Masked pre-training of transformers for histology image analysis.

In digital pathology, whole-slide images (WSIs) are widely used for applications such as cancer diagnosis and prognosis prediction. Vision transformer (ViT) models have recently emerged as a promising method for encoding large regions of WSIs while preserving spatial relationships among patches. However, due to the large number of model parameters and limited labeled data, applying transformer models to WSIs remains challenging.

Oligometastatic Melanoma Treated by Metastasectomy in Combination with Immune Checkpoint and BRAF Inhibitors: A Case Series.

BACKGROUND Early therapies for metastatic melanoma improved patient quality of life; however, median survival remained unaffected. Studies are showing that surgical excision with the combination of immune checkpoint inhibitor (ICI) therapy has better outcomes than systemic therapy alone. This single-center case series describes 7 patients with oligometastatic melanoma treated by metastasectomy in combination with ICI and BRAF inhibitors.

Large-scale validation study of an improved semiautonomous urine cytology assessment tool: AutoParis-X.

Background: Adopting a computational approach for the assessment of urine cytology specimens has the potential to improve the efficiency, accuracy, and reliability of bladder cancer screening, which has heretofore relied on semisubjective manual assessment methods. As rigorous, quantitative criteria and guidelines have been introduced for improving screening practices (e.g.

Pancreatic acinar metaplasia at the gastroesophageal junction is associated with protective effect against intestinal metaplasia in patients with gastroesophageal reflux disease.

Anecdotal evidence suggests that pancreatic acinar metaplasia (PAM) and intestinal metaplasia (IM) overlap infrequently at the gastroesophageal junction/distal esophagus (GEJ/DE). The goal of this study was to evaluate the significance of PAM at GEJ/DE in relation to IM in patients with gastroesophageal reflux disease (GERD). Group 1 comprised 230 consecutive patients with GEJ/DE biopsies (80.

Examining longitudinal markers of bladder cancer recurrence through a semiautonomous machine learning system for quantifying specimen atypia from urine cytology.

Background: Urine cytology is generally considered the primary approach for screening for recurrence of bladder cancer. However, it is currently unclear how best to use cytological examinations for assessment and early detection of recurrence, beyond identifying a positive finding that requires more invasive methods to confirm recurrence and decide on therapeutic options. Because screening programs are frequent, and can be burdensome, finding quantitative means to reduce this burden for patients, cytopathologists, and urologists is an important endeavor and can improve both the efficiency and reliability of findings.

MHAttnSurv: Multi-head attention for survival prediction using whole-slide pathology images.

Whole slide images (WSI) based survival prediction has attracted increasing interest in pathology. Despite this, extracting prognostic information from WSIs remains a challenging task due to their enormous size and the scarcity of pathologist annotations. Previous studies have utilized multiple instance learning approach to combine information from several randomly sampled patches, but this approach may not be adequate as different visual patterns may contribute unequally to prognosis prediction.

Graph Convolutional Neural Networks for Histologic Classification of Pancreatic Cancer.

Context.—: Pancreatic ductal adenocarcinoma has some of the worst prognostic outcomes among various cancer types. Detection of histologic patterns of pancreatic tumors is essential to predict prognosis and decide the treatment for patients.

Detection of Colorectal Adenocarcinoma and Grading Dysplasia on Histopathologic Slides Using Deep Learning.

Colorectal cancer (CRC) is one of the most common types of cancer among men and women. The grading of dysplasia and the detection of adenocarcinoma are important clinical tasks in the diagnosis of CRC and shape the patients' follow-up plans. This study evaluated the feasibility of deep learning models for the classification of colorectal lesions into four classes: benign, low-grade dysplasia, high-grade dysplasia, and adenocarcinoma.

Uncovering additional predictors of urothelial carcinoma from voided urothelial cell clusters through a deep learning-based image preprocessing technique.

Background: Urine cytology is commonly used as a screening test for high-grade urothelial carcinoma for patients with risk factors or hematuria and is an essential step in longitudinal monitoring of patients with previous bladder cancer history. However, the semisubjective nature of current reporting systems for urine cytology (e.g.

Predicting oncogene mutations of lung cancer using deep learning and histopathologic features on whole-slide images.

Lung cancer is a leading cause of death in both men and women globally. The recent development of tumor molecular profiling has opened opportunities for targeted therapies for lung adenocarcinoma (LUAD) patients. However, the lack of access to molecular profiling or cost and turnaround time associated with it could hinder oncologists' willingness to order frequent molecular tests, limiting potential benefits from precision medicine.

Incidence of Pancreatic Intraepithelial Neoplasia in an Autopsy Series.

Objectives: Pancreatic intraepithelial neoplasia (PanIN) is the currently preferred designation for putative preneoplastic changes in the pancreas. There are few data for the incidence of PanIN in the general population. Our goal was to determine the incidence of PanIN in a large group of pancreases obtained at autopsy.

The Host-Microbiome Response to Hyperbaric Oxygen Therapy in Ulcerative Colitis Patients.

Background & Aims: Hyperbaric oxygen therapy (HBOT) is a promising treatment for moderate-to-severe ulcerative colitis. However, our current understanding of the host and microbial response to HBOT remains unclear. This study examined the molecular mechanisms underpinning HBOT using a multi-omic strategy.

Evaluation of an Artificial Intelligence-Augmented Digital System for Histologic Classification of Colorectal Polyps.

Importance: Colorectal polyps are common, and their histopathologic classification is used in the planning of follow-up surveillance. Substantial variation has been observed in pathologists' classification of colorectal polyps, and improved assessment by pathologists may be associated with reduced subsequent underuse and overuse of colonoscopy.

Objective: To compare standard microscopic assessment with an artificial intelligence (AI)-augmented digital system that annotates regions of interest within digitized polyp tissue and predicts polyp type using a deep learning model to assist pathologists in colorectal polyp classification.

Resolution-based distillation for efficient histology image classification.

Developing deep learning models to analyze histology images has been computationally challenging, as the massive size of the images causes excessive strain on all parts of the computing pipeline. This paper proposes a novel deep learning-based methodology for improving the computational efficiency of histology image classification. The proposed approach is robust when used with images that have reduced input resolution, and it can be trained effectively with limited labeled data.

CD8 T-Cell-Predominant Lymphocytic Esophagitis is One of the Major Patterns of Lymphocytic Inflammation in Gastroesophageal Reflux Disease.

Context.—: Published reports have suggested an association of lymphocytic esophagitis (LyE) with gastroesophageal reflux disease (GERD) and primary motility disorders and have also shown that GERD and motility disorders frequently overlap. These findings make it difficult to determine the true relationship between LyE and GERD, which may be confounded by the presence of motility disorders with LyE.

A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies.

Non-alcoholic steatohepatitis (NASH) is a fatty liver disease characterized by accumulation of fat in hepatocytes with concurrent inflammation and is associated with morbidity, cirrhosis and liver failure. After extraction of a liver core biopsy, tissue sections are stained with hematoxylin and eosin (H&E) to grade NASH activity, and stained with trichrome to stage fibrosis. Methods to computationally transform one stain into another on digital whole slide images (WSI) can lessen the need for additional physical staining besides H&E, reducing personnel, equipment, and time costs.

Predicting colorectal polyp recurrence using time-to-event analysis of medical records.

Identifying patient characteristics that influence the rate of colorectal polyp recurrence can provide important insights into which patients are at higher risk for recurrence. We used natural language processing to extract polyp morphological characteristics from 953 polyp-presenting patients' electronic medical records. We used subsequent colonoscopy reports to examine how the time to polyp recurrence (731 patients experienced recurrence) is influenced by these characteristics as well as anthropometric features using Kaplan-Meier curves, Cox proportional hazards modeling, and random survival forest models.

Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides.

Importance: Histologic classification of colorectal polyps plays a critical role in screening for colorectal cancer and care of affected patients. An accurate and automated algorithm for the classification of colorectal polyps on digitized histopathologic slides could benefit practitioners and patients.

Objective: To evaluate the performance and generalizability of a deep neural network for colorectal polyp classification on histopathologic slide images using a multi-institutional data set.

Generative Image Translation for Data Augmentation in Colorectal Histopathology Images.

We present an image translation approach to generate augmented data for mitigating data imbalances in a dataset of histopathology images of colorectal polyps, adenomatous tumors that can lead to colorectal cancer if left untreated. By applying cycle-consistent generative adversarial networks (CycleGANs) to a source domain of normal colonic mucosa images, we generate synthetic colorectal polyp images that belong to diagnostically less common polyp classes. Generated images maintain the general structure of their source image but exhibit adenomatous features that can be enhanced with our proposed filtration module, called Path-Rank-Filter.

Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides.

Importance: Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown promising results. These approaches, however, require a laborious annotation process and are fragmented.

Objective: To evaluate a novel deep learning method that uses tissue-level annotations for high-resolution histological image analysis for Barrett esophagus (BE) and esophageal adenocarcinoma detection.

Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach.

Context: Celiac disease (CD) prevalence and diagnosis have increased substantially in recent years. The current gold standard for CD confirmation is visual examination of duodenal mucosal biopsies. An accurate computer-aided biopsy analysis system using deep learning can help pathologists diagnose CD more efficiently.

Automating the Paris System for urine cytopathology-A hybrid deep-learning and morphometric approach.

Background: The Paris System for Urine Cytopathology (the Paris System) has succeeded in making the analysis of liquid-based urine preparations more reproducible. Any algorithm seeking to automate this system must accurately estimate the nuclear-to-cytoplasmic (N:C) ratio and produce a qualitative "atypia score." The authors propose a hybrid deep-learning and morphometric model that reliably automates the Paris System.

Prediction of Atypical Ductal Hyperplasia Upgrades Through a Machine Learning Approach to Reduce Unnecessary Surgical Excisions.

Purpose: Surgical excision is currently recommended for all occurrences of atypical ductal hyperplasia (ADH) found on core needle biopsies for malignancy diagnoses and treatment of lesions. The excision of all ADH lesions may lead to overtreatment, which results in invasive surgeries for benign lesions in many women. A machine learning method to predict ADH upgrade may help clinicians and patients decide whether combined active surveillance and hormonal therapy is a reasonable alternative to surgical excision.

Pancreatic Ductal Adenocarcinoma and Its Precursor Lesions: Histopathology, Cytopathology, and Molecular Pathology.

Pancreatic ductal adenocarcinoma is one of the most aggressive malignant neoplasms with poor outcomes. At the time of diagnosis, the disease is usually at an advanced stage and only a minority is eligible for surgical resection. To improve the prognosis, it is essential to diagnose and treat the disease in an early stage before its progression into an invasive disease.

Mucosal inflammation in Candida esophagitis has distinctive features that may be helpful diagnostically.

The diagnosis of Candida esophagitis can be challenging when the epithelium containing Candida filamentous forms is not readily seen or is entirely sloughed away. Mucosal inflammation could be helpful diagnostically, if distinctive. However it is thought to be nonspecific in Candida esophagitis.