Accuracy of Digital Pathologic Analysis vs Traditional Microscopy in the Interpretation of Melanocytic Lesions

Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns

Deep learning techniques offer improvements in computer-aided diagnosis systems. However, acquiring image domain annotations is challenging due to the knowledge and commitment required of expert pathologists. Pathologists often identify regions in whole slide images with diagnostic relevance rather than examining the entire slide, with a positive correlation between the time spent on these critical image regions and diagnostic accuracy. In this paper, a heatmap is generated to represent pathologists' viewing patterns during diagnosis and used to guide a deep learning architecture during training. The proposed system outperforms traditional approaches based on color and texture image characteristics, integrating pathologists' domain expertise to enhance region of interest detection without needing individual case annotations. Evaluating our best model, a U-Net model with a pre-trained ResNet-18 encoder, on a skin biopsy whole slide image dataset for melanoma diagnosis, shows its potential in detecting regions of interest, surpassing conventional methods with an increase of 20%, 11%, 22%, and 12% in precision, recall, F1-score, and Intersection over Union, respectively. In a clinical evaluation, three dermatopathologists agreed on the model's effectiveness in replicating pathologists' diagnostic viewing behavior and accurately identifying critical regions. Finally, our study demonstrates that incorporating heatmaps as supplementary signals can enhance the performance of computer-aided diagnosis systems. Without the availability of eye tracking data, identifying precise focus areas is challenging, but our approach shows promise in assisting pathologists in improving diagnostic accuracy and efficiency, streamlining annotation processes, and aiding the training of new pathologists.

The Role of Artificial Intelligence in Early Diagnosis and Molecular Classification of Head and Neck Skin Cancers: A Multidisciplinary Approach

Cancer remains a significant global health concern, with increasing genetic and metabolic irregularities linked to its onset. Among various forms of cancer, skin cancer, including squamous cell carcinoma, basal cell carcinoma, and melanoma, is on the rise worldwide, often triggered by ultraviolet (UV) radiation. The propensity of skin cancer to metastasize highlights the importance of early detection for successful treatment. This narrative review explores the evolving role of artificial intelligence (AI) in diagnosing head and neck skin cancers from both radiological and pathological perspectives. In the past two decades, AI has made remarkable progress in skin cancer research, driven by advances in computational capabilities, digitalization of medical images, and radiomics data. AI has shown significant promise in image-based diagnosis across various medical domains. In dermatology, AI has played a pivotal role in refining diagnostic and treatment strategies, including genomic risk assessment. This technology offers substantial potential to aid primary clinicians in improving patient outcomes. Studies have demonstrated AI's effectiveness in identifying skin lesions, categorizing them, and assessing their malignancy, contributing to earlier interventions and better prognosis. The rising incidence and mortality rates of skin cancer, coupled with the high cost of treatment, emphasize the need for early diagnosis. Further research and integration of AI into clinical practice are warranted to maximize its benefits in skin cancer diagnosis and treatment.

Integrating Spatial and Morphological Characteristics into Melanoma Prognosis: A Computational Approach

In this study, the prognostic value of cellular morphology and spatial configurations in melanoma has been examined, aiming to complement traditional prognostic indicators like mitotic activity and tumor thickness. Through a computational pipeline using machine learning and deep learning methods, we quantified nuclei sizes within different spatial regions and analyzed their prognostic significance using univariate and multivariate Cox models. Nuclei sizes in the invasive band demonstrated a significant hazard ratio (HR) of 1.1 (95% CI: 1.03, 1.18). Similarly, the nuclei sizes of tumor cells and Ki67 S100 co-positive cells in the invasive band achieved HRs of 1.07 (95% CI: 1.02, 1.13) and 1.09 (95% CI: 1.04, 1.16), respectively. Our findings reveal that nuclei sizes, particularly in the invasive band, are potentially prognostic factors. Correlation analyses further demonstrated a meaningful relationship between cellular morphology and tumor progression, notably showing that nuclei size within the invasive band correlates substantially with tumor thickness. These results suggest the potential of integrating spatial and morphological analyses into melanoma prognostication.

