Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm.

Background And Aims: Celiac disease with its endoscopic manifestation of villous atrophy (VA) is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of VA at routine EGD may improve diagnostic performance.

Methods: A dataset of 858 endoscopic images of 182 patients with VA and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet18 deep learning model to detect VA.

Fatal cases after Omicron BA.1 and BA.2 infection: Results of an autopsy study.

Objectives: Omicron lineages BA.1/2 are considered to cause mild clinical courses. Nevertheless, fatal cases after those infections are recognized but little is known about risk factors.

[Benign and malignant neoplasms of the adrenal glands from the perspective of pathology].

In the case of neoplasms of the adrenal glands that are radiologically and clinically unclear, the indications for surgical resection as well as the subsequent clarification of the entity and dignity on the surgical specimen are difficult. The diagnostics of adrenal neoplasms, in particular the clear distinction between an adenoma and a carcinoma are often tricky from the point of view of a pathologist. In the following, not only the problems of classification and the possibilities of diagnostics in pathology but also an overview of the most important differential diagnoses of other benign and malignant tumors of the adrenal cortex and medulla are presented.

ALK, NUT, and TRK Do Not Play Relevant Roles in Gastric Cancer-Results of an Immunohistochemical Study in a Large Series.

ALK, NUT, and TRK are rare molecular aberrations that are pathognomonic for specific rare tumors. In low frequencies, however, they are found in a wide range of other tumor entities. This study aimed to investigate the frequency, association with clinicopathological characteristics, and prognosis of the immunohistochemical expressions of ALK, NUT, and TRK in 477 adenocarcinomas of the stomach and gastroesophageal junction.

Stroma AReactive Invasion Front Areas (SARIFA)-A New Easily to Determine Biomarker in Colon Cancer-Results of a Retrospective Study.

Many studies have used histomorphological features to more precisely predict the prognosis of patients with colon cancer, focusing on tumor budding, poorly differentiated clusters, and the tumor-stroma ratio. Here, we introduce SARIFA: Stroma AReactive Invasion Front Area(s). We defined SARIFA as the direct contact between a tumor gland/tumor cell cluster (≥5 cells) and inconspicuous surrounding adipose tissue in the invasion front.

Semiautomatic analysis of tumor proportion in colon cancer: Lessons from a validation study.

The tumor stroma ratio (TSR) is a promising histopathologic prognostic biomarker, which could allow for more accurate risk stratification and improved patient management in colorectal cancer. The purpose of our research was to validate the results of a previous study, which had suggested that not only a low but also a high tumor proportion (TP) might be an independent risk factor for occurrence of distant metastasis and worse overall survival using a semiautomatic image analysis approach with the open-source software ImageJ. We investigated 253 pT3 and pT4 adenocarcinomas of no special type.

Deep Learning Prediction of Metastasis in Locally Advanced Colon Cancer Using Binary Histologic Tumor Images.

In this study, we developed the Binary ImaGe Colon Metastasis classifier (BIg-CoMet), a semi-guided approach for the stratification of colon cancer patients into two risk groups for the occurrence of distant metastasis, using an InceptionResNetV2-based deep learning model trained on binary images. We enrolled 291 colon cancer patients with pT3 and pT4 adenocarcinomas and converted one cytokeratin-stained representative tumor section per case into a binary image. Image augmentation and dropout layers were incorporated to avoid overfitting.