Immunohistochemistry for Diagnosing Melanoma in Older Adults.

Importance: Pathologic assessment to diagnose skin biopsies, especially for cutaneous melanoma, can be challenging, and immunohistochemistry (IHC) staining has the potential to aid decision-making. Currently, the temporal trends regarding the use of IHC for the examination of skin biopsies on a national level have not been described. Objective: To illustrate trends in the use of IHC for the examination of skin biopsies in melanoma diagnoses. Design, Setting, and Participants: A retrospective cross-sectional study was conducted to examine incident cases of melanoma diagnosed between January 2000 and December 2017. The analysis used the SEER-Medicare linked database, incorporating data from 17 population-based registries. The study focused on incident cases of in situ or malignant melanoma of the skin diagnosed in patients 65 years or older. Data were analyzed between August 2022 and November 2023. Main Outcomes and Measures: The main outcomes encompassed the identification of claims for IHC within the month of melanoma diagnoses and extending up to 14 days into the month following diagnosis. The SEER data on patients with melanoma comprised demographic, tumor, and area-level characteristics. Results: The final sample comprised 132 547 melanoma tumors in 116 117 distinct patients. Of the 132 547 melanoma diagnoses meeting inclusion criteria from 2000 to 2017, 43 396 cases had accompanying IHC claims (33%). Among these cases, 28 298 (65%) were diagnosed in male patients, 19 019 (44%) were diagnosed in patients aged 65 years to 74 years, 16 444 (38%) in patients aged 75 years to 84 years, and 7933 (18%) in patients aged 85 years and older. In 2000, 11% of melanoma cases had claims for IHC at or near the time of diagnosis. This proportion increased yearly, with 51% of melanoma cases having associated IHC claims in 2017. Increasing IHC use is observed for all stages of melanoma, including in situ melanoma. Claims for IHC in melanomas increased in all 17 SEER registries but at different rates. In 2017, the use of IHC for melanoma diagnosis ranged from 39% to 68% across registries. Conclusions and Relevance: Considering the dramatically rising and variable use of IHC in diagnosing melanoma by pathologists demonstrated in this retrospective cross-sectional study, further investigation is warranted to understand the clinical utility and discern when IHC most improves diagnostic accuracy or helps patients.

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.

Implementing the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis: Long-term effect of a simple educational intervention.

Background: A standardized pathology management tool for melanocytic skin lesions may improve patient care by simplifying interpretation and categorization of the diverse terminology currently extant. Objective: To assess an online educational intervention that teaches dermatopathologists to use the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx), a schema collapsing multiple diagnostic terms into 5 classes ranging from benign to invasive melanoma. Methods: Practicing dermatopathologists (N = 149) from 40 US states participated in a 2-year educational intervention study (71% response rate). The intervention involved a brief tutorial followed by practice on 28 melanocytic lesions, with the goal of teaching pathologists how to correctly use the MPATH-Dx schema; competence using the MPATH-Dx tool 12-24 months postintervention was assessed. Participants' self-reported confidence using the MPATH-Dx tool was assessed preintervention and postintervention. Results: At preintervention, confidence using the MPATH-Dx tool was already high, despite 68% lacking prior familiarity with it, and confidence increased postintervention (P = .0003). During the intervention, participants used the MPATH-Dx tool correctly for 90% of their interpretations; postintervention, participants used the MPATH-Dx tool correctly for 88% of their interpretations. Limitations: Future research should examine implementing a standardized pathology assessment schema in actual clinical practice. Conclusion: Dermatopathologists can be taught to confidently and competently use the MPATH-Dx schema with a simple educational tutorial followed by practice.

VSGD-Net: Virtual Staining Guided Melanocyte Detection on Histopathological Images.

Detection of melanocytes serves as a critical prerequisite in assessing melanocytic growth patterns when diagnosing melanoma and its precursor lesions on skin biopsy specimens. However, this detection is challenging due to the visual similarity of melanocytes to other cells in routine Hematoxylin and Eosin (H&E) stained images, leading to the failure of current nuclei detection methods. Stains such as Sox10 can mark melanocytes, but they require an additional step and expense and thus are not regularly used in clinical practice. To address these limitations, we introduce VSGD-Net, a novel detection network that learns melanocyte identification through virtual staining from H&E to Sox10. The method takes only routine H&E images during inference, resulting in a promising approach to support pathologists in the diagnosis of melanoma. To the best of our knowledge, this is the first study that investigates the detection problem using image synthesis features between two distinct pathology stainings. Extensive experimental results show that our proposed model outperforms state-of-the-art nuclei detection methods for melanocyte detection. The source code and pre-trained model are available at: https://github.com/kechunl/VSGD-Net.

Diagnostic error, uncertainty, and overdiagnosis in melanoma.

Diagnostic error can be defined as deviation from a gold standard diagnosis, typically defined in terms of expert opinion, although sometimes in terms of unexpected events that might occur in follow-up (such as progression and death from disease). Although diagnostic error does exist for melanoma, deviations from gold standard diagnosis, certainly among appropriately trained and experienced practitioners, are likely to be the result of uncertainty and lack of specific criteria, and differences of opinion, rather than lack of diagnostic skills. In this review, the concept of diagnostic error will be considered in relation to diagnostic uncertainty, and the concept of overdiagnosis in melanoma will be presented and discussed.

Revision of the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis Classification Schema for Melanocytic Lesions: A Consensus Statement.

Importance: A 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. Objective: To 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 Review: Practicing 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. Findings: The 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 Relevance: The 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.

Prognostic modeling of cutaneous melanoma stage I patients using cancer registry data identifies subsets with very-low melanoma mortality.

BACKGROUND: Evidence exists that escalating melanoma incidence is due in part to overdiagnosis, the diagnosis of lesions that will not lead to symptoms or death. The authors aimed to characterize subsets of melanoma patients with very-low risk of death that may be contributing to overdiagnosis. METHODS: Melanoma patients diagnosed in 2010 and 2011 with stage I lesions ≤1.0 mm thick and negative clinical lymph nodes from the Surveillance, Epidemiology, and End Results database were selected. Classification and regression tree and logistic regression models were developed and validated to identify patients with very-low risk of death from melanoma within 7 years. Logistic models were also used to identify patients at higher risk of death among this group of stage I patients. RESULTS: Compared to an overall 7-year mortality from melanoma of 2.5% in these patients, a subset comprising 25% had a risk below 1%. Younger age at diagnosis and Clark level II were associated with low risk of death in all models. Breslow thickness below 0.4 mm, absence of mitogenicity, absence of ulceration, and female sex were also associated with lower mortality. A small subset of high-risk patients with >20% risk of death was also identified. CONCLUSION: Patients with very-low risk of dying from melanoma within 7 years of diagnosis were identified. Such cases warrant further study and consensus discussion to develop classification criteria, with the potential to be categorized using an alternative term such as "melanocytic neoplasms of low malignant potential." LAY SUMMARY: Although melanoma is the most serious skin cancer, most melanoma patients have high chances of survival. There is evidence that some lesions diagnosed as melanoma would never have caused symptoms or death, a phenomenon known as overdiagnosis. In this study, we used cancer registry data to identify a subset of early-stage melanoma patients with almost no melanoma deaths. Using two statistical approaches, we identified patients with <1% risk of dying from melanoma in 7 years. Such patients tended to be younger with minimal invasion into the skin. We also identified a very small patient subset with higher mortality risk.

Effect of Prior Diagnoses on Dermatopathologists' Interpretations of Melanocytic Lesions: A Randomized Controlled Trial.

Importance: Medical second opinions are common, although little is known about the best processes for obtaining them. This study assesses whether knowledge of a prior physician's diagnosis influences consulting physicians' diagnoses. Objective: To measure the extent to which dermatopathologists' diagnoses are influenced by prior diagnostic information from another dermatopathologist. Design, Setting, and Participants: Dermatopathologists were randomly assigned to interpret 1 slide set of 18 melanocytic skin biopsy specimens in 2 phases (5 slide sets totaling 90 cases). Phase 1 interpretations were conducted without prior diagnostic information. After a washout period of 12 or more months, dermatopathologists' phase 2 interpretations were conducted with their identical slide set; for a random subset of cases in phase 2, participants were shown prior diagnoses by other dermatopathologists that were either more or less severe than their own phase 1 diagnosis of the case. Using the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis tool, cases ranged from class I (benign) to class V (≥pT1b invasive melanoma). Data collection took place from August 2018 to March 2021, and data analysis was performed from March to December 2021. Intervention: Prior diagnoses were actual diagnoses from board-certified and/or fellowship-trained dermatopathologists. A prior diagnosis was always in a more severe or less severe diagnostic class than the participant's phase 1 interpretation; more or less severe was determined by the randomization scheme. In the control condition of no prior diagnostic information, the participants were told that a prior diagnosis was not available. Main Outcomes and Measures: When exposure was to a prior diagnosis in a higher diagnostic class, the primary study outcome was whether a participant's diagnosis in phase 2 was in a higher diagnostic class than the participant's diagnosis in phase 1. When exposure was to a prior diagnosis in a lower diagnostic class, the primary study outcome was whether a participant's diagnosis in phase 2 was in a lower diagnostic class than the participant's diagnosis in phase 1. The effect of prior diagnostic information was measured using the relative risk (RR) of each outcome relative to the control condition of no prior diagnostic information, adjusted for the diagnostic class of the phase 1 diagnosis. Prior to data collection, it was hypothesized that participants would be swayed in the direction of prior diagnostic information. Results: A total of 149 dermatopathologists (median [range] age, 47 years [34-76] years; 101 [68%] were male) provided 5322 interpretations of study cases. Participants were more likely to increase the severity of their diagnosis when the prior diagnosis was of greater severity compared with when no prior diagnosis was provided (RR, 1.52; 95% CI, 1.34-1.73); likewise, participants gave less severe diagnoses when prior diagnoses were of lesser severity (RR, 1.38; 95% CI, 1.19-1.59). Trends were similar among dermatopathologists who had previously stated they were "not at all influenced" by prior diagnoses. Prior diagnoses also swayed dermatopathologists away from correct diagnoses. Conclusions and Relevance: In this randomized controlled trial, despite the preference of most dermatopathologists to receive prior diagnoses when providing second opinions, this information swayed them away from a correct diagnosis to an incorrect diagnosis.

Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation.

Invasive melanoma, a common type of skin cancer, is considered one of the deadliest. Pathologists routinely evaluate melanocytic lesions to determine the amount of atypia, and if the lesion represents an invasive melanoma, its stage. However, due to the complicated nature of these assessments, inter- and intra-observer variability among pathologists in their interpretation are very common. Machine-learning techniques have shown impressive and robust performance on various tasks including healthcare. In this work, we study the potential of including semantic segmentation of clinically important tissue structure in improving the diagnosis of skin biopsy images. Our experimental results show a 6% improvement in F-score when using whole slide images along with epidermal nests and cancerous dermal nest segmentation masks compared to using whole-slide images alone in training and testing the diagnosis pipeline.

Segmenting Skin Biopsy Images with Coarse and Sparse Annotations using U-Net.

The number of melanoma diagnoses has increased dramatically over the past three decades, outpacing almost all other cancers. Nearly 1 in 4 skin biopsies is of melanocytic lesions, highlighting the clinical and public health importance of correct diagnosis. Deep learning image analysis methods may improve and complement current diagnostic and prognostic capabilities. The histologic evaluation of melanocytic lesions, including melanoma and its precursors, involves determining whether the melanocytic population involves the epidermis, dermis, or both. Semantic segmentation of clinically important structures in skin biopsies is a crucial step towards an accurate diagnosis. While training a segmentation model requires ground-truth labels, annotation of large images is a labor-intensive task. This issue becomes especially pronounced in a medical image dataset in which expert annotation is the gold standard. In this paper, we propose a two-stage segmentation pipeline using coarse and sparse annotations on a small region of the whole slide image as the training set. Segmentation results on whole slide images show promising performance for the proposed pipeline.

Dermatopathologist Perceptions of Overdiagnosis of Melanocytic Skin Lesions and Association With Diagnostic Behaviors.

Importance: Despite 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. Objective: To 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 Participants: This 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 Measures: Agreement 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. Results: Of 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 Relevance: In 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.

Characterization of multiple diagnostic terms in melanocytic skin lesion pathology reports.

BACKGROUND: Histopathologically ambiguous melanocytic lesions lead some pathologists to list multiple diagnostic considerations in the pathology report. The frequency and circumstance of multiple diagnostic considerations remain poorly characterized. METHODS: Two hundred and forty skin biopsy samples were interpreted by 187 pathologists (8976 independent diagnoses) and classified according to a diagnostic/treatment stratification (MPATH-Dx). RESULTS: Multiple diagnoses in different MPATH-Dx classes were used in n = 1320 (14.7%) interpretations, with 97% of pathologists and 91% of cases having at least one such interpretation. Multiple diagnoses were more common for intermediate risk lesions and are associated with greater subjective difficulty and lower confidence. We estimate that 6% of pathology reports for melanocytic lesions in the United States contain two diagnoses of different MPATH-Dx prognostic classes, and 2% of cases are given two diagnoses with significant treatment implications. CONCLUSIONS: Difficult melanocytic diagnoses in skin may necessitate multiple diagnostic considerations; however, as patients increasingly access their health records and retrieve pathology reports (as mandated by US law), uncertainty should be expressed unambiguously.

