Why do melanomas get so dark?

Abstract:  Cutaneous malignant melanomas often exhibit pigmented regions that are darker than the surrounding skin. While melanoma cells are the original source of the melanin, keratinocytes and melanophages also contribute to the tumor colour because they contain melanin obtained from melanoma cells. However, little is known of the origin of darkly pigmented melanoma cells or of the molecular pathways regulating their melanin production. Here we discuss observations that dark melanoma cells emerge from within populations of melanoma in situ and that, in addition to producing abundant dark pigment, they appear to be undergoing autophagy. Moreover, autophagy appears to be a common trait of invasive melanoma cells in the dermis. The underlying cause of this phenomenon may stem from aberrant production of glycosylation structures known as β1,6-branched oligosaccharides. Our studies of dark cutaneous melanomas were prompted by analyses of experimental mouse macrophage-melanoma hybrids fused in the laboratory. Like melanoma cells in cutaneous malignant melanoma, experimental hybrids also displayed abundant dark pigment and autophagy, and had high levels of β1,6-branched oligosaccharides. Whether or not darkly pigmented malignant melanoma cells originate from fusion with macrophages in vivo remains to be determined. In any event, pigmentation in melanoma, long considered as a secondary aspect of the malignancy, may be a visible warning that the cells have gained competence for invasion and metastasis.     

Diagnostic ability of general practitioners and dermatologists in discriminating pigmented skin lesions

BACKGROUND: Early recognition of melanoma is the key in preventing metastatic disease. OBJECTIVE: The aim of this study was to evaluate diagnostic ability of general practitioners (GPs) and dermatologists concerning pigmented skin lesions in general and melanoma in particular. We also investigated whether the diagnostic ability of GPs changed after a lecture on melanoma. METHODS: A test set of 13 pigmented skin lesions on 35-mm color slides was presented to 160 GPs and 60 dermatologists during educational courses. RESULTS: GPs correctly evaluated biologic behavior of the pigmented skin lesions in 72% of the evaluations. In 71% of these evaluations they correctly identified the lesions. The proportion of lesions correctly identified was positively correlated with the frequency of pigmented skin lesions in everyday practice. Dermatologists made a correct identification of the lesions in 88% of all evaluations, and they correctly evaluated biologic behavior in 94% of these. Recognition of melanoma was proportional to melanoma exposure in everyday practice. Thick melanomas were better recognized than thin melanomas in both physician groups. After a lecture on melanoma, sensitivity of GPs to recognize malignant disease increased from 72% to 84%, without a significant decrease in specificity. The proportion of lesions correctly identified also rose significantly (66% vs 52%).     

Using 3D differential forms to characterize a pigmented lesion in vivo

Background/purpose: After the formulation of ABCD rules, many new feature extraction methods are emerging to describe the asymmetry, border irregularity, color variation and diameter of malignant melanoma. In this paper, a new research direction orthogonal to ABCD rules that characterizes 3D local disruption of skin surfaces to realize automatic recognition of melanoma is described.
Methods: This paper examines 3D differential forms of skin surfaces to characterize the local geometrical properties of melanoma. Firstly, 3D data of skin surfaces are obtained using a photometric stereo device. Then differential forms of lesion surfaces are determined to describe the geometrical texture patterns involved. Using only these geometrical features, a simple least-squared error-based linear classifier can be constructed to realize the classification of malignant melanomas and benign lesions.
Results: As with the 3D data of 35 melanoma and 66 benign lesion samples collected from local pigmented lesion clinics, the optimal sensitivity and specificity of the constructed linear classifier are 71.4% and 86.4%, respectively. The total area enclosed by the corresponding receiver operating characteristics curve is 0.823.
Conclusion: This study indicates that differential forms obtained from 3D data are very promising in characterizing melanoma. Combining these features with other skin features such as border irregularity and color variation might further improve the accuracy and reliability of the automatic diagnosis of melanoma.     

