Oncology/Hematology

A spatial multi-omic portrait of survival outcome for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the leading cause of kidney cancer-related death, but how the tumor microenvironment shapes patient survival is not completely understood. Here, we describe the characterization of ccRCC tumor ecosystems from 498 patients using imaging mass cytometry with a focus on tumor, myeloid, and T cell landscapes. Data from more than 3 million single cells is analy...

Pan-cancer tumour classification and risk stratification from whole-genome somatic variants via dual-task representation learning

Tumour typing from whole-genome sequencing is increasingly accurate, yet molecular subtyping from somatic variants remains challenging because of tumour heterogeneity and inconsistent clinical annotations. Here, we present Mutation-Attention Dual-Task (MuAt2), a Transformer model that jointly classifies histological tumour types and subtypes directly from somatic single-nucleotide variants, indels...

LLMsFold: Integrating Large Language Models and Biophysical Simulations for De Novo Drug Design

The discovery of novel small molecules is challenging because of the vastness of chemical space and the complexity of protein-ligand interactions, lea...

Likelihood-Free Parameter Inference for Spatiotemporal Stochastic Biological Models using Neural Posterior Estimation

Cell migration is a key biological process underlying wound healing, tissue development, and cancer metastasis, yet calibrating mathematical models of...

CoRe-BT: A Multimodal Radiology-Pathology-Text Benchmark for Robust Brain Tumor Typing

Accurate brain tumor typing requires integrating heterogeneous clinical evidence, including magnetic resonance imaging (MRI), histopathology, and path...

TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growth

Glioblastoma exhibits diverse, infiltrative, and patient-specific growth patterns that are only partially visible on routine MRI, making it difficult ...

Revisiting the Role of Foundation Models in Cell-Level Histopathological Image Analysis under Small-Patch Constraints -- Effects of Training Data Scale and Blur Perturbations on CNNs and Vision Transformers

Background and objective: Cell-level pathological image analysis requires working with extremely small image patches (40x40 pixels), far below standar...

Conversational Artificial Intelligence Agents-Enabled Dissection of RTK-RAS and MAPK Pathway Dependencies in Gemcitabine-Treated Pancreatic Ductal Adenocarcinoma (PDAC)

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by profound molecular heterogeneity and inconsistent responses to ge...

Early treatment outcome prediction in metastatic castration-resistant prostate cancer utilizing 3-month tumor growth rate (g-rate) based machine learning model

Summary Background Once the treatment starts, early prediction of treatment benefit and its correlation with overall survival (OS) remains challenging...

Transcriptomic profiling of mouse mammary tumors enables prognostic and predictive biomarker discovery for human breast cancers

The development and validation of prognostic and predictive biomarkers in breast cancer is limited by the availability of well-annotated datasets link...

Exploration of the screening and regulatory mechanisms of biomarkers related to ac4C modification in laryngeal squamous cell carcinoma patients based on single-cell analysis and machine learning

Background: N4-acetylcytidine (ac4C) modification plays a critical role in cancer development. Exploring ac4C modification in laryngeal squamous cell ...

BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathology

Generalist pathology foundation models (PFMs), pretrained on large-scale multi-organ datasets, have demonstrated remarkable predictive capabilities ac...

Multi-Omics Integration for Identification of Prognostic Molecular Signatures for Survival Stratification in Lung Cancer

Lung cancer is characterized by profound intratumoral and inter-patient heterogeneity, spanning histological subtypes, molecular landscapes, and the t...

Temporal dynamics of radiotherapy and chemotherapy response in lower-grade gliomas using causal machine learning

Lower-grade gliomas (World Health Organization [WHO] grades 2-3) exhibit variable treatment responses, yet clinical decisions remain guided by populat...

t2pmhc: A Structure-Informed Graph Neural Network to predict TCR-pMHC Binding

Mapping of T cell receptors (TCRs) to their cognate MHC-presented peptides (pMHC) is central for the development of precision immunotherapies and vacc...

Detecting Extrachromosomal DNA from Routine Histopathology

Extrachromosomal DNA (ecDNA) is a major driver of oncogene amplification, tumour heterogeneity and poor clinical outcomes [1-3], yet its detection rel...

PathMoE: Interpretable Multimodal Interaction Experts for Pediatric Brain Tumor Classification

Accurate classification of pediatric central nervous system tumors remains challenging due to histological complexity and limited training data. While...

Robust White Blood Cell Classification with Stain-Normalized Decoupled Learning and Ensembling

White blood cell (WBC) classification is fundamental for hematology applications such as infection assessment, leukemia screening, and treatment monit...

Bridging the gap between Performance and Interpretability: An Explainable Disentangled Multimodal Framework for Cancer Survival Prediction

While multimodal survival prediction models are increasingly more accurate, their complexity often reduces interpretability, limiting insight into how...

The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction

Breast cancer is the most frequently diagnosed malignancy among women worldwide and a leading cause of cancer-related mortality. Dynamic contrast-enha...