Oncology/Hematology

Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistance

Precision oncology lacks scalable tools to assess, at the patient level, systems-level tumor microenvironment (TME) programs driving therapeutic resistance. To address this gap, we trained a weakly-supervised deep learning model that uses routine H&E whole-slide images (WSIs) to derive quantitative activity for therapeutically-relevant TME phenotypes, spanning immune, metabolic, and tumor cell-int...

Bridging visual saliency and large language models for explainable deep learning in medical imaging

The opaque nature of deep learning models remains a significant barrier to their clinical adoption in medical imaging. This paper presents a multimodal explainability framework that bridges the gap between convolutional neural network (CNN) predictions and clinically actionable insights for brain tumor classification, leveraging large language models (LLMs) to deliver human-interpretable diagnosti...

BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation

We introduce a new strategy for compositional neural surrogates for radiation-matter interactions, a key task spanning domains from particle physics t...

Spatial remodeling of the urothelial carcinoma tumor microenvironment shapes response to neoadjuvant atezolizumab

The ABACUS study was a single arm, phase II trial evaluating neoadjuvant atezolizumab in operable urothelial carcinoma. Initial bulk transcriptomic an...

Metastatic Site Prediction in Breast Cancer using Kirchhoff's Law and Omics Knowledge Graph

Predicting the anatomical site of metastasis from a primary tumour remains an unsolved problem in breast cancer (BRCA) and metastatic disease more bro...

Predicting bladder cancer molecular subtypes linked to bacillus Calmette-Guerin response from histology images using deep learning

Background and objective: High-risk non-muscle-invasive bladder cancer (HR-NMIBC) is treated with transurethral resection and intravesical BCG instill...

Integrated Multi-Omics Analysis for the Identification of Disease-Associated Variations and Prognostic Biomarkers in Triple-Negative Breast Cancer (TNBC)

Background: Triple-negative breast cancer (TNBC) exhibits substantial molecular heterogeneity and lacks targeted receptor therapies. Single-omic appro...

Tumor cell specific total mRNA expression informed neural networks predicts cancer progression

Inferring tumor molecular phenotypes from high-dimensional multi-omic data is a fundamental challenge in computational biology. Current methods for es...

Mechanistic learning to predict and understand minimal residual disease

Mechanistic modeling has long been used as a tool to describe the dynamics of biological systems, especially cancer in response to treatment. Their ke...

Extracting adverse event nature, severity, timelines and resulting interventions from clinical notes of patients receiving CAR-T therapy using large language models.

Chimeric Antigen Receptor T-cell (CAR-T) therapy, where genetically engineered patient T cells target tumor antigens, has transformed care for hematol...

Detection of Hepatocellular Carcinoma from B-Mode and Contrast-Enhanced Ultrasound Using a Dual-Path Convolutional Network

Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, with particularly severe consequences in sub-Saha...

Interpreting Omics Data Analysis with Large Language Models for Disease Target and Drug Discovery

In biomedical scientific discovery, synthesizing prior knowledge from the literature is an essential component of interpreting numerical omics data an...

Clonal embeddings allow exploratory analysis of lineage-resolved single-cell data

Assays coupling high-throughput lineage tracing with single-cell transcriptomics are transforming studies of development and disease biology, revealin...

Orientation-Aware Unsupervised Domain Adaptation for Brain Tumor Classification Across Multi-Modal MRI

The clinical integration of deep learning models for brain tumor diagnosis in neuro-oncology is severely constrained by limited expert-annotated MRI d...

Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training

A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of non-essential cells close to...

Recurrent Deep Reinforcement Learning for Chemotherapy Control under Partial Observability

Chemotherapy dose optimization can be formulated as a dynamic treatment regime, requiring sequential decisions under uncertainty that must balance tum...

Biological Spatial Priors Regularize Foundation Model Representations for Cross-Site MSI Generalization in Colorectal Cancer

Predicting microsatellite instability (MSI) status from routine hematoxylin and eosin (H&E) whole slide images (WSIs) offers a practical alternative t...

Virtual Scanning for NSCLC Histology: Investigating the Discriminatory Power of Synthetic PET

Accurate histological differentiation between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) is critical for personalized treatment in non-sma...

Automatic Bevacizumab Response Prediction in Ovarian Cancer from Digital Pathology Images via Novel AI-based Computational Pipeline

Ovarian cancer is one of the gynecological cancer types, which, if metastasized and not detected early, can cause deaths among women. Therefore, there...

A generative reference grammar of healthy TCR repertoires reveals cancer-associated immune remodeling

T-cell receptor (TCR) repertoires encode the organization of adaptive immunity and its reshaping by cancer and therapy, but disentangling treatment-as...