Principles of dermatoscopy of pigmented skin lesions

There has been a dramatic increase in the incidence of malignant melanoma in most parts of the world. Because the tumor thickness is the most important prognostic factor for the prognosis of the malignant melanoma, the early detection of thin melanomas is essential. Dermatoscopy allows the physician to discriminate between melanocytic and nonmelanocytic lesions with high diagnostic accuracy and to detect initial malignant melanomas. We review the principles of dermatoscopy and the differential diagnosis of pigmented skin lesions. Before using the ABCD rule of dermatoscopy to classify melanocytic lesions into benign, suspicious, or malignant, the distinction between melanocytic and nonmelanocytic lesions is necessary. An essential prerequisite for the usefulness of this technique is adequate training.     

Electrical impedance spectroscopy and the diagnostic accuracy for malignant melanoma

Abstract: Background: The accuracy of diagnosis of skin cancer and especially of early malignant melanoma is most important to reduce its morbidity and mortality. Previous pilot studies using electrical impedance measurements indicate statistically significant accuracies for the detection of skin cancer. Objectives: The aim of this study is to investigate the accuracy of electrical impedance spectra to distinguish between malignant melanoma and benign skin lesions using an automated classification algorithm. Patients/Methods: Electrical impedance spectra were measured in a multi‐centre study at 12 clinics around Europe. Data from 285 histologically analysed lesions were used to train an algorithm to sort out lesions for automatic detection of melanoma. Another data cohort of 210 blinded lesions (148 various benign lesions and 62 malignant melanomas where 38 being from Breslow thickness ≤1 mm) from 183 patients was thereafter used to estimate the accuracy of the technique. Results: Observed sensitivity to malignant melanoma is 95% (59/62) and observed specificity 49% (72/148). Conclusions: The results suggest that electrical impedance spectra can distinguish between malignant melanoma and benign skin lesions. Although it is indicated that the accuracy of the device is clinically promising, the overall performance, and the sensitivity to thin malignant melanomas, must be improved and thoroughly validated before the instrument can be used as a routine stand‐alone diagnostic decision support tool. The technique is under revision to further improve the reproducibility, specificity and sensitivity.     

Melanocytic nevi clinically simulating melanoma

Melanoma and other benign or malignant pigmented skin tumors can significantly overlap in their clinical and dermoscopical presentations. Thus, pigmented skin lesions may be misdiagnosed in a large number of cases. An extensive review of the published work provides numerous examples of benign lesions mimicking melanoma. Although a number of melanocytic nevi may have been identified as melanomas, information about their clinical appearance is limited. In this report, we present the clinical appearances of two melanocytic nevi on the vulva and the upper extremity that were difficult to diagnose clinically. Detecting melanoma at an early stage is of the utmost importance. However, more attention should be given to the diagnostic accuracy of benign pigmented skin lesions, which otherwise may be diagnosed and treated as melanoma.     

Current technologies for the in vivo diagnosis of cutaneous melanomas

The rising incidence of cutaneous malignant melanoma has been observed in the past decades. Currently, there is no cure for metastatic melanoma; only early diagnosis followed by prompt excision of cutaneous lesions ensures a good prognosis. The clinical ABCD rule is created as a framework for differentiating melanomas from benign pigmented skin lesions, and it serves as the basis for current clinical diagnosis. The ABCD rule relies on four simple clinical morphologies of melanoma: 1) Asymmetry, 2) Border irregularity, 3) Color variegation, and 4) Diameter greater than 6 mm. Although it is valuable, it has its limitations. Currently, the diagnostic accuracy for physicians is about 65%. This statistic implies that 1) melanomas with subtle signs are missed as benign lesions, and 2) benign lesions are over diagnosed as melanomas, which lead to unnecessary biopsies.     

