The "ugly duckling" sign is based upon the observation that, in an individual with multiple nevi, the nevi tend to exhibit one or more predominant, morphologic types (the "signature nevi"), defining a relatively specific "profile." A pigmented lesion that is obviously different from the others in a given individual must be considered suspicious, even if it does not fulfill the ABCD (asymmetry, border irregularity, color variegation, diameter >6 mm) criteria. The "ugly duckling" sign was proposed as an additional criterion for the clinician to identify those lesions that deserve special attention in patients with multiple nevi and is a central component of the so-called intrapatient comparative analysis [47,52].

The predictive value of the "ugly duckling" sign has not been systematically studied. However, the "ugly duckling" sign has been shown to play an important role in the overall pattern recognition process that expert clinicians use to diagnose a pigmented lesion in their daily practice [42]. Although individual clinicians may have a different threshold in their perception of an "ugly duckling," there is a good agreement beyond chance among experts in the recognition of lesions that are different from the signature nevi in a given patient [52,53].

ABCDE criteria — In 1985, dermatologists from New York University first devised the acronym ABCD (asymmetry, border irregularity, color variegation, diameter >6 mm) to educate primary care clinicians and laypeople on the identification of early melanoma [54]. In 2004, the criteria were enhanced with the addition of "E" (evolution) to incorporate the fundamental concept of change, including a modification over time of a pre-existing nevus or the development of a new lesion, especially in individuals older than 40 years [46]:

Asymmetry (if a lesion is bisected, one half is not identical to the other half (picture 3B))

Border irregularities (picture 3B-C)

Color variegation (presence of multiple shades of red, blue, black, gray, or white (picture 3D))

Diameter ≥6 mm

Evolution (a lesion that is changing in size, shape, or color, or a new lesion)

These criteria apply most commonly to the superficial spreading subtype and are less applicable to nodular and desmoplastic melanoma subtypes. Moreover, melanomas in children and adolescents often lack the conventional ABCDE criteria and may be amelanotic. (See "Melanoma in children", section on 'Physical examination'.)

The diagnostic accuracy of the ABCD mnemonic has been assessed in a few studies, all having methodologic limitations [55,56]. The sensitivity and specificity of the ABCDE criteria vary when they are used individually or in combination, and the risks of over- and under-referral must be balanced accordingly.

The use of a single criterion is sensitive but not specific, meaning that many benign lesions would be biopsied or referred, whereas using more than one criterion for referral is more specific but increases the chances of missing malignant lesions.

In a retrospective study of 1140 lesions including 460 melanomas, the sensitivity in identifying a lesion as a melanoma was 97 percent when using a single criterion and 43 percent when using all five criteria jointly. By contrast, specificity was 36 percent for a single criterion and 100 percent for all five criteria [57].

The revised seven-point checklist — Another set of criteria for referral or biopsy, the Glasgow seven-point checklist, was developed in the United Kingdom from a retrospective review of patients with melanoma and subsequently revised. The use of the revised (weighted) version is promoted in primary care settings to guide referral by the United Kingdom National Institute for Clinical Excellence and by the Scottish Intercollegiate Guidelines Network [58-60]. The revised seven-point checklist includes three major features and four minor features [60]:

Major:

Change in size/new lesion

Change in shape/irregular border

Change in color/irregular pigmentation

Minor:

Diameter ≥7 mm

Inflammation

Crusting or bleeding

Sensory change/itch

A score of 2 is assigned to each major feature, while a score of 1 is assigned to each minor feature. The presence of any major feature plus a minor feature or at least three minor features is an indication for referral [59,60]. One study to evaluate the sensitivity and specificity of the revised seven-point checklist found a sensitivity of 100 percent and a specificity of 37 percent in the diagnosis of 65 melanomas and 100 benign, pigmented lesions [61]. The only validation study using prospective data from a randomized trial evaluating the addition of a diagnostic aid to the management of suspicious skin lesions in primary care found a sensitivity of 92 percent and a specificity of 33 percent for melanoma when using at least one major feature and one minor feature [51].

Challenging lesions — Less common subtypes of melanoma, such as nodular melanomas, desmoplastic melanomas, amelanotic and hypomelanotic melanomas (which may be of any subtype), melanomas of the nail unit, and melanomas occurring in children, may be difficult to diagnose clinically and dermoscopically, as they lack the clinical features usually associated with cutaneous melanoma and frequently mimic benign skin lesions [62]. (See 'Uncommon variants' above.)

Several alternative clinical criteria have been proposed to help clinicians maintain a high index of suspicion when evaluating persistent, pink or red lesions.

'Pink lesions' — Nodular melanomas and, in particular, those with little or no pigmentation ("pink lesions" (picture 4A, 4D)) are characterized by delay in diagnosis, emphasizing the importance of recognizing change in a lesion ("E" for "evolving") or the "ugly duckling" sign. Because the ABCDE criteria are likely to miss early nodular melanomas, the EFG rule was proposed to facilitate the clinical diagnosis of melanomas that can appear as innocent lesions [63]:

Elevation

Firm on palpation

Continuous growth for one month or longer

Lesions of the nail unit — The ABCDE rule is not applicable for melanomas of the nail unit, which usually present as a longitudinal, brown or black nail plate band, with or without nail dystrophy (picture 8A-B); however, approximately one-third of these lesions lack a clinically apparent pigmentation [64]. (See "Longitudinal melanonychia".)

An alternative ABCDEF mnemonic has been proposed for subungual melanomas [65]:

Age, African Americans, Asians, and Native Americans

Brown to black band

Change in the nail band

Digit most commonly involved (great toe and thumb)

Extension of the pigment onto the proximal and/or lateral nail fold

Family or personal history of melanoma

A biopsy of the nail matrix may be warranted for a pigmented nail band with any of the following characteristics: patient age >50 years, dark color, solitary, width >3 mm, dyshomogeneous pigmentation, change in shape or pigmentation, or irregular margins (algorithm 1). The presence of periungual pigmentation (Hutchinson sign) is an additional diagnostic clue.

Pediatric melanoma — The clinical presentation of melanoma in children often defies the conventional ABCDE criteria (picture 9). In particular, the "evolution" criterion may not be helpful at an age in which the new onset/evolution of common nevi is normal. Alternative criteria, such as ABCD and CUP, have been proposed for the clinical detection of suspicious lesions in children [66] (see "Melanoma in children", section on 'Diagnosis'):

Amelanotic

Bleeding, bump

Color uniformity

De novo, any diameter

Color pink/red, changing

Ulceration, upward thickening

Pyogenic granuloma-like lesions, pop-up of new lesions

SUPPORT TECHNIQUES FOR CLINICAL DIAGNOSIS — Imaging technologies, including dermoscopy, total body photography, reflectance confocal microscopy (RCM), optical coherence tomography, electrical impedance spectroscopy, and multispectral imaging, may improve the early recognition of melanoma [67,68]. Among them, dermoscopy is the most widely used and studied diagnostic tool, with total body or lesion-directed photography assisting in clinical surveillance.

