Cytologic features of upper gynecologic tract adenocarcinomas exhibiting mesonephric-like differentiation.

BACKGROUND: Mesonephric adenocarcinomas are rare neoplasms which most commonly arise in the lateral cervix and vagina. Tumors with similar morphologic, immunophenotypic, and molecular characteristics recently have been described in the uterine corpus and ovary. Herein, the authors sought to characterize the cytomorphologic features of adenocarcinomas exhibiting mesonephric-like differentiation arising in the upper gynecologic tract. METHODS: Institutional databases were queried retrospectively for tumors of the upper gynecologic tract described as a "tumor of Wolffian origin" or "with mesonephric features" between 2007 and 2017. All available cytologic material was reviewed. Cytomorphologic characteristics were evaluated by 3 pathologists. RESULTS: The current study cohort consisted of 8 cases taken from 7 patients. Primary sites included the ovary (3 cases); endometrium (4 cases); and pelvis, not otherwise specified (1 case). All cases demonstrated tight 3-dimensional clusters of overlapping cells. Additional architectural features included tubular (5 of 8 cases; 63%) and papillary (3 of 8 cases; 38%) formations. Cells were small with scant (7 of 8 cases; 88%) to moderate (1 of 8 cases; 12%) cytoplasm. Three of the 8 cases (38%) demonstrated extracellular hyaline globules. Nuclei were uniform in size (6 of 8 cases; 75%) or showed mild anisonucleosis (2 of 8 cases; 25%). Nuclear grooves and indentations were observed in all cases. Mitoses (5 of 8 cases; 63%) and apoptotic bodies (4 of 8 cases; 50%), when present, were rare. No necrosis was noted. CONCLUSIONS: Adenocarcinomas exhibiting mesonephric-like differentiation show a monotonous population of small cells with scant to moderate cytoplasm and abundant nuclear grooves arranged in tight, overlapping, 3-dimensional clusters. Occasionally, papillary or tubular architecture, as well as extracellular hyaline globules, may be seen. These features should prompt further testing (eg, immunohistochemistry) to confirm the diagnosis and to exclude potential mimics.

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.

The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we present a multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations. We evaluated this framework at scale on a dataset of 44,732 whole slide images from 15,187 patients without any form of data curation. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph nodes resulted in areas under the curve above 0.98 for all cancer types. Its clinical application would allow pathologists to exclude 65-75% of slides while retaining 100% sensitivity. Our results show that this system has the ability to train accurate classification models at unprecedented scale, laying the foundation for the deployment of computational decision support systems in clinical practice.