Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides

Importance: Histologic classification of colorectal polyps plays a critical role in screening for colorectal cancer and care of affected patients. An accurate and automated algorithm for the classification of colorectal polyps on digitized histopathologic slides could benefit practitioners and patients. Objective: To evaluate the performance and generalizability of a deep neural network for colorectal polyp classification on histopathologic slide images using a multi-institutional data set. Design, Setting, and Participants: This prognostic study used histopathologic slides collected from January 1, 2016, to June 31, 2016, from Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, with 326 slides used for training, 157 slides for an internal data set, and 25 for a validation set. For the external data set, 238 slides for 179 distinct patients were obtained from 24 institutions across 13 US states. Data analysis was performed from April 9 to November 23, 2019. Main Outcomes and Measures: Accuracy, sensitivity, and specificity of the model to classify 4 major colorectal polyp types: tubular adenoma, tubulovillous or villous adenoma, hyperplastic polyp, and sessile serrated adenoma. Performance was compared with that of local pathologists' at the point of care identified from corresponding pathology laboratories. Results: For the internal evaluation on the 157 slides with ground truth labels from 5 pathologists, the deep neural network had a mean accuracy of 93.5% (95% CI, 89.6%-97.4%) compared with local pathologists' accuracy of 91.4% (95% CI, 87.0%-95.8%). On the external test set of 238 slides with ground truth labels from 5 pathologists, the deep neural network achieved an accuracy of 87.0% (95% CI, 82.7%-91.3%), which was comparable with local pathologists' accuracy of 86.6% (95% CI, 82.3%-90.9%). Conclusions and Relevance: The findings suggest that this model may assist pathologists by improving the diagnostic efficiency, reproducibility, and accuracy of colorectal cancer screenings

Hepatic small vessel neoplasm, a rare infiltrative vascular neoplasm of uncertain malignant potential.

Characteristic but rare vascular neoplasms in the adult liver composed of small vessels with an infiltrative border were collected from an international group of collaborators over a 5-year period (N=17). These tumors were termed hepatic small vessel neoplasm (HSVN), and the histologic differential diagnosis was angiosarcoma (AS). The average age of patients was 54years (range, 24-83years). HSVN was more common in men. The average size was 2.1cm (range, 0.2-5.5cm). Diagnosis was aided by immunohistochemical stains for vascular lineage (CD31, CD34, FLI-1), which were uniformly positive in HSVN. Immunohistochemical stains (p53, c-Myc, GLUT-1, and Ki-67) for possible malignant potential are suggestive of a benign/low-grade tumor. Capture-based next-generation sequencing (using an assay that targets the coding regions of more than 500 cancer genes) identified an activating hotspot GNAQ mutation in 2 of 3 (67%) tested samples, and one of these cases also had a hotspot mutation in PIK3CA. When compared with hepatic AS (n=10) and cavernous hemangioma (n=6), the Ki-67 proliferative index is the most helpful tool in excluding AS, which demonstrated a tumor cell proliferative index greater than 10% in all cases. Strong p53 and diffuse c-Myc staining was also significantly associated with AS but not with HSVN or cavernous hemangioma. There have been no cases with rupture/hemorrhage, disseminated intravascular coagulation, or Kasabach-Merritt syndrome. Thus far, there has been no metastasis or recurrence of HSVN, but complete resection and close clinical follow-up are recommended because the outcome remains unknown