Dermoscopy — Dermoscopic examination should be performed on all suspicious, pigmented lesions as a first-line diagnostic support modality. This technique is widely used in dermatologic settings (but generally not in primary care settings) for the clinical diagnosis of pigmented and nonpigmented skin lesions and requires training to provide an advantage over the naked-eye clinical examination [43].

Characteristic dermatoscopic features of melanoma, also called melanoma-specific criteria, include an atypical pigment network; irregular, brown-black dots/globules; streaks; multiple colors asymmetrically distributed; blue-whitish veil; and atypical (polymorphic), vascular pattern (picture 10 and picture 11 and picture 12). (See "Dermoscopic evaluation of skin lesions".)

In experienced practitioners, dermoscopy improves both the sensitivity and specificity of the clinical diagnosis of melanoma. Most importantly, dermoscopy improves the confidence in the diagnosis of benign, pigmented lesions, reducing the number of unnecessary biopsies.

A meta-analysis of nine studies of dermoscopy compared with naked-eye examination in the diagnosis of melanoma concluded that for clinicians with at least some training in dermoscopy, the addition of dermoscopy to the unaided clinical examination increases the sensitivity in detecting melanoma (90 versus 71 percent) but has similar specificity (80 to 90 percent) [43]. Similar findings were found in a subsequent meta-analysis of 26 studies. At a fixed specificity of 80 percent, sensitivity for dermoscopy plus visual inspection was 92 versus 76 percent for visual inspection alone [69].

Online resources on dermoscopy can be found at Dermoscopedia. The principles of dermoscopy and the dermoscopic evaluation of skin and special-site lesions are discussed in detail separately.

(See "Overview of dermoscopy".)

(See "Dermoscopic evaluation of skin lesions".)

(See "Dermoscopic algorithms for skin cancer triage".)

(See "Dermoscopy of facial lesions".)

(See "Dermoscopy of pigmented lesions of the palms and soles".)

(See "Dermoscopy of mucosal lesions".)

(See "Dermoscopy of nail pigmentations".)

Reflectance confocal microscopy — In clinical practice, reflectance confocal microscopy (RCM) may be an addition to clinical and dermoscopic examination for lesions with equivocal clinical and/or dermoscopic features. RCM is an imaging technology that allows for the in vivo identification of cells and tissues of the epidermis and papillary dermis with nearly histologic resolution [70].

RCM uses a low-power laser that emits near-infrared light (830 nm) that reflects off of structures in the epidermis and creates a three-dimensional image with resolution of approximately 1 millimicron, comparable with standard histology at approximately 30x magnification. Melanin granules have a high refractive index, resulting in more light to be reflected back to the confocal microscope [68]. Thus, areas of higher melanin concentration will appear as bright areas on a confocal image.

RCM may be especially useful in the recognition of amelanotic or hypomelanotic melanomas [71-73] and in mapping lentigo maligna margins before surgical excision [74]. RCM allows for a relatively rapid imaging of multiple lesions and can be used for digital monitoring of equivocal lesions over time.

A 2018 meta-analysis of 18 studies found that RCM is more accurate than dermoscopy in detecting melanoma in individuals with lesions difficult to diagnose with visual inspection alone [75]. In a set of 1452 histologically confirmed lesions that included 370 melanomas, specificities were 82 and 42 percent for RCM and dermoscopy, respectively, at a fixed sensitivity of 90 percent for both tests.

Disadvantages of RCM include the high costs of the instrumentation, limited availability, training requirement to interpret images, and a longer time to examine a single lesion (approximately seven minutes) compared with clinical and dermoscopic examination [76]. Moreover, RCM is highly operator dependent and requires formal training.

Computer-assisted diagnosis — Several studies have evaluated the performance of computer-assisted diagnosis of melanoma based upon algorithms built using large sets of images or nonvisual data (eg, electrical impedance measurements) from benign and malignant, pigmented lesions [77-82]. These systems have the potential to augment the clinician's accuracy in the diagnosis of melanoma while reducing the number of unnecessary biopsies [82]. A meta-analysis of 70 studies using automated systems for the diagnosis of pigmented skin lesions found an overall sensitivity of 74 percent (95% CI 66-80 percent) and specificity of 84 percent (95% CI 79-88 percent) in the diagnosis of melanoma, similar to that of dermatologists' diagnosis [83].

Online information and resources on computer-assisted diagnosis of melanoma can be found at The International Skin Imaging Collaboration or Dermoscopedia.

Digital dermoscopy-based systems — A number of systems commercially available in Europe, the United States, and other countries use hand-held or video dermatoscopes that communicate with computer software analyzing digital images of skin lesions.

A meta-analysis of 22 studies using digital dermoscopy-based systems to analyze nearly 9000 lesions including 1063 melanomas found a summary sensitivity and specificity of 90.1 percent (95% CI 84-94) and 74.3 percent (95% CI 63.6-82.7), respectively [84]. However, there was a considerable variability across studies in the type of technology used and algorithm performance, with sensitivities ranging from 17 to 100 percent and specificities ranging from 20 to 98 percent. Moreover, the use of artificial study settings (eg, use of images of lesions previously selected for excision and pathologic assessment, variable melanoma prevalence in datasets) may have resulted in an overestimate or underestimate of the diagnostic accuracy in certain studies.

Multispectral imaging-based systems — Multispectral imaging devices that have been approved as diagnostic aids in the evaluation of skin lesions include SIAscope, FotoFinder, and Verisante Aura [67]:

SIAscope is a multispectral device that emits radiation ranging from 400 to 1000 nm and generates eight narrowband spectrally filtered images that demonstrate the vascular composition, pigment network, and collagen content of a lesion. However, SIAscope does not seem to have a higher sensitivity or specificity in detecting suspicious lesions than dermoscopy, and its use in clinical practice cannot be recommended [85].

The MoleMate tool is a computerized device using the SIAscope technology combined with a scoring algorithm thought to improve the detection and referral of suspicious lesions in primary care settings. In a randomized trial involving 15 general practices, the proportion of lesions appropriately managed was similar among the primary care clinicians using clinical criteria alone (history, naked-eye examination, and seven-point checklist) and those also using the MoleMate tool [86].

A Canadian, prospective study compared the diagnostic performance of dermatologists' bedside examination, teledermoscopy, and four image analyzer systems (MelaFind [no longer available], FotoFinder, FotoFinder Moleanalyzer Pro, and Verisante Aura) in 180 patients with 209 lesions that were subsequently excised and histopathologically diagnosed [87]. Using the histopathologic diagnosis as reference, the sensitivities and specificities of the automated systems were 82.5 and 52.4 percent, respectively, for MelaFind; 83.1 and 75.2 percent, respectively, for FotoFinder; 88.1 and 78.8 percent, respectively, for FotoFinder Moleanalyzer Pro; and 21.4 and 86.2 percent, respectively, for Verisante Aura. The sensitivity and specificity of the teledermoscopist were 84.5 and 82.6 percent, respectively, while the local dermatologists had a sensitivity and specificity of 96.6 and 32.2 percent, respectively.