Laypersons' perceptual discrimination of pigmented skin lesions

BACKGROUND: Most cutaneous malignant melanomas of the skin are visible and should, at least in theory, be possible to detect with the naked eye. OBJECTIVE: This study was conducted to learn more about laypersons' ability to discriminate between benign pigmented lesions and malignant ones. METHODS: Four groups of laypersons (n = 120) were asked to evaluate pictures of different types of pigmented skin lesions, before and after they received information about the ABCD (asymmetry, border irregularity, color variegation, and diameter greater than 6 mm) criteria, with respect to the necessity of action. RESULTS: The respondents made adequate assessments of melanomas but overestimated the danger of benign pigmented skin lesions. Information about the ABCD criteria enhanced their ability to make adequate assessments. CONCLUSION: People seem to make adequate decisions concerning how to act if they have a melanoma. On the other hand, common moles and dysplastic nevi were harder to discriminate. Providing information to the public about the features of melanomas, in accordance with the ABCD criteria, might help laypersons in their perceptual discrimination of skin lesions.     

Awareness and early detection of cutaneous melanoma: an analysis of factors related to delay in treatment

Factors associated with the detection of cutaneous melanomas and reasons for delay in diagnosis were investigated in 429 patients with histologically proven melanoma operated on between January 1993 and June 1996. Patients were interviewed using a standardized questionnaire. In 25% of patients, treatment was delayed for more than 1 year from the time they first noticed a suspicious pigmented lesion. Melanoma was detected by the patients themselves in 67% of women and 45% of men. The three predominant clinical symptoms of melanoma were change in colour (darker), increase in size and increase in elevation of a pigmented lesion. The role of sun exposure and of naevi as risk factors for melanoma, as well as the potential benefit of early treatment, were known by 87%, 66% and 82% of the patients, respectively. However, melanoma awareness had no impact on the time period between first observation of skin changes and treatment. Among the factors associated with delay in melanoma diagnosis, an initial incorrect diagnosis as a benign lesion by the physician first visited (in 18% of all cases) had the highest significance. Patients detecting their lesions themselves were treated significantly later than patients in whom others had remarked on changes in a naevus. Furthermore, melanomas of the head and neck were treated later than melanomas at other body sites. Further efforts to educate both the public and the medical profession are essential to ensure earlier treatment for cutaneous melanomas.     

Digital dermoscopy analysis and artificial neural network for the differentiation of clinically atypical pigmented skin lesions: a retrospective study

Noninvasive diagnostic methods such as dermoscopy or epiluminescence light microscopy have been developed in an attempt to improve diagnostic accuracy of pigmented skin lesions. The evaluation of the many morphologic characteristics of pigmented skin lesions observable by epiluminescence light microscopy, however, is often extremely complex and subjective. With the aim of obviating these problems of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions have recently been designed. These methods are based on computerized analysis of digital images obtained by epiluminescence light microscopy. In this study we used a digital dermoscopy analyzer with 147 clinically atypical pigmented skin lesions (90 nevi and 57 melanomas) to determine its discriminating power with respect to histologic diagnosis. The system evaluated 48 objective parameters used to train an artificial neural network. Using the artificial neural network with 10 variables selected by a stepwise procedure, we obtained a maximum accuracy in distinguishing melanoma from benign lesions of about 93%. Comparing this result with those of the many studies using classical epiluminescence light microscopy, it emerges that the method proposed is equal or even superior in diagnostic accuracy and has the advantage of not depending on the expertise of the clinician who examines the lesion.     

Patient acceptance and diagnostic utility of automated digital image analysis of pigmented skin lesions

Background Computerized analysis of pigmented skin lesions may help to increase diagnostic accuracy for melanoma, help to avoid unnecessary procedures and reduce health care costs. Objectives We evaluated both the patient acceptance and diagnostic utility of such an analysis tool in a real clinical setting. Methods Two hundred nine consecutive patients (median age: 34 years, range: 2–73 years), who were concerned about a pigmented skin lesion, answered a questionnaire about their attitude towards computerized analysis and their confidence in the resulting findings. Using a dermoscopy analyser, their skin lesions (n = 219) were then grouped into the categories, benign, suspicious and malignant, and results were compared with those obtained by in‐person examination of dermato‐oncologic experts. Results More than half of the patients (n = 114) would accept the use of computer analysis for melanoma screening; although 16 (14.0%) patients would accept this method solely, 98 (86.0%) patients would prefer an additional in‐person examination by a dermatologist. Of the 219 pigmented skin lesions, the dermoscopic experts rated 171 (78.1%) as benign, 36 (16.4%) as suspicious and 12 (5.5%) as malignant, whereas computer analysis revealed 102 (46.6%) benign, 78 (35.6%) suspicious and 39 (17.8%) malignant lesions. At the expense of specificity (48.8%), the sensitivity of computerized analysis was excellent (100%) and equal to that of in‐person examination. Conclusions Most patients would accept computer analysis for melanoma screening, some of them even without reservations. However, due to a high rate of false positive computer assessments, it cannot be recommended as a screening tool at this time.     