Digital pathology for reporting histopathology samples, including cancer screening samples - definitive evidence from a multisite study

AIMS

To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples.

METHODS

A total of 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, including 207 breast and 250 bowel cancer screening samples. Cases were examined by four pathologists (16 study pathologists across the four speciality groups), using both LM and DP, with the order randomly assigned and 6 weeks between viewings. Reports were compared for clinical management concordance (CMC), meaning identical diagnoses plus differences which do not affect patient management. Percentage CMCs were computed using logistic regression models with crossed random-effects terms for case and pathologist. The obtained percentage CMCs were referenced to 98.3% calculated from previous studies.

RESULTS

For all cases LM versus DP comparisons showed the CMC rates were 99.95% [95% confidence interval (CI) = 99.90-99.97] and 98.96 (95% CI = 98.42-99.32) for cancer screening samples. In speciality groups CMC for LM versus DP showed: breast 99.40% (99.06-99.62) overall and 96.27% (94.63-97.43) for cancer screening samples; [gastrointestinal (GI) = 99.96% (99.89-99.99)] overall and 99.93% (99.68-99.98) for bowel cancer screening samples; skin 99.99% (99.92-100.0); renal 99.99% (99.57-100.0). Analysis of clinically significant differences revealed discrepancies in areas where interobserver variability is known to be high, in reads performed with both modalities and without apparent trends to either.

CONCLUSIONS

Comparing LM and DP CMC, overall rates exceed the reference 98.3%, providing compelling evidence that pathologists provide equivalent results for both routine and cancer screening samples irrespective of the modality used.

Artificial intelligence and skin cancer

Artificial intelligence is poised to rapidly reshape many fields, including that of skin cancer screening and diagnosis, both as a disruptive and assistive technology. Together with the collection and availability of large medical data sets, artificial intelligence will become a powerful tool that can be leveraged by physicians in their diagnoses and treatment plans for patients. This comprehensive review focuses on current progress toward AI applications for patients, primary care providers, dermatologists, and dermatopathologists, explores the diverse applications of image and molecular processing for skin cancer, and highlights AI's potential for patient self-screening and improving diagnostic accuracy for non-dermatologists. We additionally delve into the challenges and barriers to clinical implementation, paths forward for implementation and areas of active research.

Pathologist Characteristics Associated With Rendering Higher-Grade Diagnoses for Melanocytic Lesions

Importance

The incidence of melanoma diagnoses has been increasing in recent decades, and controlled studies have indicated high histopathologic discordance across the intermediate range of melanocytic lesions. The respective causes for these phenomena remain incompletely understood.

Objective

To identify pathologist characteristics associated with tendencies to diagnose melanocytic lesions as higher grade vs lower grade or to diagnose invasive melanoma vs any less severe diagnosis.

Design, Setting, and Participants

This exploratory study used data from 2 nationwide studies (the Melanoma Pathology [M-Path] study, conducted from July 2013 to May 2016, and the Reducing Errors in Melanocytic Interpretations [REMI] study, conducted from August 2018 to March 2021) in which participating pathologists who interpreted melanocytic lesions in their clinical practices interpreted study cases in glass slide format. Each pathologist was randomly assigned to interpret a set of study cases from a repository of skin biopsy samples of melanocytic lesions; each case was independently interpreted by multiple pathologists. Data were analyzed from July 2022 to February 2023.

Main Outcomes and Measures

The association of pathologist characteristics with diagnosis of a study case as higher grade (including severely dysplastic and melanoma in situ) vs lower grade (including mild to moderately dysplastic nevi) and diagnosis of invasive melanoma vs any less severe diagnosis was assessed using logistic regression. Characteristics included demographics (age, gender, and geographic region), years of experience, academic affiliation, caseload of melanocytic lesions in their practice, specialty training, and history of malpractice suits.