Artificial intelligence

Convolutional neural networks-based systems — In several studies, artificial intelligence systems using deep learning technologies for the classification of skin cancer images have been shown to consistently perform better than expert clinicians in the diagnosis of melanoma [79-82,88-90]. However, the lack of the full spectrum of skin phenotypes and lesions in the training datasets (eg, less common, banal lesions) is a limitation of these techniques. Moreover, few real-world validation studies are available to confirm the utility of artificial intelligence/machine learning in practice:

A study using a real-world, prospectively acquired, dermoscopic dataset of 281 malignant lesions and 1700 benign lesions from 435 patients compared the performance of a market-approved convolutional neural network (CNN; Moleanalyzer Pro, developed in 2018) with a conventional image analyzer (Moleanalyzer-3/Dynamole, developed in 2004) [91]. Compared with a conventional image analyzer, CNN showed higher sensitivity (78 versus 53 percent), specificity (95 versus 87 percent), and receiver operating characteristic (ROC) area under the curve (0.95 versus 0.74).

In one study, the performance of a novel system using deep CNNs trained on a very large set of clinical images was tested against 21 expert dermatologists for the diagnosis of melanoma versus benign nevus [88]. Using a set of nearly 130,000 clinical and dermoscopic images of pigmented lesions, the deep CNNs outperformed the average of the dermatologists in the classification of melanoma versus benign nevus.

Similar results were obtained in another study using 100 dermoscopic images of melanoma and atypical nevi to compare the performance of a CNN trained with a set of over 12,000 dermoscopic images with that of 157 dermatologists with various levels of experience [89]. The CNN outperformed nearly 90 percent of dermatologists. Although promising, additional prospective studies in real-life settings are necessary to validate these results before artificial intelligence-based systems can be incorporated into clinical practice.

In another study, the performance of a device (Moleanalyzer Pro) approved for the European market in the classification of 100 pigmented skin cancers and benign lesions was similar to that of 96 dermatologists of various levels of experience who used dermoscopy, clinical close-up images, and textual case information [90]. CNN showed a sensitivity and specificity of 95 percent (95% CI 83.5-98.6) and 76.7 percent (95% CI 64.6-85.6), respectively. The mean sensitivity and specificity of dermatologists were 94.1 percent (95% CI 93.1-95.1) and 80.4 percent (95% CI 78.4-82.4), respectively.

Smartphone apps — Hundreds of skin cancer smartphone applications (apps) have been developed since 2014 with a wide range of uses [92]. Some apps operate by forwarding images from the smartphone camera to an experienced professional for review, while others are designed to provide support in skin self-monitoring for individuals at high risk for melanoma. However, there is increasing interest in apps that use artificial intelligence algorithms to classify lesions into risk categories for skin cancer, particularly melanoma. Apps of this type are considered medical devices and require approval by regulatory agencies:

A systematic review evaluated two studies examining the performance of five smartphone apps that analyzed a total of 332 lesion images (86 melanomas) taken by clinicians in clinical settings [93]. The apps' algorithms classified lesion images as melanomas or high-risk lesions with sensitivities ranging from 7 to 73 percent and specificities ranging from 37 to 94 percent. This means that between 27 and 93 percent of invasive melanomas or atypical melanocytic lesions were not identified by the apps, as requiring further assessment by a clinician. The authors concluded that smartphone apps using artificial intelligence-based analysis have not yet demonstrated sufficient promise in terms of accuracy, and they are associated with a high likelihood of missing melanomas.

A subsequent, systematic review examined nine studies assessing the diagnostic accuracy of six smartphone apps for risk stratification of suspicious skin lesions [94]. As a reference, the apps used the histopathologic diagnosis (six studies, 725 lesions) or face-to-face diagnosis of an expert clinician (three studies, 407 lesions) and reported lesion classification as high, moderate, or low risk with the recommendation to "consult a doctor" for high/moderate-risk lesions and "no action" for low-risk ones (four apps). Sensitivities varied from 0 to 88 percent, and specificities varied from 56 to 100 percent. Most studies were considered at high risk of bias.

Although limited to a small number of apps, the results of these studies show that the performance of these apps is generally variable and unreliable. Moreover, as the studies were based on highly selected image sets taken in experimental conditions, the accuracy of these apps in recognizing suspicious skin lesions is likely to be poorer in real-life settings.

Adhesive patch genomic analysis — A noninvasive test that uses genetic information from cells collected from the surface of melanocytic lesions with an adhesive patch was developed to help in the decision to biopsy an atypical, melanocytic lesion [95-97]. The so-called "pigmented lesion assay" (PLA) measures the expression of the genes LINC and PRAME. Overexpression of these two genes appears to be associated with the presence of somatic mutations in BRAF non-V600E, NRAS, and TERT, which are involved in melanoma development and progression [96]. The analysis of a large, United States registry study demonstrated >99 percent negative predictive value in PLA-negative, melanocytic lesions in up to 12 months of follow-up, suggesting clinical utility to reduce unnecessary biopsies for melanoma diagnosis [98]. (See "The molecular biology of melanoma".)

DIAGNOSIS CONFIRMATION

Biopsy — The definitive diagnosis of melanoma is histopathologic. A skin biopsy is the first step to establish the diagnosis of melanoma. Prebiopsy photographs are helpful for clinical-pathologic correlation and for preventing wrong site surgery [99]. Biopsies are performed to remove the entire lesion whenever possible or, in specific situations, part of the lesion:

Excisional/complete biopsy – An excisional/complete biopsy of suspicious lesions with 1 to 3 mm margin of normal skin and extending to a depth to encompass the thickest portion of the lesion is the preferred technique and should be performed whenever possible. Narrow margin excision allows for the assessment of the entire lesion without compromising subsequent, wider surgery or potential staging with the sentinel lymph node biopsy technique [99,100]. Excisional biopsy techniques may include a full-thickness elliptical or punch excision and saucerization/deep shave removal, also referred to as a "scoop" biopsy [99,101]. In contrast with saucerization, which is performed using a shave biopsy blade and extends into the deep reticular dermis, a "disk excision" is a full-thickness, circular incision performed with a scalpel and extending vertically into the subcutaneous fat.

Incisional biopsy – Partial incisional biopsy may be acceptable if the excision of the entire lesion is not feasible (eg, large lesions, lesions on face, palm or sole, ear, distal digit, or subungual lesions). For large lesions, multiple biopsies may be needed to minimize sampling error, though this cannot be prevented with an incomplete/partial biopsy. However, there is no evidence that partial incisional biopsies adversely affect patient outcome (eg, disease recurrence, risk of metastasis) [102].

