Morphology of melanocytic lesions in situ.
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ABSTRACT:
SUBMITTER: Balois T
PROVIDER: S-EPMC3887378 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
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Morphology of melanocytic lesions in situ.
Balois Thibaut T Ben Amar Martine M
Scientific reports 20140110
Melanoma is a solid tumour with its own specificity from the biological and morphological viewpoint. On one hand, numerous mutations are already known affecting different pathways. They usually concern proliferation rate, apoptosis, cell senescence and cell behaviour. On the other hand, several visual criteria at the tissue level are used by physicians in order to diagnose skin lesions. Nevertheless, the mechanisms between the changes from the mutations at the cell level to the morphology exhibi ...[more]
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Project description:IntroductionDiagnosing invasive cutaneous melanoma (CM) can be challenging due to subjectivity in distinguishing equivocal nevi, melanoma in situ and thin CMs. The underlying molecular mechanisms of progression from nevus to melanoma must be better understood. Identifying biomarkers for treatment response, diagnostics and prognostics is crucial. Using biomedical data from biobanks and population-based healthcare data, translational research can improve patient care by implementing evidence-based findings. The BioMEL biobank is a prospective, multicentre, large-scale biomedical database on equivocal nevi and all stages of primary melanoma to metastases. Its purpose is to serve as a translational resource, enabling researchers to uncover objective molecular, genotypic, phenotypic and structural differences in nevi and all stages of melanoma. The main objective is to leverage BioMEL to significantly improve diagnostics, prognostics and therapy outcomes of patients with melanoma.Methods and analysisThe BioMEL biobank contains biological samples, epidemiological information and medical data from adult patients who receive routine care for melanoma. BioMEL is focused on primary and metastatic melanoma, but equivocal pigmented lesions such as clinically atypical nevi and melanoma in situ are also included. BioMEL data are gathered by questionnaires, blood sampling, tumour imaging, tissue sampling, medical records and histopathological reports.Ethics and disseminationThe BioMEL biobank project is approved by the national Swedish Ethical Review Authority (Dnr. 2013/101, 2013/339, 2020/00469, 2021/01432 and 2022/02421-02). The datasets generated are not publicly available due to regulations related to the ethical review authority.Trial registration numberNCT05446155.
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Project description:ImportanceA standardized pathology classification system for melanocytic lesions is needed to aid both pathologists and clinicians in cataloging currently existing diverse terminologies and in the diagnosis and treatment of patients. The Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) has been developed for this purpose.ObjectiveTo revise the MPATH-Dx version 1.0 classification tool, using feedback from dermatopathologists participating in the National Institutes of Health-funded Reducing Errors in Melanocytic Interpretations (REMI) Study and from members of the International Melanoma Pathology Study Group (IMPSG).Evidence reviewPracticing dermatopathologists recruited from 40 US states participated in the 2-year REMI study and provided feedback on the MPATH-Dx version 1.0 tool. Independently, member dermatopathologists participating in an IMPSG workshop dedicated to the MPATH-Dx schema provided additional input for refining the MPATH-Dx tool. A reference panel of 3 dermatopathologists, the original authors of the MPATH-Dx version 1.0 tool, integrated all feedback into an updated and refined MPATH-Dx version 2.0.FindingsThe new MPATH-Dx version 2.0 schema simplifies the original 5-class hierarchy into 4 classes to improve diagnostic concordance and to provide more explicit guidance in the treatment of patients. This new version also has clearly defined histopathological criteria for classification of classes I and II lesions; has specific provisions for the most frequently encountered low-cumulative sun damage pathway of melanoma progression, as well as other, less common World Health Organization pathways to melanoma; provides guidance for classifying intermediate class II tumors vs melanoma; and recognizes a subset of pT1a melanomas with very low risk and possible eventual reclassification as neoplasms lacking criteria for melanoma.Conclusions and relevanceThe implementation of the newly revised MPATH-Dx version 2.0 schema into clinical practice is anticipated to provide a robust tool and adjunct for standardized diagnostic reporting of melanocytic lesions and management of patients to the benefit of both health care practitioners and patients.
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Project description:Melanocytic neoplasms have been genetically characterized in detail during the last decade. Recurrent CTNNB1 exon 3 mutations have been recognized in the distinct group of melanocytic tumors showing deep penetrating nevus-like morphology. In addition, they have been identified in 1-2% of advanced melanoma. Performing a detailed genetic analysis of difficult-to-classify nevi and melanomas with CTNNB1 mutations, we found that benign tumors (nevi) show characteristic morphological, genetic and epigenetic traits, which distinguish them from other nevi and melanoma. Malignant CTNNB1-mutant tumors (melanomas) demonstrated a different genetic profile, instead grouping clearly with other non-CTNNB1 melanomas in methylation assays. To further evaluate the role of CTNNB1 mutations in melanoma, we assessed a large cohort of clinically sequenced melanomas, identifying 38 tumors with CTNNB1 exon 3 mutations, including recurrent S45 (n = 13, 34%), G34 (n = 5, 13%), and S27 (n = 5, 13%) mutations. Locations and histological subtype of CTNNB1-mutated melanoma varied; none were reported as showing deep penetrating nevus-like morphology. The most frequent concurrent activating mutations were BRAF V600 (n = 21, 55%) and NRAS Q61 (n = 13, 34%). In our cohort, four of seven (58%) and one of nine (11%) patients treated with targeted therapy (BRAF and MEK Inhibitors) or immune-checkpoint therapy, respectively, showed disease control (partial response or stable disease). In summary, CTNNB1 mutations are associated with a unique melanocytic tumor type in benign tumors (nevi), which can be applied in a diagnostic setting. In advanced disease, no clear characteristics distinguishing CTNNB1-mutant from other melanomas were observed; however, studies of larger, optimally prospective, cohorts are warranted.