Results

A total of 338 pathologists were included: 113 general pathologists and 74 dermatopathologists from M-Path and 151 dermatopathologists from REMI. The predominant factor associated with rendering more severe diagnoses was specialist training in dermatopathology (board certification and/or fellowship training). Pathologists with this training were more likely to render higher-grade diagnoses (odds ratio [OR], 2.63; 95% CI, 2.10-3.30; P < .001) and to diagnose invasive melanoma (OR, 1.95; 95% CI, 1.53-2.49; P < .001) than pathologists without this training interpreting the same case. Nonmitogenic pT1a diagnoses (stage pT1a melanomas with no mitotic activity) accounted for the observed difference in diagnosis of invasive melanoma; when these lesions, which carry a low risk of metastasis, were grouped with the less severe diagnoses, there was no observed association (OR, 0.95; 95% CI, 0.74-1.23; P = .71). Among dermatopathologists, those with a higher caseload of melanocytic lesions in their practice were more likely to assign higher-grade diagnoses (OR for trend, 1.27; 95% CI, 1.04-1.56; P = .02).

Conclusions and Relevance

The findings suggest that specialty training in dermatopathology is associated with a greater tendency to diagnose atypical melanocytic proliferations as pT1a melanomas. These low-risk melanomas constitute a growing proportion of melanomas diagnosed in the US.

Artificial intelligence in digital pathology of cutaneous lymphomas: a review of the current state and future perspectives

Primary cutaneous lymphomas (CLs) represent a heterogeneous group of T-cell lymphomas and B-cell lymphomas that present in the skin without evidence of extracutaneous involvement at time of diagnosis. CLs are largely distinct from their systemic counterparts in clinical presentation, histopathology, and biological behavior and, therefore, require different therapeutic management. Additional diagnostic burden is added by the fact that several benign inflammatory dermatoses mimic CL subtypes, requiring clinicopathological correlation for definitive diagnosis. Due to the heterogeneity and rarity of CL, adjunct diagnostic tools are welcomed, especially by pathologists without expertise in this field or with limited access to a centralized specialist panel. The transition into digital pathology workflows enables artificial intelligence (AI)-based analysis of patients' whole-slide pathology images (WSIs). AI can be used to automate manual processes in histopathology but, more importantly, can be applied to complex diagnostic tasks, especially suitable for rare disease like CL. To date, AI-based applications for CL have been minimally explored in literature. However, in other skin cancers and systemic lymphomas, disciplines that are recognized here as the building blocks for CLs, several studies demonstrated promising results using AI for disease diagnosis and subclassification, cancer detection, specimen triaging, and outcome prediction. Additionally, AI allows discovery of novel biomarkers or may help to quantify established biomarkers. This review summarizes and blends applications of AI in pathology of skin cancer and lymphoma and proposes how these findings can be applied to diagnostics of CL.

Histologic Screening of Malignant Melanoma, Spitz, Dermal and Junctional Melanocytic Nevi Using a Deep Learning Model

OBJECTIVE

The integration of an artificial intelligence tool into pathologists' workflow may lead to a more accurate and timely diagnosis of melanocytic lesions, directly patient care. The objective of this study was to create and evaluate the performance of such a model in achieving clinical-grade diagnoses of Spitz nevi, dermal and junctional melanocytic nevi, and melanomas.

METHODS

We created a beginner-level training environment by teaching our algorithm to perform cytologic inferences on 136,216 manually annotated tiles of hematoxylin and eosin-stained slides consisting of unequivocal melanocytic nevi, Spitz nevi, and invasive melanoma cases. We sequentially trained and tested our network to provide a final diagnosis-classification on 39 cases in total. Positive predictive value (precision) and sensitivity (recall) were used to measure our performance.

RESULTS

The tile-classification algorithm predicted the 136,216 irrelevant, melanoma, melanocytic nevi, and Spitz nevi tiles at sensitivities of 96%, 93%, 94% and 73%, respectively. The final trained model was able to correctly classify and predict the correct diagnosis in 85.7% of unseen cases (n = 28), reporting at or near screening-level performances for precision and recall of melanoma (76.2%, 100.0%), melanocytic nevi (100.0%, 75.0%), and Spitz nevi (100.0%, 75.0%).

CONCLUSIONS

Our pilot study proves that convolutional networks trained on cellular morphology to classify melanocytic proliferations can be used as a powerful tool to assist pathologists in screening for melanoma versus other benign lesions.