Nail matrix biopsy for suspicious nail lesions (eg, longitudinal melanonychia, nail bed lesions) is discussed in detail elsewhere. (See "Nail biopsy: Indications and techniques".)

In the United States, both the National Comprehensive Cancer Network and the American Academy of Dermatology guidelines recommend that superficial shave biopsy should be limited to lesions for which the suspicion of melanoma is low and performed to a depth below the anticipated plane of the lesion. However, a broad shave biopsy is often helpful for diagnosis of melanoma in situ, lentigo maligna type [101,103]. By contrast, the revised United Kingdom guidelines recommend that shave biopsies should be avoided in all cases, as they may lead to incorrect diagnosis due to sampling error [100].

On the pathology requisition, detailed clinical information on excised lesions, including anatomic location, type of biopsy performed and intent (excisional or incisional), size of the lesion, and marking of suspicious foci, should be provided to the pathologist. Additional information including the clinician's clinical impression, lesion history, dermoscopic features, and clinical or dermoscopic images, if available, would also be useful, especially for incisional or punch biopsies [104,105].

Histopathology — Since no single pathologic feature of melanoma is diagnostic, the histopathologic diagnosis is based upon a combination of architectural, cytologic, and host response features. The presence of atypical melanocytes (ie, melanocytes that are larger than normal and have large, hyperchromatic nuclei; irregular nuclear shape and nuclear polymorphism; abnormal chromatin pattern; and prominent nucleoli) and architectural disorder (ie, asymmetry, poor circumscription, nests of melanocytes of various sizes and shapes in the lower epidermis and dermis) are required for the diagnosis (picture 13). The histopathologic features of the major subtypes of melanoma are reviewed in detail separately. (See "Pathologic characteristics of melanoma".)

Although the histopathologic diagnosis of melanoma is often straightforward, in some cases, it can be difficult even for the experienced pathologist. In addition, the interpretation of a melanocytic lesion is largely subjective and may vary among pathologists and even experienced dermatopathologists [106,107].

In a study of the accuracy and reproducibility of the histopathologic diagnosis of melanocytic skin lesions in which 187 experienced pathologists independently evaluated 240 melanocytic lesions, the diagnostic accuracy using a consensus diagnosis of experienced pathologists as reference was relatively high for benign nevi and mildly dysplastic nevi and T1b invasive melanoma (92 and 72 percent, respectively) but poor for lesions with moderate atypia, lesions with severe atypia or melanoma in situ, and T1a invasive melanoma (25, 40, and 43 percent, respectively) [107]. Second opinions from pathologists with specific training in dermatopathology may reduce the rate of misclassification of difficult or equivocal melanocytic lesions [108].

Immunohistochemistry — Immunohistochemistry can be helpful in confirming the melanocytic origin of difficult lesions (eg, amelanotic lesions). The most widely used markers are S-100, Sox10, MART-1, HMB-45, and tyrosinase (table 3). (See "Pathologic characteristics of melanoma", section on 'Immunohistochemistry'.)

Molecular techniques — Molecular techniques may aid in melanoma diagnosis [109]. These include comparative genomic hybridization, fluorescence in situ hybridization (FISH), and gene expression profiling of tumors [110]. FISH allows for the evaluation of specific, chromosomal abnormalities associated with melanoma and is emerging as a tool to diagnose equivocal melanocytic lesions [111-113].

DIFFERENTIAL DIAGNOSIS — Multiple melanocytic and nonmelanocytic lesions may simulate melanoma clinically and sometimes histologically:

Melanocytic lesions:

Common melanocytic nevus (picture 14) (see "Acquired melanocytic nevi (moles)")

Atypical melanocytic nevus (picture 15A-B) (see "Atypical (dysplastic) nevi")

Traumatized nevus

Blue nevus (picture 16) (see "Acquired melanocytic nevi (moles)", section on 'Blue nevi')

Lentigo (ink spot) (picture 17)

Spitz nevus (picture 18) (see "Spitz nevus and atypical Spitz tumors")

Nonmelanocytic lesions:

Pigmented basal cell carcinoma (picture 19) (see "Basal cell carcinoma: Epidemiology, pathogenesis, clinical features, and diagnosis")

Pigmented actinic keratosis (picture 20) (see "Actinic keratosis: Epidemiology, clinical features, and diagnosis")

Seborrheic keratosis (picture 21A-F) (see "Overview of benign lesions of the skin", section on 'Seborrheic keratosis')

Pyogenic granuloma (picture 22A-B) (see "Pyogenic granuloma (lobular capillary hemangioma)")

Cherry hemangioma (picture 23A-B) (see "Overview of benign lesions of the skin", section on 'Cherry angioma')

Dermatofibroma (picture 24A-C) (see "Overview of benign lesions of the skin", section on 'Dermatofibroma')

Keratoacanthoma (picture 25A-B) (see "Keratoacanthoma: Epidemiology, risk factors, and diagnosis")

Acral lesions – For acral melanoma and melanoma of the nail unit, the differential diagnosis includes (see "Dermoscopy of pigmented lesions of the palms and soles"):

Verrucous squamous cell carcinoma of the sole (picture 26) (see "Cutaneous squamous cell carcinoma (cSCC): Clinical features and diagnosis", section on 'Verrucous carcinoma')

Melanonychia striata (picture 27A-C) (see "Longitudinal melanonychia")

Acral melanocytic nevi (picture 28) (see "Dermoscopy of pigmented lesions of the palms and soles", section on 'Acquired melanocytic nevi')

Subungual hematoma (picture 29) (see "Subungual hematoma")

Pyogenic granuloma (picture 30) (see "Pyogenic granuloma (lobular capillary hemangioma)")

Periungual warts (picture 31) (see "Cutaneous warts (common, plantar, and flat warts)")

MANAGEMENT OF SUSPICIOUS LESIONS

Indications for referral — Primary care clinicians who identify a suspicious skin lesion should have a relatively low threshold for referral to a dermatologist for dermoscopic examination and evaluation of whether biopsy is indicated. (See 'Clinical prediction rules' above.)

It is generally accepted that patients with a pigmented lesion that is changing and has additional ABCDE (asymmetry, border irregularity, color variegation, diameter >6 mm, evolution) criteria or features of the revised seven-point checklist should be strongly considered for referral to an expert in skin cancer. (See 'ABCDE criteria' above and 'The revised seven-point checklist' above.)

Moreover, patients with lesions that do not fulfill the prediction rule criteria but are not obviously benign should be examined by a dermatologist. Finally, referral is warranted for lesions that are a cause of concern and anxiety to the patient.