Surface microscopy of pigmented basal cell carcinoma

OBJECTIVES: To describe the relevant morphologic features and to create a simple diagnostic method for pigmented basal cell carcinoma (BCC) using in vivo cutaneous surface microscopy (ie, dermoscopy, dermatoscopy, or oil epiluminescence microscopy). DESIGN: Pigmented skin lesions were photographed in vivo using immersion oil (surface microscopy). All pigmented skin lesions were excised and reviewed for histological diagnosis. Photographs of 142 pigmented BCCs, 142 invasive melanomas, and 142 benign pigmented skin lesions were randomly divided into 2 equally sized training and test sets. Images from the training set were scored for 45 surface microscopy features. From this a model was derived and tested on the independent test set. SETTING: All patients were recruited from the primary case and referral centers of the Sydney Melanoma Unit, Sydney, Australia, and the Skin and Cancer Unit, Skin and Cancer Associates, Plantation, Fla. PATIENTS: A random sample (selected from a larger database) of patients whose lesions were excised. MAIN OUTCOME MEASURES: Sensitivity and specificity of the model for diagnosis of pigmented BCCs. RESULTS: The following model was created. For a pigmented BCC to be diagnosed it must not have the negative feature of a pigment network and must have 1 or more of the following 6 positive features: large gray-blue ovoid nests, multiple gray-blue globules, maple leaflike areas, spoke wheel areas, ulceration, and arborizing "treelike" telangiectasia. On an independent test set the model had a sensitivity of 97% for the diagnosis of pigmented BCCs and a specificity of 93% for the invasive melanoma set and 92% for the benign pigmented skin lesion set. CONCLUSION: A robust surface microscopy method is described that allows the diagnosis of pigmented BCCs from invasive melanomas and benign pigmented skin lesions. Arch Dermatol. 2000;136:1012-1016     

Digital computer analysis of dermatoscopical images of 260 melanocytic skin lesions; perimeter/area ratio for the differentiation between malignant melanomas and melanocytic nevi

BACKGROUND: Digital computer analysis of dermatoscopical images has been reported to facilitate the differential diagnosis of pigmented skin lesions in recent years. OBJECTIVE: The aim of our study was to perform digital computer analysis of a set of different melanocytic lesions and compare the objective results. METHODS: The set of 260 melanocytic lesions (150 excised difficult cases (46 melanomas, 47 atypical nevi, 57 common nevi and 110 unexcised common nevi) was automatically analysed by the digital dermatoscopical system microDERM. We searched for differences in asymmetry, size, compactness and colour distribution. Perimeter/area ratio was calculated. RESULTS: The perimeter/area ratio was detected as the most important criterion for differentiation between malignant and benign melanocytic lesions (sensitivity 91.3% and specificity 90.7% for malignant melanomas vs. all benign nevi; sensitivity 91.3% and specificity 80.8% for melanomas vs. clinically atypical nevi). Differences in size of the lesion, shape and asymmetry of colour were found and statistically verified. Using step-wise logistic regression the formula for calculation of probability of malignant nature of every analysed lesion was constructed. CONCLUSION: The perimeter/area ratio is a simple parameter for the differential diagnosis of melanocytic skin lesions.     

