Benchmarking Deep Learning-Based Image Retrieval of Oral Tumor Histology.

Introduction:  Oral tumors necessitate a dependable computer-assisted pathological diagnosis system considering their rarity and diversity. A content-based image retrieval (CBIR) system using deep neural networks has been successfully devised for digital pathology. No CBIR system for oral pathology has been investigated because of the lack of an extensive image database and feature extractors tailored to oral pathology.

Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists.

Numerous cancer histopathology specimens have been collected and digitized over the past few decades. A comprehensive evaluation of the distribution of various cells in tumor tissue sections can provide valuable information for understanding cancer. Deep learning is suitable for achieving these goals; however, the collection of extensive, unbiased training data is hindered, thus limiting the production of accurate segmentation models.

Functional genomics screening identifies aspartyl-tRNA synthetase as a novel prognostic marker and a therapeutic target for gastric cancers.

Efficient molecular targeting therapies for most gastric cancers (GCs) are currently lacking, despite GC being one of the most frequent and often devastating malignancies worldwide. Thus, identification of novel therapeutic targets for GC is in high demand. Recent advancements of high-throughput nucleic acid synthesis methods combined with next-generation sequencing (NGS) platforms have made it feasible to conduct functional genomics screening using large-scale pooled lentiviral libraries aimed at discovering novel cancer therapeutic targets.

Universal encoding of pan-cancer histology by deep texture representations.

Cancer histological images contain rich biological and clinical information, but quantitative representation can be problematic and has prevented the direct comparison and accumulation of large-scale datasets. Here, we show successful universal encoding of cancer histology by deep texture representations (DTRs) produced by a bilinear convolutional neural network. DTR-based, unsupervised histological profiling, which captures the morphological diversity, is applied to cancer biopsies and reveals relationships between histologic characteristics and the response to immune checkpoint inhibitors (ICIs).

Rectal cancer developing from an anastomotic site 18 years after resection due to intussusception caused by Peutz-Jeghers polyposis in a 31-year-old man: a case report.

Background: Peutz-Jeghers syndrome (PJS) is an autosomal dominant disorder characterized by hamartomatous polyposis of the gastrointestinal tract. It is associated with a high risk of malignancy in the gastrointestinal tract, as well as in other organs. We report a case of colon cancer at the anastomotic site that occurred 18 years after high anterior resection of the rectum for intussusception caused by Peutz-Jeghers polyposis.

The Significant Antitumor Activity of Nivolumab in Lung Adenocarcinoma with Choriocarcinomatous Features.

We report the case of a 60-year-old Japanese man with a metastatic brain tumor that caused ataxia. As a consequence of resection of a cerebellar tumor, the tumor was diagnosed as a poorly differentiated adenocarcinoma with choriocarcinomatous features. The patient underwent bronchoscopy, leading to a diagnosis of the same histology as the brain tumor.