Chemotherapy

A Visually Interpretable Histopathology-Based Immune Model Predicts T-effector Biology and Response to Immune checkpoint inhibition in Clear Cell Renal Cell Carcinoma Clinical Trial and Contemporary Real-World Datasets

Immune checkpoint inhibitors (ICI) are central to the treatment of metastatic clear cell renal cell carcinoma (ccRCC), yet only a subset of patients derive durable benefit, and clinically deployable predictive biomarkers remain an unmet need. RNA-based T-effector signatures capture cytotoxic immune biology and have been associated with ICI response in clinical trial cohorts; however, their clinica...

Predicting Chemotherapy Response from Staging Laparoscopy Images

BackgroundFor patients with metastatic gastrointestinal cancers, chemotherapy resistance is a common phenomenon that, if known in advance, would allow for individualized treatment decisions. This study aimed to test the feasibility of developing a deep learning computer vision system that uses laparoscopy images depicting peritoneal surface metastases (i.e., capturing the in-vivo optical appearanc...

Cross-Attention Multimodal Learning for Predicting Response to Neoadjuvant Imatinib in Gastrointestinal Stromal Tumors: A Multicenter Retrospective Study

Background: Response to neoadjuvant imatinib in gastrointestinal stromal tumors (GISTs) is highly variable and cannot be reliably predicted using curr...

OracleScreen-LILRB4: Machine Learning-Guided Discovery of Myeloid Immune Checkpoint Binders Validated in Patient-Derived Cells

The identification of small molecule modulators of immune checkpoint proteins remains a significant challenge in drug discovery due to the flat, featu...

Physics-Informed Neural Networks for Chemotherapy Pharmacokinetics: Benchmarking the Clinical Estimator and Exposing Parameter Identifiability

Physics-Informed Neural Networks (PINNs) are an attractive tool for partial-observation problems in biology, where the governing dynamics are known bu...

Prognostic performance of an AI-based recurrence risk model in clinically low-risk HR+/HER2- early breast cancer

Objective Accurate prognostication of recurrence risk in HR+/HER2- early breast cancer is central for therapeutic decision-making, including identifyi...

Self-Reported Side Effects Among Reddit Users Taking Unapproved Retatrutide

Gray-market retatrutide use is increasing, but patient safety experiences remain poorly characterized. This cross-sectional analysis examined Reddit p...

A Foundation Model for the Cancer Genome

Cancer is a disease of the genome, in which somatic mutations and copy-number alterations determine tumour identity, clinical behaviour, and response ...

Deep Learning Spatial Profiling of CD103+CD8+ T Cells and Survival in Rectal Cancer After Neoadjuvant Chemoradiotherapy

Background: CD8+ tumor-infiltrating lymphocytes (TILs) are established prognostic markers in colorectal cancer, yet the clinical significance of CD103...

Interconnecting ADC Structure with Tumor Cell Biology with Multimodal Learning

Antibody-drug conjugates (ADCs) represent a significant advancement in cancer therapy, yet their development remains constrained by high attrition rat...

Predicting Response to Neoadjuvant Chemotherapy in Ovarian Cancer from CT Baseline Using Multi-Loss Deep Learning

Ovarian cancer is the most lethal gynecologic malignancy: around 60% of patients are diagnosed at an advanced stage, with an associated 5-year surviva...

Integrative Genomic, Single-Cell, and Functional Profiling of the CD48-CD244 Axis and NK-Cell Dysfunction in Multiple Myeloma

Multiple myeloma (MM) orchestrates immune evasion by subverting natural killer (NK) cell function. CD48, one of the most abundant NK-ligands on MM cel...

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...

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...

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...

A Tissue Microenvironment Analogous to Certain Tumor Microenvironments Facilitates HIV Persistence

The HIV reservoir that establishes early upon infection and persists in tissues remains the primary barrier to a functional cure. While progress has b...

Explainable AI Predicts Hematoxicity from Cancer Treatment Using Multimodal Real-World Data

Adverse drug effects remain a major barrier to safe and effective cancer therapy, underscoring the need for tools that predict treatment-related toxic...

Early prediction of skeletal muscle loss using longitudinal clinical data in patients with gastric cancer after radical gastrectomy and adjuvant chemotherapy: a retrospective cohort study

Gastric cancer patients frequently experience skeletal muscle loss during the perioperative and adjuvant treatment period, which has been associated w...

On the predictability of progression-free survival in ovarian cancer from NanoString gene expression data

In the treatment of high grade serous ovarian cancer (HGSC), patients initially diagnosed with unresectable tumors are first treated with neoadjuvant ...

A Conversational Artificial Intelligence Framework for Comparative Pathway-Level Profiling of Sezary Syndrome and Primary Cutaneous CD8+ Aggressive Epidermotropic Cytotoxic T-Cell Lymphoma (PCAECTCL)

Background: Sezary syndrome (SS) is an aggressive leukemic variant of cutaneous T-cell lymphoma (CTCL) with distinct clinical and biological features ...