Spectrum of malignant skin adnexal tumors – a single institution study of 17 cases with clinicopathological correlation

Background: Skin adnexal tumors are a rare, assorted group of tumors with differentiation towards hair follicle, sebaceous glands or sweat glands. A vast majority of them are benign. But for every benign adnexal tumor, a malignant counterpart exists. Many histological subtypes of these malignant tumors been described, but only in short series or individual case reports. So, not much is known about their incidence or prognosis simply because of the limited number of cases available for analysis. This study was undertaken to contribute towards this less traversed area of dermatopathology. Methods: In the present study, a total of 60 cases with a histopathological diagnosis of skin adnexal tumors were studied. The slides and blocks were retrieved from the archives and were reviewed and were reclassified and subtyped as per WHO classification of skin tumors, 2006. Results: Among the 60 cases of adnexal tumors documented and reviewed over the four year study period, 17 cases of malignant adnexal tumors were encountered. Of these, 10 (58%) were tumors with eccrine or apocrine differentiation, 5 (29%) were of follicular differentiation and two (12%) were of sebaceous differentiation. Mammary paget disease (MPD) was the most frequent malignant tumor encountered both overall and among the tumors with eccrine and apocrine differentiation. Other tumors encountered in their order of frequency were Malignant proliferating trichelemmal tumor, apocrine carcinoma, sebaceous carcinoma and extramammary paget disease, trichelemmal carcinoma and eccrine carcinoma. These tumors were evaluated with regard to their age, site, gender distribution, clinical characters and histopathological features. Conclusion: Malignant adnexal tumors are extremely rare with indistinct clinical characteristics. They are locally aggressive, and have the potential for nodal involvement and distant metastasis, with a poor clinical outcome. A high index of suspicion is necessary to establish a diagnosis in most cases.

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing