Artificial intelligence as a tool for diagnosis in digital pathology whole slide images: A systematic review.

Digital pathology had a recent growth, stimulated by the implementation of digital whole slide images (WSIs) in clinical practice, and the pathology field faces shortage of pathologists in the last few years. This scenario created fronts of research applying artificial intelligence (AI) to help pathologists. One of them is the automated diagnosis, helping in the clinical decision support, increasing efficiency and quality of diagnosis.

Cytologic features of upper gynecologic tract adenocarcinomas exhibiting mesonephric-like differentiation.

Background: Mesonephric adenocarcinomas are rare neoplasms which most commonly arise in the lateral cervix and vagina. Tumors with similar morphologic, immunophenotypic, and molecular characteristics recently have been described in the uterine corpus and ovary. Herein, the authors sought to characterize the cytomorphologic features of adenocarcinomas exhibiting mesonephric-like differentiation arising in the upper gynecologic tract.

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.

The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we present a multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations. We evaluated this framework at scale on a dataset of 44,732 whole slide images from 15,187 patients without any form of data curation.

Well differentiated grade 3 pancreatic neuroendocrine tumors compared with related neoplasms: A morphologic study.

Background: Pancreatic neuroendocrine neoplasms with a Ki-67 labeling index greater than 20% were reclassified in 2017 by the World Health Organization into well differentiated (WD) and poorly differentiated grade 3 neuroendocrine carcinoma (NEC). The authors describe the cytologic features of grade 3 WD pancreatic neuroendocrine neoplasms compared with grade 2 neoplasms and NEC.

Methods: Fine-needle aspirates from 65 pancreatic neuroendocrine neoplasms were reviewed, and their cytomorphologic features were compared across grade 2, WD grade 3, and PD small cell type (PD-S), large cell type (PD-L), and type not otherwise specified (PD-NOS) neoplasms.

Assessment of cytologic differentiation in high-grade pancreatic neuroendocrine neoplasms: A multi-institutional study.

Background: Well-differentiated (WD) and poorly differentiated (PD) pancreatic neuroendocrine neoplasms are biologically distinct entities with different therapies and prognoses. WD neoplasms with elevated proliferation (Ki-67 > 20%) have been shown to have an overlapping histology with PD neuroendocrine carcinomas. This study compared expert cytomorphologic assessments of differentiation in pancreatic neuroendocrine neoplasms in a multi-institutional study.

Biliary carcinomas: pathology and the role of DNA mismatch repair deficiency.

The pathology of biliary carcinomas is diverse with different gross and histological features in tumors arising in the different segments of the biliary system. Various epidemiological risk factors, varied genetic makeup, and tissue microenvironment are contributory factors. As biliary tumors have been shown to be a part of the Lynch syndrome tumor spectrum, it is plausible to speculate that DNA mismatch repair (MMR) deficiency plays a role in biliary tumors.