Guidelines published in 2010 by the British Association of Dermatologists suggest the following indications for referral [100]:

A new mole appearing after the onset of puberty that is changing in shape, color, or size

A long-standing mole that is changing in shape, color, or size

Any mole that has three or more colors or has lost its symmetry

A mole that is itching or bleeding

Any new, persistent skin lesion, especially if growing, pigmented, or vascular in appearance, and if the diagnosis is not clear

A new, pigmented line in a nail, especially where there is associated damage to the nail

A lesion growing under a nail

Follow-up of suspicious lesions that are not excised — Baseline clinical documentation of lesion size and appearance and, if possible, baseline clinical and dermoscopic images of suspicious lesions that are not excised should be taken at the time of first examination and stored for comparison. These lesions should be examined three months after the initial examination (ie, short interval follow-up) and compared with the baseline images to detect possible changes and early signs of melanoma. In clinical practice settings where dermoscopic and/or dermatologic expertise is not available, short-interval monitoring of a changing or otherwise concerning skin lesion and a low threshold for dermatologist referral are advised.

Sequential digital dermatoscopic documentation is especially useful for patients with multiple atypical nevi, as it enables the detection of melanoma and reduces the number of unnecessary excisions [45].

SOCIETY GUIDELINE LINKS — Links to society and government-sponsored guidelines from selected countries and regions around the world are provided separately. (See "Society guideline links: Melanoma screening, prevention, diagnosis, and management".)

SUMMARY AND RECOMMENDATIONS

Melanoma classification – The 2018 World Health Organization (WHO) classification of cutaneous melanoma takes into account the site of origin (epithelium associated versus nonepithelium associated), role of cumulative sun damage (CSD; high CSD related, low CSD related, or non-CSD related), mole phenotype (high versus low nevus count), and frequency of BRAF, NRAS, and other relevant mutations (table 1). However, the traditional clinicopathologic classification may by useful for recognition and diagnosis of melanoma by clinicians. (See 'Overview' above.)

Melanoma subtypes – Based on morphologic features, there are four main types of cutaneous melanoma: superficial spreading melanoma (picture 1), lentigo maligna melanoma (picture 5D), acral lentiginous melanoma (picture 6C), and nodular melanoma (picture 2). Less common variants include amelanotic melanoma, spitzoid melanoma, and desmoplastic melanoma. (See 'Melanoma subtypes and associated features' above and 'Uncommon variants' above.)

When to suspect melanoma – Early signs of melanoma include asymmetry, irregular borders, variegated color, and diameter ≥6 mm in a pigmented lesion; a recent change in a pre-existing pigmented lesion; and the development of a new and evolving lesion (which may be amelanotic), particularly in adults. A skin lesion that looks different from other surrounding lesions ("ugly duckling" sign) is an important finding in patients with multiple nevi. (See 'Clinical prediction rules' above.)

Challenging lesions – Alternative clinical criteria have been proposed for challenging lesions that lack clinical features usually associated with melanoma, such as nodular melanoma, desmoplastic melanoma, amelanotic and hypomelanotic melanomas ("pink lesions" (picture 4A, 4D)), melanoma of the nail unit (picture 8A-B), and melanoma occurring in children (picture 9). (See 'Challenging lesions' above.)

Techniques that aid in clinical diagnosis:

Dermoscopy – Dermoscopic examination should be performed on all suspicious, pigmented lesions as a first-line diagnostic support modality. Dermoscopy can substantially improve the recognition of suspicious lesions by clinicians who have received adequate training.

Other imaging techniques – Additional imaging technologies that have been introduced to improve the early recognition of melanoma include reflectance confocal microscopy, multispectral imaging, and artificial intelligence systems. (See 'Support techniques for clinical diagnosis' above and 'Dermoscopy' above.)

Diagnosis confirmation – The definitive diagnosis of melanoma is histopathologic. Molecular techniques may be helpful in difficult cases (see 'Histopathology' above and "Pathologic characteristics of melanoma"):

Biopsy – A complete full-thickness excisional biopsy of suspicious lesions with 1 to 3 mm margin of normal skin and extending to a depth to encompass the thickest portion of the lesion should be performed whenever possible. Partial incisional biopsy may be acceptable for very large lesions or for certain sites, including the face, palm or sole, ear, distal digit, or subungual lesions. (See 'Biopsy' above.)

Histopathology – The histopathologic diagnosis of melanoma is based upon a combination of architectural, cytologic, and host response features. Immunohistochemical stainings may be helpful in difficult cases (table 3). (See 'Histopathology' above and 'Immunohistochemistry' above.)

Molecular techniques – Molecular techniques, including comparative genomic hybridization, fluorescence in situ hybridization (FISH), and gene expression profiling of tumors, may further aid in the diagnosis of equivocal melanocytic lesions. (See 'Molecular techniques' above and "Pathologic characteristics of melanoma" and "Pathologic characteristics of melanoma", section on 'Genetic and molecular characterization of melanoma'.)

Management of suspicious lesions – Primary care clinicians who identify a skin lesion that is not clearly benign should have a relatively low threshold for referral to a dermatologist for dermoscopic examination and evaluation of whether biopsy is indicated. A change in a long-standing mole; the development of a new, persistent skin lesion after puberty; a new, pigmented band in a nail; and any lesion growing under the nail are the most important criteria for referral. (See 'Management of suspicious lesions' above.)

