Intravascular Large B-Cell Lymphoma Within a Thyroid Nodule: A Diagnostic Pitfall.

Intravascular large B-cell lymphoma (IVLBCL) is a subtype of diffuse large B-cell lymphoma, where the neoplastic lymphoid proliferation resides predominantly within the lumens of blood vessels but with no or few circulating neoplastic cells in the peripheral circulation. Focal or subtle involvement in some cases can cause the diagnosis to be misinterpreted or even overlooked, delaying the initiation of appropriate treatment. Our report focuses on a 78-year-old woman with a progressively enlarging thyroid mass, verified by ultrasound. She underwent a hemithyroidectomy, and microscopic evaluation demonstrated nodular thyroid parenchyma with atypical large cells in an intravascular distribution pattern identified on high magnification. Thorough evaluation showed that the large intravascular cells were positive CD20, PAX-5, and Ki-67 by immunoperoxidase staining, which lead to the diagnosis of IVLBCL. This case emphasizes the subtle appearance of IVLBCL, which may be missed on low-power light microscopy, and the need for careful evaluation of thyroid resection specimens.

Deep Learning for Classification of Colorectal Polyps on Whole-slide Images.

CONTEXT: Histopathological characterization of colorectal polyps is critical for determining the risk of colorectal cancer and future rates of surveillance for patients. However, this characterization is a challenging task and suffers from significant inter- and intra-observer variability. AIMS: We built an automatic image analysis method that can accurately classify different types of colorectal polyps on whole-slide images to help pathologists with this characterization and diagnosis. SETTING AND DESIGN: Our method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks. SUBJECTS AND METHODS: Our method covers five common types of polyps (i.e., hyperplastic, sessile serrated, traditional serrated, tubular, and tubulovillous/villous) that are included in the US Multisociety Task Force guidelines for colorectal cancer risk assessment and surveillance. We developed multiple deep-learning approaches by leveraging a dataset of 2074 crop images, which were annotated by multiple domain expert pathologists as reference standards. STATISTICAL ANALYSIS: We evaluated our method on an independent test set of 239 whole-slide images and measured standard machine-learning evaluation metrics of accuracy, precision, recall, and F1 score and their 95% confidence intervals. RESULTS: Our evaluation shows that our method with residual network architecture achieves the best performance for classification of colorectal polyps on whole-slide images (overall accuracy: 93.0%, 95% confidence interval: 89.0%-95.9%). CONCLUSIONS: Our method can reduce the cognitive burden on pathologists and improve their efficacy in histopathological characterization of colorectal polyps and in subsequent risk assessment and follow-up recommendations.