Quantitative Evaluation of Inflammatory Markers in Peri-Implantitis and Periodontitis Tissues: Digital vs. Manual Analysis—A Proof of Concept Study

Background and Objectives: In dentistry, the assessment of the histomorphometric features of periodontal (PD) and peri-implant (PI) lesions is important to evaluate their underlying pathogenic mechanism. The present study aimed to compare manual and digital methods of analysis in the evaluation of the inflammatory biomarkers in PI and PD lesions. Materials and Methods: PD and PI inflamed soft tissues were excised and processed for histological and immunohistochemical analyses for CD3+, CD4+, CD8+, CD15+, CD20+, CD68+, and CD138+. The obtained slides were acquired using a digital scanner. For each marker, 4 pictures per sample were extracted and the area fraction of the stained tissue was computed both manually using a 594-point counting grid (MC) and digitally using a dedicated image analysis software (DC). To assess the concordance between MC and DC, two blinded observers analysed a total of 200 pictures either with good quality of staining or with non-specific background noise. The inter and intraobserver concordance was evaluated using the intraclass coefficient and the agreement between MC and DC was assessed using the Bland–Altman plot. The time spent analysing each picture using the two methodologies by both observers was recorded. Further, the amount of each marker was compared between PI and PD with both methodologies. Results: The inter- and intraobserver concordance was excellent, except for images with background noise analysed using DC. MC and DC showed a satisfying concordance. DC was performed in half the time compared to MC. The morphological analysis showed a larger inflammatory infiltrate in PI than PD lesions. The comparison between PI and PD showed differences for CD68+ and CD138+ expression. Conclusions: DC could be used as a reliable and time-saving procedure for the immunohistochemical analysis of PD and PI soft tissues. When non-specific background noise is present, the experience of the pathologist may be still required.

An analysis of pathologists' viewing processes as they diagnose whole slide digital images

Although pathologists have their own viewing habits while diagnosing, viewing behaviors leading to the most accurate diagnoses are under-investigated. Digital whole slide imaging has enabled investigators to analyze pathologists' visual interpretation of histopathological features using mouse and viewport tracking techniques. In this study, we provide definitions for basic viewing behavior variables and investigate the association of pathologists' characteristics and viewing behaviors, and how they relate to diagnostic accuracy when interpreting whole slide images. We use recordings of 32 pathologists' actions while interpreting a set of 36 digital whole slide skin biopsy images (5 sets of 36 cases; 180 cases total). These viewport tracking data include the coordinates of a viewport scene on pathologists' screens, the magnification level at which that viewport was viewed, as well as a timestamp. We define a set of variables to quantify pathologists' viewing behaviors such as zooming, panning, and interacting with a consensus reference panel's selected region of interest (ROI). We examine the association of these viewing behaviors with pathologists' demographics, clinical characteristics, and diagnostic accuracy using cross-classified multilevel models. Viewing behaviors differ based on clinical experience of the pathologists. Pathologists with a higher caseload of melanocytic skin biopsy cases and pathologists with board certification and/or fellowship training in dermatopathology have lower average zoom and lower variance of zoom levels. Viewing behaviors associated with higher diagnostic accuracy include higher average and variance of zoom levels, a lower magnification percentage (a measure of consecutive zooming behavior), higher total interpretation time, and higher amount of time spent viewing ROIs. Scanning behavior, which refers to panning with a fixed zoom level, has marginally significant positive association with accuracy. Pathologists' training, clinical experience, and their exposure to a range of cases are associated with their viewing behaviors, which may contribute to their diagnostic accuracy. Research in computational pathology integrating digital imaging and clinical informatics opens up new avenues for leveraging viewing behaviors in medical education and training, potentially improving patient care and the effectiveness of clinical workflow.

Assessment of a Diagnostic Classification System for Management of Lesions to Exclude Melanoma

Importance

The proposed MOLEM (Management of Lesion to Exclude Melanoma) schema is more clinically relevant than Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MATH-Dx) for the management classification of melanocytic and nonmelanocytic lesions excised to exclude melanoma. A more standardized way of establishing diagnostic criteria will be crucial in the training of artificial intelligence (AI) algorithms.