  1. Long GV, Swetter SM, Menzies AM, et al. Cutaneous melanoma. Lancet 2023; 402:485.
  2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019; 69:7.
  3. Brady MS, Oliveria SA, Christos PJ, et al. Patterns of detection in patients with cutaneous melanoma. Cancer 2000; 89:342.
  4. Carli P, De Giorgi V, Palli D, et al. Self-detected cutaneous melanomas in Italian patients. Clin Exp Dermatol 2004; 29:593.
  5. Carli P, De Giorgi V, Palli D, et al. Dermatologist detection and skin self-examination are associated with thinner melanomas: results from a survey of the Italian Multidisciplinary Group on Melanoma. Arch Dermatol 2003; 139:607.
  6. Epstein DS, Lange JR, Gruber SB, et al. Is physician detection associated with thinner melanomas? JAMA 1999; 281:640.
  7. Schwartz JL, Wang TS, Hamilton TA, et al. Thin primary cutaneous melanomas: associated detection patterns, lesion characteristics, and patient characteristics. Cancer 2002; 95:1562.
  8. Swetter SM, Johnson TM, Miller DR, et al. Melanoma in middle-aged and older men: a multi-institutional survey study of factors related to tumor thickness. Arch Dermatol 2009; 145:397.
  9. Green AC, Baade P, Coory M, et al. Population-based 20-year survival among people diagnosed with thin melanomas in Queensland, Australia. J Clin Oncol 2012; 30:1462.
  10. Elder DE, Bastian BC, Cree IA, et al. The 2018 World Health Organization Classification of Cutaneous, Mucosal, and Uveal Melanoma: Detailed Analysis of 9 Distinct Subtypes Defined by Their Evolutionary Pathway. Arch Pathol Lab Med 2020; 144:500.
  11. Bastian BC. The molecular pathology of melanoma: an integrated taxonomy of melanocytic neoplasia. Annu Rev Pathol 2014; 9:239.
  12. Scolyer RA, Balamurgan T, Busam K, et al. Invasive Melanoma, Histopathology Reporting Guide, 2nd edition. International Collaboration on Cancer Reporting, 2019; Sydney, Australia. http://www.iccr-cancer.org/datasets/published-datasets/skin/melanoma (Accessed on February 11, 2021).
  13. Shon W, Frishberg D, Gershenwald J, et al. Protocol for the examination of biopsy specimens from patients with melanoma of the skin, version 4.2.0.0. College of American Pathologists (CAP) 2019. https://documents.cap.org/protocols/cp-skin-melanoma-biopsy-20-4200.pdf (Accessed on February 11, 2021).
  14. Shon W, Frishberg D, Gershenwald J, et al. Protocol for the examination of excision specimens from patients with melanoma of the skin, version 4.2.0.0. College of American Pathologists (CAP) 2020. https://documents.cap.org/protocols/cp-skin-melanoma-excision-20-4200.pdf (Accessed on February 11, 2021).
  15. Breslow A. Thickness, cross-sectional areas and depth of invasion in the prognosis of cutaneous melanoma. Ann Surg 1970; 172:902.
  16. Hassel JC, Enk AH. Melanoma. In: Fitzpatrick's Dermatology, 9th Edition, Kang S, Amagai M, Bruckner AL, et al (Eds), McGraw-Hill Education, 2019. Vol 1, p.1982.
  17. Lasithiotakis KG, Leiter U, Gorkievicz R, et al. The incidence and mortality of cutaneous melanoma in Southern Germany: trends by anatomic site and pathologic characteristics, 1976 to 2003. Cancer 2006; 107:1331.
  18. Pampena R, Kyrgidis A, Lallas A, et al. A meta-analysis of nevus-associated melanoma: Prevalence and practical implications. J Am Acad Dermatol 2017; 77:938.
  19. American Academy of Dermatology Ad Hoc Task Force for the ABCDEs of Melanoma, Tsao H, Olazagasti JM, et al. Early detection of melanoma: reviewing the ABCDEs. J Am Acad Dermatol 2015; 72:717.
  20. Geller AC, Elwood M, Swetter SM, et al. Factors related to the presentation of thin and thick nodular melanoma from a population-based cancer registry in Queensland Australia. Cancer 2009; 115:1318.
  21. Warycha MA, Christos PJ, Mazumdar M, et al. Changes in the presentation of nodular and superficial spreading melanomas over 35 years. Cancer 2008; 113:3341.
  22. Demierre MF, Chung C, Miller DR, Geller AC. Early detection of thick melanomas in the United States: beware of the nodular subtype. Arch Dermatol 2005; 141:745.
  23. Clark WH Jr, Mihm MC Jr. Lentigo maligna and lentigo-maligna melanoma. Am J Pathol 1969; 55:39.
  24. Swetter SM, Boldrick JC, Jung SY, et al. Increasing incidence of lentigo maligna melanoma subtypes: northern California and national trends 1990-2000. J Invest Dermatol 2005; 125:685.
  25. Brunsgaard EK, Wu YP, Grossman D. Melanoma in skin of color: Part I. Epidemiology and clinical presentation. J Am Acad Dermatol 2023; 89:445.
  26. Coleman WP 3rd, Loria PR, Reed RJ, Krementz ET. Acral lentiginous melanoma. Arch Dermatol 1980; 116:773.
  27. Scarfì F, Galeone M, Bassi A, et al. Melanoma manifesting as a verrucous lesion in the interdigital toe space. Int J Dermatol 2014; 53:1125.
  28. Gencoglan G, Inanir I, Miskioglu M, Temiz P. Acral amelanotic verrucous melanoma: dermoscopic findings. Dermatol Surg 2011; 37:107.
  29. Choi YD, Chun SM, Jin SA, et al. Amelanotic acral melanomas: clinicopathological, BRAF mutation, and KIT aberration analyses. J Am Acad Dermatol 2013; 69:700.
  30. Buka R, Friedman KA, Phelps RG, et al. Childhood longitudinal melanonychia: case reports and review of the literature. Mt Sinai J Med 2001; 68:331.
  31. Ruben BS. Pigmented lesions of the nail unit: clinical and histopathologic features. Semin Cutan Med Surg 2010; 29:148.
  32. Strazzulla LC, Li X, Zhu K, et al. Clinicopathologic, misdiagnosis, and survival differences between clinically amelanotic melanomas and pigmented melanomas. J Am Acad Dermatol 2019; 80:1292.
  33. Jaimes N, Braun RP, Thomas L, Marghoob AA. Clinical and dermoscopic characteristics of amelanotic melanomas that are not of the nodular subtype. J Eur Acad Dermatol Venereol 2012; 26:591.
  34. Pizzichetta MA, Talamini R, Stanganelli I, et al. Amelanotic/hypomelanotic melanoma: clinical and dermoscopic features. Br J Dermatol 2004; 150:1117.
  35. Gualandri L, Betti R, Crosti C. Clinical features of 36 cases of amelanotic melanomas and considerations about the relationship between histologic subtypes and diagnostic delay. J Eur Acad Dermatol Venereol 2009; 23:283.
  36. Wee E, Wolfe R, Mclean C, et al. Clinically amelanotic or hypomelanotic melanoma: Anatomic distribution, risk factors, and survival. J Am Acad Dermatol 2018; 79:645.
  37. Raghavan SS, Peternel S, Mully TW, et al. Spitz melanoma is a distinct subset of spitzoid melanoma. Mod Pathol 2020; 33:1122.
