Oncology/Hematology

The University of Texas Southwestern Glioma Dataset - MRI, Molecular Markers and Segmentations.

Gliomas are the most common type of primary brain tumors. Their management options and outcomes depend significantly on the underlying molecular-marker profile. Traditionally, molecular markers are determined through pathological testing on a tissue specimen acquired through biopsy. Several Magnetic Resonance Imaging (MRI) based Deep Learning (DL) methods offer a promising, non-invasive approach t...

A multimodal approach integrating NK cell-associated gene signatures and pathomics to predict colon adenocarcinoma prognosis.

ThyroFusion: A Multi-modal Deep Learning Framework Integrating Vision and Language for Thyroid Nodule Malignancy Risk Assessment.

Accurate differentiation between benign and malignant thyroid nodules remains challenging in clinical practice. Current deep learning approaches predo...

Delineating Pediatric Adrenocortical Tumors by GC-MS Urinary Steroid Metabolome Analysis: Observations From the MET Study.

INTRODUCTION: Adrenocortical tumors (ACTs) comprise adrenocortical adenomas (ACAs) and adrenocortical carcinomas (ACCs); the latter are highly aggress...

An Interpretable Machine-Learning Model for Predicting Occult Central Lymph Node Metastasis in Papillary Thyroid Cancer.

CONTEXT: Accurate preoperative prediction of occult lymph node metastasis (OLNM) in clinically lymph node negative (cN0) papillary thyroid carcinoma (...

Graves' disease: a new era of pathophysiology-guided therapeutics.

Graves' disease (GD) is an autoimmune entity that had an unchanged treatment paradigm for more than half a century with radioactive iodine, antithyroi...

Game of clones: decipher lineage plasticity in hormone-driven cancers.

Hormone-dependent cancers such as prostate, breast, and endometrial carcinomas rely on nuclear hormone receptors to sustain lineage identity and growt...

Multiclass lung cancer detection using a hybrid capsule inspired deep neural network.

Convolutional Neural Networks are widely used in lung cancer detection for more than a decade. However, it suffers from preserving spatial relationshi...

Divergent macrophage-regulated T cell states determine response to Bacillus Calmette-Guérin vaccine in high-risk bladder cancer.

BACKGROUNDPrimary therapy for high-risk bladder cancer (BCa) is repeated instillations of the tuberculosis vaccine Bacillus Calmette-Guérin (BCG). Alt...

FDA-authorized oncology artificial intelligence and machine learning devices and their clinical evidence: A cross-sectional analysis.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) tools are increasingly embedded in cancer care, yet the scope of U.S. Food and Drug...

Exploring the toxicological impact of perfluorooctanoic acid and perfluorooctane sulfonate on glioblastoma through network toxicology, machine learning, and multi-dimensional bioinformatics analysis.

Exposure to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) has been associated with the development of various malignant tumors. H...

Nanomaterials reshaping cancer radiotherapy: Radiosensitization mechanisms, delivery and theranostic platforms, multimodal synergy, and clinical translation strategies.

Radiotherapy (RT) remains a cornerstone of cancer management but is fundamentally constrained by normal tissue toxicity, intrinsic and acquired radior...

Crosstalk between immune and programmed cell death and their application in colorectal cancer risk stratification.

Programmed cell death (PCD) and cellular immunity play pivotal roles in colorectal cancer (CRC) progression; understanding their crosstalk and identif...

Identification of oral squamous cell carcinoma by an electronic nose based on an array of metal oxide sensors with machine learning.

World Health Organization classification of tumours of the breast 6th edition 2026.

The 6th edition of the WHO Classification of Breast Tumours introduces both major and minor changes based on recent advances in our understanding of b...

A novel autoantibody panel as potential diagnostic markers for hepatocellular carcinoma.

AIMS: Hepatocellular carcinoma (HCC) represents a major global health burden. Tumor-associated autoantibodies (TAAs) represent promising biomarkers fo...

Artificial intelligence-based screening of phytochemicals for targeted cancer therapy.

Cancer remains one of the leading causes of death worldwide and continues to pose a serious public health challenge. The limited success of many curre...

Letter to the Editor on: Predicting Postoperative Neurological Outcomes in Metastatic Spinal Tumor Surgery Using Machine Learning.

[Prevention in Gastroenterology].

Gastroenterology offers a broad, evidence-based range of preventive measures that goes beyond colorectal cancer screening. As a specialty of systemic ...

From pixels to pathology: how artificial intelligence mammographic risk scores capture tumor biology through imaging.

OBJECTIVES: To characterize clinical-pathologic tumor features associated with artificial intelligence (AI)-generated risk scores from prior-year scre...