Objective

To examine pathologists' variability, reliability, and confidence in reporting melanocytic and nonmelanocytic lesions excised to exclude melanoma using the MOLEM schema in a population of higher-risk patients.

Design, Setting, and Participants

This cohort study enrolled higher-risk patients referred to a primary care skin clinic in New South Wales, Australia, between April 2019 and December 2019. Baseline demographic characteristics including age, sex, and related clinical details (eg, history of melanoma) were collected. Patients with lesions suspicious for melanoma assessed by a primary care physician underwent clinical evaluation, dermoscopy imaging, and subsequent excision biopsy of the suspected lesion(s). A total of 217 lesions removed and prepared by conventional histologic method and stained with hematoxylin-eosin were reviewed by up to 9 independent pathologists for diagnosis using the MOLEM reporting schema. Pathologists evaluating for MOLEM schema were masked to the original histopathologic diagnosis.

Main Outcomes and Measures

Characteristics of the lesions were described and the concordance of cases per MOLEM class was assessed. Interrater agreement and the agreement between pathologists' ratings and the majority MOLEM diagnosis were calculated by Gwet AC1 with quadratic weighting applied. The diagnostic confidence of pathologists was then assessed.

Results

A total of 197 patients were included in the study (102 [51.8%] male; 95 [48.2%] female); mean (SD) age was 64.2 (15.8) years (range, 24-93 years). Overall, 217 index lesions were assessed with a total of 1516 histological diagnoses. Of 1516 diagnoses, 677 (44.7%) were classified as MOLEM class I; 120 (7.9%) as MOLEM class II; 564 (37.2%) as MOLEM class III; 114 (7.5%) as MOLEM class IV; and 55 (3.6%) as MOLEM class V. Concordance rates per MOLEM class were 88.6% (class I), 50.8% (class II), 76.2% (class III), 77.2% (class IV), and 74.2% (class V). The quadratic weighted interrater agreement was 91.3%, with a Gwet AC1 coefficient of 0.76 (95% CI, 0.72-0.81). The quadratic weighted agreement between pathologists' ratings and majority MOLEM was 94.7%, with a Gwet AC1 coefficient of 0.86 (95% CI, 0.84-0.88). The confidence in diagnosis data showed a relatively high level of confidence (between 1.0 and 1.5) when diagnosing classes I (mean [SD], 1.3 [0.3]), IV (1.3 [0.3]) and V (1.1 [0.1]); while classes II (1.8 [0.2]) and III (1.5 [0.4]) were diagnosed with a lower level of pathologist confidence (≥1.5). The quadratic weighted interrater confidence rating agreement was 95.2%, with a Gwet AC1 coefficient of 0.92 (95% CI, 0.90-0.94) for the 1314 confidence ratings collected. The confidence agreement for each MOLEM class was 95.0% (class I), 93.5% (class II), 95.3% (class III), 96.5% (class IV), and 97.5% (class V).

Conclusions and Relevance

The proposed MOLEM schema better reflects clinical practice than the MPATH-Dx schema in lesions excised to exclude melanoma by combining diagnoses with similar prognostic outcomes for melanocytic and nonmelanocytic lesions into standardized classification categories. Pathologists' level of confidence appeared to follow the MOLEM schema diagnostic concordance trend, ie, atypical naevi and melanoma in situ diagnoses were the least agreed upon and the most challenging for pathologists to confidently diagnose.