  38. Chen LL, Jaimes N, Barker CA, et al. Desmoplastic melanoma: a review. J Am Acad Dermatol 2013; 68:825.
  39. Kottschade LA, Grotz TE, Dronca RS, et al. Rare presentations of primary melanoma and special populations: a systematic review. Am J Clin Oncol 2014; 37:635.
  40. Vyas R, Keller JJ, Honda K, et al. A systematic review and meta-analysis of animal-type melanoma. J Am Acad Dermatol 2015; 73:1031.
  41. Bax MJ, Brown MD, Rothberg PG, et al. Pigmented epithelioid melanocytoma (animal-type melanoma): An institutional experience. J Am Acad Dermatol 2017; 77:328.
  42. Gachon J, Beaulieu P, Sei JF, et al. First prospective study of the recognition process of melanoma in dermatological practice. Arch Dermatol 2005; 141:434.
  43. Vestergaard ME, Macaskill P, Holt PE, Menzies SW. Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting. Br J Dermatol 2008; 159:669.
  44. Grob JJ, Gaudy-Marqueste C, Cha KB, et al. Melanoma. In: Rook's Textbook of Dermatology, Ninth Edition, Griffiths C, Barker J, Bleiker T, et al (Eds), Wiley, 2016.
  45. Garbe C, Amaral T, Peris K, et al. European consensus-based interdisciplinary guideline for melanoma. Part 1: Diagnostics - Update 2019. Eur J Cancer 2020; 126:141.
  46. Abbasi NR, Shaw HM, Rigel DS, et al. Early diagnosis of cutaneous melanoma: revisiting the ABCD criteria. JAMA 2004; 292:2771.
  47. Grob JJ, Bonerandi JJ. The 'ugly duckling' sign: identification of the common characteristics of nevi in an individual as a basis for melanoma screening. Arch Dermatol 1998; 134:103.
  48. Dinnes J, Deeks JJ, Grainge MJ, et al. Visual inspection for diagnosing cutaneous melanoma in adults. Cochrane Database Syst Rev 2018; 12:CD013194.
  49. Carli P, De Giorgi V, Crocetti E, et al. Diagnostic and referral accuracy of family doctors in melanoma screening: effect of a short formal training. Eur J Cancer Prev 2005; 14:51.
  50. Peuvrel L, Quereux G, Jumbou O, et al. Impact of a campaign to train general practitioners in screening for melanoma. Eur J Cancer Prev 2009; 18:225.
  51. Walter FM, Prevost AT, Vasconcelos J, et al. Using the 7-point checklist as a diagnostic aid for pigmented skin lesions in general practice: a diagnostic validation study. Br J Gen Pract 2013; 63:e345.
  52. Gaudy-Marqueste C, Wazaefi Y, Bruneu Y, et al. Ugly Duckling Sign as a Major Factor of Efficiency in Melanoma Detection. JAMA Dermatol 2017; 153:279.
  53. Scope A, Dusza SW, Halpern AC, et al. The "ugly duckling" sign: agreement between observers. Arch Dermatol 2008; 144:58.
  54. Friedman RJ, Rigel DS, Kopf AW. Early detection of malignant melanoma: the role of physician examination and self-examination of the skin. CA Cancer J Clin 1985; 35:130.
  55. Whited JD, Grichnik JM. The rational clinical examination. Does this patient have a mole or a melanoma? JAMA 1998; 279:696.
  56. Harrington E, Clyne B, Wesseling N, et al. Diagnosing malignant melanoma in ambulatory care: a systematic review of clinical prediction rules. BMJ Open 2017; 7:e014096.
  57. Thomas L, Tranchand P, Berard F, et al. Semiological value of ABCDE criteria in the diagnosis of cutaneous pigmented tumors. Dermatology 1998; 197:11.
  58. National Collaborating Centre for Cancer (UK). Melanoma: Assessment and Management. London: National Institute for Health and Care Excellence (UK); 2015. www.ncbi.nlm.nih.gov/pubmedhealth/PMH0079031/pdf/PubMedHealth_PMH0079031.pdf (Accessed on March 28, 2017).
  59. Scottish Intercollegiate Guidelines Network. Cutaneous Melanoma. A national clinical guideline. January 2017. www.sign.ac.uk/pdf/SIGN146.pdf (Accessed on March 28, 2017).
  60. MacKie RM. Clinical recognition of early invasive malignant melanoma. BMJ 1990; 301:1005.
  61. Healsmith MF, Bourke JF, Osborne JE, Graham-Brown RA. An evaluation of the revised seven-point checklist for the early diagnosis of cutaneous malignant melanoma. Br J Dermatol 1994; 130:48.
  62. Pampena R, Lai M, Lombardi M, et al. Clinical and Dermoscopic Features Associated With Difficult-to-Recognize Variants of Cutaneous Melanoma: A Systematic Review. JAMA Dermatol 2020; 156:430.
  63. Chamberlain AJ, Fritschi L, Kelly JW. Nodular melanoma: patients' perceptions of presenting features and implications for earlier detection. J Am Acad Dermatol 2003; 48:694.
  64. Tan KB, Moncrieff M, Thompson JF, et al. Subungual melanoma: a study of 124 cases highlighting features of early lesions, potential pitfalls in diagnosis, and guidelines for histologic reporting. Am J Surg Pathol 2007; 31:1902.
  65. Levit EK, Kagen MH, Scher RK, et al. The ABC rule for clinical detection of subungual melanoma. J Am Acad Dermatol 2000; 42:269.
  66. Cordoro KM, Gupta D, Frieden IJ, et al. Pediatric melanoma: results of a large cohort study and proposal for modified ABCD detection criteria for children. J Am Acad Dermatol 2013; 68:913.
  67. March J, Hand M, Grossman D. Practical application of new technologies for melanoma diagnosis: Part I. Noninvasive approaches. J Am Acad Dermatol 2015; 72:929.
  68. Ferris LK, Harris RJ. New diagnostic aids for melanoma. Dermatol Clin 2012; 30:535.
  69. Dinnes J, Deeks JJ, Chuchu N, et al. Dermoscopy, with and without visual inspection, for diagnosing melanoma in adults. Cochrane Database Syst Rev 2018; 12:CD011902.
  70. Que SK, Fraga-Braghiroli N, Grant-Kels JM, et al. Through the looking glass: Basics and principles of reflectance confocal microscopy. J Am Acad Dermatol 2015; 73:276.
  71. Losi A, Longo C, Cesinaro AM, et al. Hyporeflective pagetoid cells: a new clue for amelanotic melanoma diagnosis by reflectance confocal microscopy. Br J Dermatol 2014; 171:48.
  72. Maier T, Sattler EC, Braun-Falco M, et al. Reflectance confocal microscopy in the diagnosis of partially and completely amelanotic melanoma: report on seven cases. J Eur Acad Dermatol Venereol 2013; 27:e42.
  73. Braga JC, Scope A, Klaz I, et al. The significance of reflectance confocal microscopy in the assessment of solitary pink skin lesions. J Am Acad Dermatol 2009; 61:230.
  74. Guitera P, Moloney FJ, Menzies SW, et al. Improving management and patient care in lentigo maligna by mapping with in vivo confocal microscopy. JAMA Dermatol 2013; 149:692.
  75. Dinnes J, Deeks JJ, Saleh D, et al. Reflectance confocal microscopy for diagnosing cutaneous melanoma in adults. Cochrane Database Syst Rev 2018; 12:CD013190.