Association of Second-Opinion Strategies in the Histopathologic Diagnosis of Cutaneous Melanocytic Lesions With Diagnostic Accuracy and Population-Level Costs.

IMPORTANCE: Diagnostic variation among pathologists interpreting cutaneous melanocytic lesions could lead to suboptimal care. OBJECTIVE: To estimate the potential association of second-opinion strategies in the histopathologic diagnosis of cutaneous melanocytic lesions with diagnostic accuracy and 1-year population-level costs in the US. DESIGN, SETTING, AND PARTICIPANTS: Decision analysis with 1-year time horizon including melanocytic lesion diagnoses available from US pathologists participating in the Melanoma Pathology Study (M-Path) and from the study panel of reference pathologists who classified cases using the MPATH-Dx classification tool. M-Path data collection occurred from July 2013 through March 2015; analyses for the present study were performed between April 2015 and January 2021. EXPOSURES: Various second-opinion strategies for interpretation of melanocytic cutaneous lesions. MAIN OUTCOMES AND MEASURES: Estimated accuracy of pathologists' diagnoses, defined as concordance with the reference panel diagnoses, and 1-year postbiopsy medical costs under various second-opinion strategies. Expected percentage of concordant diagnoses, including percentages of overinterpretation and underinterpretation, and 1-year costs of medical care per 100 000 in the US population. RESULTS: Decision-analytic model parameters were based on diagnostic interpretations for 240 cases by 187 pathologists compared with reference panel diagnoses. Without second opinions, 83.2% of diagnoses in the US were estimated to be accurate-ie, concordant with the reference diagnosis; with overinterpretation (8.0%) or underinterpretation (8.8%), and 16 850 misclassified diagnoses per 100 000 biopsies. Accuracy increased under all second-opinion strategies. Accuracy (87.4% concordance with 3.6% overinterpretation and 9.1% underinterpretation) and cost (an increase of more than $10 million per 100 000 biopsies per year) were highest when second opinions were universal (eg, performed on all biopsies), relative to no second opinions. A selective second-opinion strategy based on pathologists' desire or institutional requirements for a second opinion was most accurate (86.5% concordance; 4.4% overinterpretation; 9.1% underinterpretation) and would reduce costs by more than $1.9 million per 100 000 skin biopsies relative to no second opinions. Improvements in diagnostic accuracy with all second-opinion strategies were associated with reductions in overinterpretation but not underinterpretation. CONCLUSIONS AND RELEVANCE: In this decision-analytic model, selective second-opinion strategies for interpretation of melanocytic skin lesions showed the potential to improve diagnostic accuracy and decrease costs relative to no second opinions or universal second opinions.

Histopathologic synoptic reporting of invasive melanoma: How reliable are the data?

BACKGROUND: Synoptic reporting is recommended by many guideline committees to encourage the thorough histologic documentation necessary for optimal management of patients with melanoma. METHODS: One hundred fifty-one pathologists from 40 US states interpreted 41 invasive melanoma cases. For each synoptic reporting factor, the authors identified cases with "complete agreement" (all participants recorded the same value) versus any disagreement. Pairwise agreement was calculated for each case as the proportion of pairs of responses that agreed, where paired responses were generated by the comparison of each reviewer's response with all others. RESULTS: There was complete agreement among all reviewers for 22 of the 41 cases (54%) on Breslow thickness dichotomized at 0.8 mm, with pairwise agreement ranging from 49% to 100% across the 41 cases. There was complete agreement for "no ulceration" in 24 of the 41 cases (59%), with pairwise agreement ranging from 42% to 100%. Tumor transected at base had complete agreement for 26 of the 41 cases (63%), with pairwise agreement ranging from 31% to 100%. Mitotic rate, categorized as 0/mm2 , 1/mm2 , or 2/mm2 , had complete agreement for 17 of the 41 cases (41%), with pairwise agreement ranging from 36% to 100%. Regression saw complete agreement for 14 of 41 cases (34%), with pairwise agreement ranging from 40% to 100%. Lymphovascular invasion, perineural invasion, and microscopic satellites were rarely reported as present. Respectively, these prognostic factors had complete agreement for 32 (78%), 37 (90%), and 18 (44%) of the 41 cases, and the ranges of pairwise agreement were 47% to 100%, 70% to 100%, and 53% to 100%, respectively. CONCLUSIONS: These findings alert pathologists and clinicians to the problem of interobserver variability in recording critical prognostic factors. LAY SUMMARY: This study addresses variability in the assessment and reporting of critical characteristics of invasive melanomas that are used by clinicians to guide patient care. The authors characterize the diagnostic variability among pathologists and their reporting methods in light of recently updated national guidelines. Results demonstrate considerable variability in the diagnostic reporting of melanoma with regard to the following: Breslow thickness, mitotic rate, ulceration, regression, and microscopic satellites. This work serves to alert pathologists and clinicians to the existence of variability in reporting these prognostic factors.

