Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteria for evaluation. In this study, we propose a deep learning model that automatically classifies the histologic patterns of lung adenocarcinoma on surgical resection slides. Our model uses a convolutional neural network to identify regions of neoplastic cells, then aggregates those classifications to infer predominant and minor histologic patterns for any given whole-slide image. We evaluated our model on an independent set of 143 whole-slide images. It achieved a kappa score of 0.525 and an agreement of 66.6% with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0.485 and agreement of 62.7% on this test set. All evaluation metrics for our model and the three pathologists were within 95% confidence intervals of agreement. If confirmed in clinical practice, our model can assist pathologists in improving classification of lung adenocarcinoma patterns by automatically pre-screening and highlighting cancerous regions prior to review. Our approach can be generalized to any whole-slide image classification task, and code is made publicly available at https://github.com/BMIRDS/deepslide .

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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.

This case describes the first reported concurrent DIIHA and DIIT due to TMP-SMX-induced antibodies in an HSCT patient. DIIHA and DIIT can present a diagnostic challenge in the setting of intermittent medication dosing.