  76. Pellacani G, Pepe P, Casari A, Longo C. Reflectance confocal microscopy as a second-level examination in skin oncology improves diagnostic accuracy and saves unnecessary excisions: a longitudinal prospective study. Br J Dermatol 2014; 171:1044.
  77. Dreiseitl S, Binder M, Hable K, Kittler H. Computer versus human diagnosis of melanoma: evaluation of the feasibility of an automated diagnostic system in a prospective clinical trial. Melanoma Res 2009; 19:180.
  78. Burroni M, Corona R, Dell'Eva G, et al. Melanoma computer-aided diagnosis: reliability and feasibility study. Clin Cancer Res 2004; 10:1881.
  79. Brinker TJ, Hekler A, Utikal JS, et al. Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review. J Med Internet Res 2018; 20:e11936.
  80. Hekler A, Utikal JS, Enk AH, et al. Superior skin cancer classification by the combination of human and artificial intelligence. Eur J Cancer 2019; 120:114.
  81. Maron RC, Weichenthal M, Utikal JS, et al. Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. Eur J Cancer 2019; 119:57.
  82. Marchetti MA, Liopyris K, Dusza SW, et al. Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017. J Am Acad Dermatol 2020; 82:622.
  83. Dick V, Sinz C, Mittlböck M, et al. Accuracy of Computer-Aided Diagnosis of Melanoma: A Meta-analysis. JAMA Dermatol 2019; 155:1291.
  84. Ferrante di Ruffano L, Takwoingi Y, Dinnes J, et al. Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults. Cochrane Database Syst Rev 2018; 12:CD013186.
  85. Haniffa MA, Lloyd JJ, Lawrence CM. The use of a spectrophotometric intracutaneous analysis device in the real-time diagnosis of melanoma in the setting of a melanoma screening clinic. Br J Dermatol 2007; 156:1350.
  86. Walter FM, Morris HC, Humphrys E, et al. Effect of adding a diagnostic aid to best practice to manage suspicious pigmented lesions in primary care: randomised controlled trial. BMJ 2012; 345:e4110.
  87. MacLellan AN, Price EL, Publicover-Brouwer P, et al. The use of noninvasive imaging techniques in the diagnosis of melanoma: a prospective diagnostic accuracy study. J Am Acad Dermatol 2021; 85:353.
  88. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542:115.
  89. Brinker TJ, Hekler A, Enk AH, et al. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur J Cancer 2019; 113:47.
  90. Haenssle HA, Fink C, Toberer F, et al. Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions. Ann Oncol 2020; 31:137.
  91. Sies K, Winkler JK, Fink C, et al. Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions. Eur J Cancer 2020; 135:39.
  92. Flaten HK, St Claire C, Schlager E, et al. Growth of mobile applications in dermatology - 2017 update. Dermatol Online J 2018; 24.
  93. Chuchu N, Takwoingi Y, Dinnes J, et al. Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma. Cochrane Database Syst Rev 2018; 12:CD013192.
  94. Freeman K, Dinnes J, Chuchu N, et al. Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ 2020; 368:m127.
  95. Gerami P, Alsobrook JP 2nd, Palmer TJ, Robin HS. Development of a novel noninvasive adhesive patch test for the evaluation of pigmented lesions of the skin. J Am Acad Dermatol 2014; 71:237.
  96. Ferris LK, Moy RL, Gerami P, et al. Noninvasive Analysis of High-Risk Driver Mutations and Gene Expression Profiles in Primary Cutaneous Melanoma. J Invest Dermatol 2019; 139:1127.
  97. Lee N, Scope A, Rabinovitz H. Assessing Skin Cancer Using Epidermal Genetic Information Retrieved by Adhesive Patch Skin Surface Sampling. Dermatol Clin 2017; 35:521.
  98. Ferris LK, Rigel DS, Siegel DM, et al. Corrigendum: Impact on clinical practice of a non-invasive gene expression melanoma rule-out test: 12-month followup of negative test results and utility data from a large US registry study. Dermatol Online J 2019; 25.
  99. Swetter SM, Tsao H, Bichakjian CK, et al. Guidelines of care for the management of primary cutaneous melanoma. J Am Acad Dermatol 2019; 80:208.
  100. Marsden JR, Newton-Bishop JA, Burrows L, et al. Revised U.K. guidelines for the management of cutaneous melanoma 2010. Br J Dermatol 2010; 163:238.
  101. https://www.nccn.org/store/login/login.aspx?ReturnURL=https://www.nccn.org/professionals/physician_gls/pdf/cutaneous_melanoma.pdf (Accessed on April 30, 2020).
  102. Mills JK, White I, Diggs B, et al. Effect of biopsy type on outcomes in the treatment of primary cutaneous melanoma. Am J Surg 2013; 205:585.
  103. https://wiki.cancer.org.au/australia/Clinical_question:What_type_of_biopsy_should_be_performed_for_a_suspicious_pigmented_skin_lesion%3F (Accessed on April 30, 2020).
  104. www.aad.org/education-and-quality-care/clinical-guidelines/current-and-upcoming-guidelines/current-guidelines-and-guidelines-in-development (Accessed on April 25, 2011).
  105. Marghoob AA, Terushkin V, Dusza SW, et al. Dermatologists, general practitioners, and the best method to biopsy suspect melanocytic neoplasms. Arch Dermatol 2010; 146:325.
  106. Eriksson H, Frohm-Nilsson M, Hedblad MA, et al. Interobserver variability of histopathological prognostic parameters in cutaneous malignant melanoma: impact on patient management. Acta Derm Venereol 2013; 93:411.
  107. Elmore JG, Barnhill RL, Elder DE, et al. Pathologists' diagnosis of invasive melanoma and melanocytic proliferations: observer accuracy and reproducibility study. BMJ 2017; 357:j2813.
  108. Piepkorn MW, Longton GM, Reisch LM, et al. Assessment of Second-Opinion Strategies for Diagnoses of Cutaneous Melanocytic Lesions. JAMA Netw Open 2019; 2:e1912597.
  109. Ji AL, Bichakjian CK, Swetter SM. Molecular Profiling in Cutaneous Melanoma. J Natl Compr Canc Netw 2016; 14:475.
  110. Minca EC, Al-Rohil RN, Wang M, et al. Comparison between melanoma gene expression score and fluorescence in situ hybridization for the classification of melanocytic lesions. Mod Pathol 2016; 29:832.
  111. Gerami P, Wass A, Mafee M, et al. Fluorescence in situ hybridization for distinguishing nevoid melanomas from mitotically active nevi. Am J Surg Pathol 2009; 33:1783.
  112. Ponti G, Ruini C, Massi D, et al. Fluorescence in-situ hybridization and dermoscopy in the assessment of controversial melanocytic tumors. Melanoma Res 2013; 23:474.
  113. Weissinger SE, Frick M, Möller P, et al. Performance Testing of RREB1, MYB, and CCND1 Fluorescence In Situ Hybridization in Spindle-Cell and Desmoplastic Melanoma Argues for a Two-Step Test Algorithm. Int J Surg Pathol 2017; 25:148.
Topic 15806 Version 42.0

References

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