Scale-Aware Transformers for Diagnosing Melanocytic Lesions.

Diagnosing melanocytic lesions is one of the most challenging areas of pathology with extensive intra- and inter-observer variability. The gold standard for a diagnosis of invasive melanoma is the examination of histopathological whole slide skin biopsy images by an experienced dermatopathologist. Digitized whole slide images offer novel opportunities for computer programs to improve the diagnostic performance of pathologists. In order to automatically classify such images, representations that reflect the content and context of the input images are needed. In this paper, we introduce a novel self-attention-based network to learn representations from digital whole slide images of melanocytic skin lesions at multiple scales. Our model softly weighs representations from multiple scales, allowing it to discriminate between diagnosis-relevant and -irrelevant information automatically. Our experiments show that our method outperforms five other state-of-the-art whole slide image classification methods by a significant margin. Our method also achieves comparable performance to 187 practicing U.S. pathologists who interpreted the same cases in an independent study. To facilitate relevant research, full training and inference code is made publicly available at https://github.com/meredith-wenjunwu/ScATNet.

Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline.

In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification. Based on recent advancements in instance segmentation and the Mask RCNN model, our duct-level segmenter tries to identify each ductal individual inside a microscopic image; then, it extracts tissue-level information from the identified ductal instances. Leveraging three levels of information obtained from these ductal instances and also the histopathology image, the proposed DIOP outperforms previous approaches (both feature-based and CNN-based) in all diagnostic tasks; for the four-way classification task, the DIOP achieves comparable performance to general pathologists in this unique dataset. The proposed DIOP only takes a few seconds to run in the inference time, which could be used interactively on most modern computers. More clinical explorations are needed to study the robustness and generalizability of this system in the future.

Machine learning techniques for mitoses classification.

BACKGROUND: Pathologists analyze biopsy material at both the cellular and structural level to determine diagnosis and cancer stage. Mitotic figures are surrogate biomarkers of cellular proliferation that can provide prognostic information; thus, their precise detection is an important factor for clinical care. Convolutional Neural Networks (CNNs) have shown remarkable performance on several recognition tasks. Utilizing CNNs for mitosis classification may aid pathologists to improve the detection accuracy. METHODS: We studied two state-of-the-art CNN-based models, ESPNet and DenseNet, for mitosis classification on six whole slide images of skin biopsies and compared their quantitative performance in terms of sensitivity, specificity, and F-score. We used raw RGB images of mitosis and non-mitosis samples with their corresponding labels as training input. In order to compare with other work, we studied the performance of these classifiers and two other architectures, ResNet and ShuffleNet, on the publicly available MITOS breast biopsy dataset and compared the performance of all four in terms of precision, recall, and F-score (which are standard for this data set), architecture, training time and inference time. RESULTS: The ESPNet and DenseNet results on our primary melanoma dataset had a sensitivity of 0.976 and 0.968, and a specificity of 0.987 and 0.995, respectively, with F-scores of .968 and .976, respectively. On the MITOS dataset, ESPNet and DenseNet showed a sensitivity of 0.866 and 0.916, and a specificity of 0.973 and 0.980, respectively. The MITOS results using DenseNet had a precision of 0.939, recall of 0.916, and F-score of 0.927. The best published result on MITOS (Saha et al. 2018) reported precision of 0.92, recall of 0.88, and F-score of 0.90. In our architecture comparisons on MITOS, we found that DenseNet beats the others in terms of F-Score (DenseNet 0.927, ESPNet 0.890, ResNet 0.865, ShuffleNet 0.847) and especially Recall (DenseNet 0.916, ESPNet 0.866, ResNet 0.807, ShuffleNet 0.753), while ResNet and ESPNet have much faster inference times (ResNet 6 s, ESPNet 8 s, DenseNet 31 s). ResNet is faster than ESPNet, but ESPNet has a higher F-Score and Recall than ResNet, making it a good compromise solution. CONCLUSION: We studied several state-of-the-art CNNs for detecting mitotic figures in whole slide biopsy images. We evaluated two CNNs on a melanoma cancer dataset and then compared four CNNs on a public breast cancer data set, using the same methodology on both. Our methodology and architecture for mitosis finding in both melanoma and breast cancer whole slide images has been thoroughly tested and is likely to be useful for finding mitoses in any whole slide biopsy images.