Renal cell carcinoma (RCC) is the most common renal cancer in adults. The histopathologic classification of RCC is essential for diagnosis, prognosis, and management of patients. Reorganization and classification of complex histologic patterns of RCC on biopsy and surgical resection slides under a microscope remains a heavily specialized, error-prone, and time-consuming task for pathologists. In this study, we developed a deep neural network model that can accurately classify digitized surgical resection slides and biopsy slides into five related classes: clear cell RCC, papillary RCC, chromophobe RCC, renal oncocytoma, and normal. In addition to the whole-slide classification pipeline, we visualized the identified indicative regions and features on slides for classification by reprocessing patch-level classification results to ensure the explainability of our diagnostic model. We evaluated our model on independent test sets of 78 surgical resection whole slides and 79 biopsy slides from our tertiary medical institution, and 917 surgical resection slides from The Cancer Genome Atlas (TCGA) database. The average area under the curve (AUC) of our classifier on the internal resection slides, internal biopsy slides, and external TCGA slides is 0.98 (95% confidence interval (CI): 0.97–1.00), 0.98 (95% CI: 0.96–1.00) and 0.97 (95% CI: 0.96–0.98), respectively. Our results suggest that the high generalizability of our approach across different data sources and specimen types. More importantly, our model has the potential to assist pathologists by (1) automatically pre-screening slides to reduce false-negative cases, (2) highlighting regions of importance on digitized slides to accelerate diagnosis, and (3) providing objective and accurate diagnosis as the second opinion.
Knowledge of the segmental anatomy and intersegmental biliary connections is an essential prerequisite to the effective management of patients with complex biliary strictures. Three dimensional (3D) imaging has the ability to demonstrate complex anatomical relationships that are difficult to appreciate on simple non-invasive two dimensional (2D) imaging. Our aim was to develop a technique for accurate, non-invasive 3D computed tomography (CT) cholangiography. Contiguous 4 mm CT sections were obtained through the liver during a dynamic bolus of 200 ml IV contrast. 3D surface reconstructions were then performed, the biliary system was isolated from surrounding hepatic parenchyma using segmentation and contrast threshold algorithms. 14 patients (six females, eight males, median age 68 years (range 48-82)) were studied. 13/14 had malignant biliary obstruction and one had obstruction secondary to a pancreatic pseudocyst. Obstruction was at the liver hilum in eight, the common bile duct in five and the common hepatic duct in one. Four patients had biliary endoprostheses but were symptomatic from inadequate drainage. There was good demonstration of the biliary anatomy, obstructed segments and intersegmental biliary connections in 13/14; irregular biliary dilatation secondary to primary sclerosing cholangitis rendered interpretation difficult in one. 3D cholangiography provided a useful adjunct to other imaging techniques. In particular, in patients with complex hilar strictures it aided implementation of appropriate interventional drainage procedures.
The following fictional case is intended as a learning tool within the Pathology Competencies for Medical Education (PCME), a set of national standards for teaching pathology. These are divided into three basic competencies: Disease Mechanisms and Processes, Organ System Pathology, and Diagnostic Medicine and Therapeutic Pathology. For additional information, and a full list of learning objectives for all three competencies, see http://journals.sagepub.com/doi/10.1177/2374289517715040.1
Primary sarcomas of the lung are extremely uncommon. A diverse group of round cell sarcomas has been reported to originate in this location, including Ewing sarcoma, desmoplastic small round cell tumor, rhabdomyosarcoma, and poorly differentiated synovial sarcoma. The rarity of these tumors presents a potential pitfall; without careful study, they may easily be misidentified as the significantly more common poorly differentiated carcinoma. While histomorphology is a key aspect of correctly identifying a sarcoma, ancillary testing has become increasingly important in making a definitive diagnosis, as more and more recurrent genetic alterations are discovered and new entities are defined. We present three cases of primary round cell sarcomas of the lung that proved diagnostically challenging, describe the features and ancillary testing that led to the correct diagnoses, and discuss classic and evolving entities among sarcomas with round cell morphology.
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