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Project description:BackgroundDeep learning has the potential to improve diagnostic accuracy and efficiency in medical image recognition. In the current study, we developed a deep learning algorithm and assessed its performance in discriminating melanoma from nevus using whole-slide pathological images (WSIs).MethodsThe deep learning algorithm was trained and validated using a set of 781 WSIs (86 melanomas, 695 nevi) from PLA General Hospital. The diagnostic performance of the algorithm was tested on an independent test set of 104 WSIs (29 melanomas, 75 nevi) from Tianjin Chang Zheng Hospital. The same test set was also diagnostically classified by 7 expert dermatopathologists.ResultsThe deep learning algorithm receiver operating characteristic (ROC) curve achieved a sensitivity 100% at the specificity of 94.7% in the classification of melanoma and nevus on the test set. The area under ROC curve was 0.99. Dermatopathologists achieved a mean sensitivity and specificity of 95.1% (95% confidence interval [CI]: 92.0%-98.2%) and 96.0% (95% CI: 94.2%-97.8%), respectively. At the operating point of sensitivity of 95.1%, the algorithm revealed a comparable specificity with 7 dermatopathologists (97.3% vs. 96.0%, P = 0.11). At the operating point of specificity of 96.0%, the algorithm also achieved a comparable sensitivity with 7 dermatopathologists (96.5% vs. 95.1%, P = 0.30). A more transparent and interpretable diagnosis could be generated by highlighting the regions of interest recognized by the algorithm in WSIs.ConclusionThe performance of the deep learning algorithm was on par with that of 7 expert dermatopathologists in interpreting WSIs with melanocytic lesions. By pre-screening the suspicious melanoma regions, it might serve as a supplemental diagnostic tool to improve working efficiency of pathologists.
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Project description:ImportanceDespite evidence of overdiagnosis of in situ and invasive melanoma, neither the perceptions of practicing dermatopathologists about overdiagnosis nor possible associations between perceptions of overdiagnosis and diagnostic practices have been studied.ObjectiveTo examine practicing US dermatopathologists' perceptions of melanoma overdiagnosis as a public health issue, and to associate diagnostic behaviors of dermatopathologists with perceptions of melanoma overdiagnosis.Design, setting, and participantsThis survey study included 115 board-certified and/or fellowship-trained dermatopathologists and their diagnostic interpretations on a set of 18 skin biopsy cases (5 slide sets comprising 90 melanocytic skin lesions). Participants interpreted cases remotely using their own microscopes. Survey invitations occurred during 2018 to 2019, with data collection completed 2021. Data analysis was performed from June to September 2021.Main outcomes and measuresAgreement vs disagreement that overdiagnosis is a public health issue for atypical nevi, melanoma in situ, and invasive melanoma. Associations between perceptions regarding overdiagnosis and interpretive behavior on study cases.ResultsOf 115 dermatopathologists, 68% (95% CI, 59%-76%) agreed that overdiagnosis is a public health issue for atypical nevi; 47% (95% CI, 38%-56%) for melanoma in situ; and 35% (95% CI, 26%-43%) for invasive melanoma. Dermatopathologists with more years in practice were significantly less likely to perceive that atypical nevi are overdiagnosed, eg, 46% of dermatopathologists with 20 or more years of experience agreed that atypical nevi are overdiagnosed compared with 93% of dermatopathologists with 1 to 4 years of experience. Compared with other dermatopathologists, those who agreed that all 3 conditions are overdiagnosed were slightly more likely to diagnose study cases as mild to moderately dysplastic nevi (odds ratio, 1.26; 95% CI, 0.97-1.64; P = .08), but the difference was not statistically significant. Dermatopathologists who agreed that invasive melanoma is overdiagnosed did not significantly differ in diagnosing invasive melanoma for study cases compared with those who disagreed (odds ratio, 1.10; 95% CI, 0.86-1.41; P = .44).Conclusions and relevanceIn this survey study, about two-thirds of dermatopathologists thought that atypical nevi are overdiagnosed, half thought that melanoma in situ is overdiagnosed, and one-third thought that invasive melanoma is overdiagnosed. No statistically significant associations were found between perceptions about overdiagnosis and interpretive behavior when diagnosing skin biopsy cases.
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