Accuracy in skin cancer diagnosis: a retrospective study of an Australian public hospital dermatology department

Background/Objectives: Accurate identification of skin lesions is vital in ensuring malignancies are not missed and that they are treated early to avoid mortality. It is also important that appropriate lesions are submitted for biopsy to decrease the costs and morbidity associated with the unnecessary removal of benign lesions. This study attempted to assess current accuracy in skin cancer diagnosis. Methods: Histology reports for all biopsies and excisions performed in an 18‐month period at a public hospital dermatology department were reviewed. Dermatology registrars and consultants were involved in assessing lesions for biopsy. Calculations were made to quantify the sensitivity of melanoma diagnosis; naevi to melanoma ratio (NMR); biopsy to treatment ratio (BTR), and number needed to treat for melanoma (NNT). NNT was calculated as the number of pigmented lesions (seborrhoeic keratoses, naevi and melanoma) removed to identify one melanoma. Results: 6546 biopsies/excisions were performed, identifying 55 melanomas. The sensitivity of melanoma diagnosis was 76% and 11% of melanomas were thought to be dysplastic naevi. The NMR was 6.4. The BTR was 1.97, indicating that one in every 1.97 biopsies was identified as a non‐melanoma skin cancer. The NNT was 11.9. All dysplastic naevi and 91% of melanomas were biopsied using either shave or excision biopsy. Conclusions: These audits are important to ensure quality of care and could aid in identifying doctors and institutions that may benefit from further training in melanoma diagnostic algorithms. These figures can be used as a benchmark to measure the impact of new vectors in skin cancer diagnosis as they are introduced.     

A simple scoring system for the diagnosis of palmo‐plantar pigmented skin lesions by digital dermoscopy analysis

Background Many research groups have recently developed equipments and statistical methods enabling pattern classification of pigmented skin lesions. To differentiate between benign and malignant ones, the mathematical extraction of digital patterns together with the use of appropriate statistical approaches is a challenging task. Objective To design a simple scoring model that provides accurate classification of benign and malignant palmo‐plantar pigmented skin lesions, by evaluation of parameters obtained by digital dermoscopy analysis (DDA). Patients and Methods In the present study we used a digital dermoscopy analyser to evaluate a series of 445 palmo‐plantar melanocytic skin lesion images (25 melanomas 420 nevi). Area under the receiver operator curve, sensitivity and specificity were calculated to evaluate the diagnostic performance of our scoring model for the differentiation of benign and malignant palmo‐plantar melanocytic lesions. Results Model performance reached a very high value (0.983). The DDA parameters selected by the model that proved statistically significant were: area, peripheral dark regions, total imbalance of colours, entropy, dark area and red and blue multicomponent. When all seven model variables were used in a multivariate mode, setting sensitivity at 100% to avoid false negatives, we estimated a minimum specificity of about 80%. Conclusions Simplicity of use and effectiveness of implementation are important requirements for the success of quantitative methods in routine clinical practice. Scoring systems meet these requirements. Their outcomes are accessible in real time without the use of any data processing system, thus allowing decisions to be made quickly and effectively.     

Pre-diagnostic digital imaging prediction model to discriminate between malignant melanoma and benign pigmented skin lesion

Background: Malignant cutaneous melanoma is the most deadly form of skin cancer with an increasing incidence over the past decades. The final diagnosis provided is typically based on a biopsy of the skin lesion under consideration. To assist the naked-eye examination and decision on whether or not a biopsy is necessary, digital image processing techniques provide promising results.
Hypothesis and aims: The hypothesis of this study was that a computer-aided assessment tool could assist the evaluation of a pigmented skin lesion. Hence, the overall aim was to discriminate between malignant and benign pigmented skin lesions using digital image processing.
Methods: Discriminating algorithms utilizing novel well-established morphological operations and methods were constructed. The algorithms were implemented utilizing graphical programming (LabVIEW Vision). Verification was performed with reference to an image database consisting of 97 pigmented skin lesion pictures of various resolutions and light distributions. The outcome of the algorithms was analysed statistically with MATLAB and a prediction model was constructed.
Results/Conclusion: The prediction model evaluates pigmented skin lesions with regards to the overall shape, border and colour distribution with a total of nine different discriminating parameters. The prediction model outputs an index score, and by using the optimal threshold value, a diagnostic accuracy of 77% in discriminating between malignant and benign skin lesions was obtained. This is an improvement compared with the naked-eye analysis performed by professionals, rendering the system a significant assistance in detecting malignant cutaneous melanoma.     

