Ovarian strumal carcinoid containing appendiceal-type mucinous tumor patterns presenting as pseudomyxoma peritonei.

We report for the first time a case of ovarian strumal carcinoid containing both trabecular carcinoid and mucinous glands lined by both goblet and neuroendocrine cells and a low-grade mucinous neoplasm that presented clinically as pseudomyxoma peritonei in the absence of appendiceal lesion in a 58-yr-old woman. Histologically, there were both a tall columnar cell epithelial component lacking neuroendocrine cells, showing the scalloped contours and subepithelial clefts of low-grade appendiceal-type neoplasms and a mixed goblet cell neuroendocrine element. Characteristically, both reproduced appendiceal neoplastic phenotypes in a teratoid fashion. In addition, we present previously unreported oncocytic and mucinous changes in the thyroideal components of strumal carcinoid. This case represents a rare instance of pseudomyxoma peritonei of primary ovarian origin and is an example of multiple somatic teratoid endodermal differentiations of the different sections of the embryonal gut: foregut represented by thyroid, midgut by both mucinous appendiceal components, and hindgut by trabecular carcinoid.

Papillary ependymoma of the endometrium.

AIMS: To report an exceptional case of papillary ependymoma occurring in the endometrium. METHODS AND RESULTS: A clinicopathological study was performed regarding a case of papillary ependymoma occurring in the endometrial cavity of a 61-year-old patient who had presented with a solid-type, stage III anaplastic ependymoma of the ovary, treated with cytoreductive surgery that included total abdominal hysterectomy. The uterus was enlarged and showed a dilated cavity, with broadly implanted papillary excrescences without myometrial invasion that were covered by tall, cylindrical cells. These cells had glial fibrillary acidic protein-expressing cytoplasm that was also positive for cytokeratins 7, 8, 18, and 34ß-E12, epithelial membrane antigen, S100 protein, vimentin, and oestrogen and progesterone receptors. CONCLUSIONS: Pathogenetically, the presence of this uterine ependymoma could represent either an example of multicentricity or a phenomenon of transtubal implantation of the ovarian tumour. Exceptionally, papillary ependymoma can occur in the endometrium, and may prompt differential diagnoses with other papillary endometrial tumours. Pathologists should consider this rare possibility in the differential diagnosis of papillary ovarian and endometrial lesions.

A foundation model for clinical-grade computational pathology and rare cancers detection.

The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. The success of such applications depends on the ability to model the diverse patterns observed in pathology images. To this end, we present Virchow, the largest foundation model for computational pathology to date. In addition to the evaluation of biomarker prediction and cell identification, we demonstrate that a large foundation model enables pan-cancer detection, achieving 0.95 specimen-level area under the (receiver operating characteristic) curve across nine common and seven rare cancers. Furthermore, we show that with less training data, the pan-cancer detector built on Virchow can achieve similar performance to tissue-specific clinical-grade models in production and outperform them on some rare variants of cancer. Virchow's performance gains highlight the value of a foundation model and open possibilities for many high-impact applications with limited amounts of labeled training data.

Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection.

CONTEXT.­: Prostate cancer diagnosis rests on accurate assessment of tissue by a pathologist. The application of artificial intelligence (AI) to digitized whole slide images (WSIs) can aid pathologists in cancer diagnosis, but robust, diverse evidence in a simulated clinical setting is lacking. OBJECTIVE.­: To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance. DESIGN.­: Eighteen pathologists, 2 of whom were genitourinary subspecialists, evaluated 610 prostate needle core biopsy WSIs prepared at 218 institutions, with the option for deferral. Two evaluations were performed sequentially for each WSI: initially without assistance, and immediately thereafter aided by Paige Prostate (PaPr), a deep learning-based system that provides a WSI-level binary classification of suspicious for cancer or benign and pinpoints the location that has the greatest probability of harboring cancer on suspicious WSIs. Pathologists' changes in sensitivity and specificity between the assisted and unassisted modalities were assessed, together with the impact of PaPr output on the assisted reads. RESULTS.­: Using PaPr, pathologists improved their sensitivity and specificity across all histologic grades and tumor sizes. Accuracy gains on both benign and cancerous WSIs could be attributed to PaPr, which correctly classified 100% of the WSIs showing corrected diagnoses in the PaPr-assisted phase. CONCLUSIONS.­: This study demonstrates the effectiveness and safety of an AI tool for pathologists in simulated diagnostic practice, bridging the gap between computational pathology research and its clinical application, and resulted in the first US Food and Drug Administration authorization of an AI system in pathology.