Rethinking Low-Risk Papillary Thyroid Cancers < 1cm (Papillary Microcarcinomas): An Evidence Review for Recalibrating Diagnostic Thresholds and/or Alternative Labels

Background: Recalibrating diagnostic thresholds or using alternative labels may mitigate overdiagnosis and overtreatment of papillary microcarcinoma (mPTC). We aimed at identifying and collating relevant epidemiological evidence on mPTC, to assess the case for recalibration and/or new labels. Methods: We searched EMBASE and PubMed databases from inception to December 2020 for natural history, autopsy, diagnostic drift, and diagnostic reproducibility studies. Where a relevant systematic review was pre-identified, only new articles were additionally included. Non-English articles were excluded. One author screened titles and abstracts. Two authors screened full text articles, performed quality assessments, and extracted data. We undertook narrative synthesis of included evidence (pooled estimates from systematic reviews and single estimates from primary studies). Results: One systematic review of patients undergoing active surveillance found that after 5 years of follow-up, 5.3% (95% confidence interval [CI 4.4-6.4%]) of the mPTC lesions had increased in size by ≥3 mm, and 1.6% [CI 1.1-2.4%] of patients had lymph node metastases. Among 7 new primary studies (including 3 updates on 2 studies included in the systematic review), 1-5% of patients undergoing active surveillance had lymph node metastases after a median follow-up of 1-10 years. One systematic review found that subclinical thyroid cancer incidentally discovered at autopsy is relatively common, with a pooled prevalence of 11.2% [CI 6.7-16.1%] among studies that examined the whole thyroid. Four diagnostic drift studies evaluated the new classification of non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Three studies of cases previously diagnosed as papillary thyroid cancer found 1.3-2.3% were reclassified as NIFTP (reclassifications were from follicular variation of papillary thyroid cancer [FVPTC]). One study of 48 cases previously diagnosed as mPTC found that 23.5% were reclassified as NIFTP. Thirteen reproducibility studies of papillary thyroid lesions found substantial variation in the histopathological diagnosis of thyroid lesions, including FVPTC and NIFTP classifications (no study evaluated mPTC). Conclusions: This review supports consideration of recalibrating diagnostic thresholds and/or alternative labels for low-risk mPTC.

Multiplexed Immunohistochemistry and Digital Pathology as the Foundation for Next-Generation Pathology in Melanoma: Methodological Comparison and Future Clinical Applications

The state-of-the-art for melanoma treatment has recently witnessed an enormous revolution, evolving from a chemotherapeutic, "one-drug-for-all" approach, to a tailored molecular- and immunological-based approach with the potential to make personalized therapy a reality. Nevertheless, methods still have to improve a lot before these can reliably characterize all the tumoral features that make each patient unique. While the clinical introduction of next-generation sequencing has made it possible to match mutational profiles to specific targeted therapies, improving response rates to immunotherapy will similarly require a deep understanding of the immune microenvironment and the specific contribution of each component in a patient-specific way. Recent advancements in artificial intelligence and single-cell profiling of resected tumor samples are paving the way for this challenging task. In this review, we provide an overview of the state-of-the-art in artificial intelligence and multiplexed immunohistochemistry in pathology, and how these bear the potential to improve diagnostics and therapy matching in melanoma. A major asset of in-situ single-cell profiling methods is that these preserve the spatial distribution of the cells in the tissue, allowing researchers to not only determine the cellular composition of the tumoral microenvironment, but also study tissue sociology, making inferences about specific cell-cell interactions and visualizing distinctive cellular architectures - all features that have an impact on anti-tumoral response rates. Despite the many advantages, the introduction of these approaches requires the digitization of tissue slides and the development of standardized analysis pipelines which pose substantial challenges that need to be addressed before these can enter clinical routine.

The future of pathology is digital

Information, archives, and intelligent artificial systems are part of everyday life in modern medicine. They already support medical staff by mapping their workflows with shared availability of cases' referral information, as needed for example, by the pathologist, and this support will be increased in the future even more. In radiology, established standards define information models, data transmission mechanisms, and workflows. Other disciplines, such as pathology, cardiology, and radiation therapy, now define further demands in addition to these established standards. Pathology may have the highest technical demands on the systems, with very complex workflows, and the digitization of slides generating enormous amounts of data up to Gigabytes per biopsy. This requires enormous amounts of data to be generated per biopsy, up to the gigabyte range. Digital pathology allows a change from classical histopathological diagnosis with microscopes and glass slides to virtual microscopy on the computer, with multiple tools using artificial intelligence and machine learning to support pathologists in their future work.

Artificial Intelligence in Dermatology: A Primer

Artificial intelligence is becoming increasingly important in dermatology, with studies reporting accuracy matching or exceeding dermatologists for the diagnosis of skin lesions from clinical and dermoscopic images. However, real-world clinical validation is currently lacking. We review dermatological applications of deep learning, the leading artificial intelligence technology for image analysis, and discuss its current capabilities, potential failure modes, and challenges surrounding performance assessment and interpretability. We address the following three primary applications: (i) teledermatology, including triage for referral to dermatologists; (ii) augmenting clinical assessment during face-to-face visits; and (iii) dermatopathology. We discuss equity and ethical issues related to future clinical adoption and recommend specific standardization of metrics for reporting model performance.

Assessment of Second-Opinion Strategies for Diagnoses of Cutaneous Melanocytic Lesions

Importance

Histopathologic criteria have limited diagnostic reliability for a range of cutaneous melanocytic lesions.

Objective

To evaluate the association of second-opinion strategies by general pathologists and dermatopathologists with the overall reliability of diagnosis of difficult melanocytic lesions.

Design, Setting, and Participants

This diagnostic study used samples from the Melanoma Pathology Study, which comprises 240 melanocytic lesion samples selected from a dermatopathology laboratory in Bellevue, Washington, and represents the full spectrum of lesions from common nevi to invasive melanoma. Five sets of 48 samples were evaluated independently by 187 US pathologists from July 15, 2013, through May 23, 2016. Data analysis was performed from April 2016 through November 2017.

Main Outcomes and Measures

Accuracy of diagnosis, defined as concordance with an expert consensus diagnosis of 3 experienced pathologists, was assessed after applying 10 different second-opinion strategies.

Results

Among the 187 US pathologists examining the 24 lesion samples, 113 were general pathologists (65 men [57.5%]; mean age at survey, 53.7 years [range, 33.0-79.0 years]) and 74 were dermatopathologists (49 men [66.2%]; mean age at survey, 46.4 years [range, 33.0-77.0 years]). Among the 8976 initial case interpretations, physicians desired second opinions for 3899 (43.4%), most often for interpretation of severely dysplastic nevi. The overall misclassification rate was highest when interpretations did not include second opinions and initial reviewers were all general pathologists lacking subspecialty training (52.8%; 95% CI, 51.3%-54.3%). When considering different second opinion strategies, the misclassification of melanocytic lesions was lowest when the first, second, and third consulting reviewers were subspecialty-trained dermatopathologists and when all lesions were subject to second opinions (36.7%; 95% CI, 33.1%-40.7%). When the second opinion strategies were compared with single interpretations without second opinions, the reductions in misclassification rates for some of the strategies were statistically significant, but none of the strategies eliminated diagnostic misclassification. Melanocytic lesions in the middle of the diagnostic spectrum had the highest misclassification rates (eg, moderately or severely dysplastic nevus, Spitz nevus, melanoma in situ, and pathologic stage [p]T1a invasive melanoma). Variability of in situ and thin invasive melanoma was relatively intractable to all examined strategies.

Conclusions and Relevance

The results of this study suggest that second opinions rendered by dermatopathologists improve reliability of melanocytic lesion diagnosis. However, discordance among pathologists remained high.

Melanoma of the eyelid and periocular skin: Histopathologic classification and molecular pathology

Cutaneous melanoma, a potentially lethal malignancy of the periocular skin, represents only a small proportion of the roughly 87,000 new cases of cutaneous melanoma diagnosed annually in the United States. Most of our understanding of melanoma of the eyelid skin is extrapolated from studies of cutaneous melanoma located elsewhere. Recent years have witnessed major breakthroughs in molecular biology and genomics of cutaneous melanoma, some of which have led to the development of targeted therapies. The molecular insights have also kindled interest in rethinking how cutaneous melanomas are classified and assessed for risk. We provide a synopsis of the epidemiology, histopathologic classification, and clinical experience of eyelid melanoma since 1990 and then review major advances in the molecular biology of cutaneous melanoma, exploring how this impacts our understanding of classification and predicting risk.