Digital dermoscopy analysis of atypical pigmented skin lesions: a stepwise logistic discriminant analysis approach

Background: Digital microscopy is a non-invasive diagnostic technique enabling determination of characteristics that cannot be appreciated by direct observation. If correctly applied, this technique can be useful for the diagnosis of pigmented skin lesions.
Purpose: To evaluate the utility of digital microscopy for analysing atypical benign and malignant pigmented skin lesions exploiting digital numerical filtering and automatic measurements.
Methods: Forty-eight parameters were identified as possible discriminating variables, and were grouped in four categories: geometries, colours, textures, and islands of colour. Statistical analysis was used to identify the variables with the highest discriminating power.
Results: The high quality of the digital image made it possible to observe diagnostic signs in pigmented skin lesion images, acquired by the present technique, in great detail. Specially designed filtering enhanced certain diagnostic patterns. Stepwise discriminant analysis selected only 10 variables (the means of these variables were higher in melanomas than in nevi).
Conclusions: The combined use of digital dermoscopy and stepwise logistic discriminant analysis made it possible to single out the best objective variables for distinguishing atypical nevi and early melanoma.     

Predictive power of irregular border shapes for malignant melanomas

BACKGROUND/PURPOSE: The Irregularity Index is a measure of border irregularity from pigmented skin lesion images. The measure attempts to quantify the degree of irregularity of the structural indentations and protrusions along a lesion border. A carefully designed study has shown that the parameters derived from the Irregularity Index were highly correlated with expert dermatologists' notion of border shape. This paper investigates the predictive power of these parameters on a set of data with known histological diagnosis. METHODS: A set of 188 pigmented skin lesions (30 malignant melanomas and 158 benign lesions) was selected for the study. Their images were segmented and their border shapes were analysed by the Irregularity Index, producing four border irregularity parameters. The predictive power of these four parameters was estimated by a series of statistical tests. RESULTS: The mean values of the four border irregularity parameters were significantly different between the melanoma group and the benign lesion group. When using the four parameters to predict its disease status, the leave-one-out classification rate was 82.4%, and the area under the receiver operating characteristic curve was 0.77. A malignant melanoma was 8.9 times more likely to have an irregular border than a benign lesion. CONCLUSION: This study confirmed that border irregularity is an important clinical feature for the diagnosis of malignant melanoma. It also indicates that the computer-derived measures based on the Irregularity Index capture to certain extent the kind of irregularity which is exhibited by melanomas.     

A basis function feature‐based approach for skin lesion discrimination in dermatology dermoscopy images

Background: Skin lesion color is an important feature for diagnosing malignant melanoma. New basis function correlation features are proposed for discriminating malignant melanoma lesions from benign lesions in dermoscopy images. The proposed features are computed based on correlating the luminance histogram of melanoma or benign labeled relative colors from a specified portion of the skin lesion with a set of basis functions. These features extend previously developed statistical and fuzzy logic‐based relative color histogram analysis techniques for automated mapping of colors representative of melanoma and benign skin lesions from a training set of lesion images. Methods: Using the statistical and fuzzy logic‐based approaches for relative color mapping, melanoma and benign color features are computed over skin lesion region of interest, respectively. Luminance histograms are obtained from the melanoma and benign mapped colors within the lesion region of interest and are correlated with a set of basis functions to quantify the distribution of colors. The histogram analysis techniques and feature calculations are evaluated using a data set of 279 malignant melanomas and 442 benign dysplastic nevi images. Results: Experimental test results showed that combining existing melanoma and benign color features with the proposed basis function features found from the melanoma mapped colors yielded average correct melanoma and benign lesion discrimination rates as high as 86.45% and 83.35%, respectively. Conclusions: The basis function features provide an alternative approach to melanoma discrimination that quantifies the variation and distribution of colors characteristic of melanoma and benign skin lesions.