Cardiovascular

A quantitative comparison between human experts and AI at estimating tumor-stroma ratio

The tumor–stroma ratio (TSR) is an established prognostic biomarker across several cancer types, yet its manual assessment remains labour-intensive and subject to inter-observer variability. An artificial intelligence (AI)-based estimate could offer an efficient, consistent alternative for this task. In this study, quantitative comparisons were made between expert humans and an AI model for TSR es...

Integration of Gene Expression and Digital Histology to Predict Treatment-Specific Responses in Breast Cancer

Deep learning models applied to digital histology can predict gene expression signatures (GES) and offer a low-cost, rapidly available alternative to molecular testing at the time of diagnosis. We optimized transformer-based models to infer GES results and applied this approach to pre-treatment H&E-stained biopsies from 1,940 breast cancer patients treated with neoadjuvant chemotherapy in clinical...

Optical Microscopy Predictions of Focal Recurrence in Glioblastoma

A hallmark of glioblastoma (GBM) is disease recurrence, which occurs in all patients despite tumor resection, radiation, and chemotherapy. A critical ...

Causal Machine Learning Analysis of All-Cause Mortality in Japanese Atomic-Bomb Survivors

The health consequences of ionizing radiation have long been studied, yet significant uncertainties remain, particularly at low doses. In particular, ...

Clinical Validation of RlapsRisk BC in an international multi-cohorts setting

This study evaluated the prognostic performance of RlapsRisk BC, a multimodal deep learning tool designed to predict distant recurrence-free interval ...

Development of a RAG-based Expert LLM for Clinical Support in Radiation Oncology

The ability of pre-trained large language models (LLMs) to rapidly master novel natural language processing tasks holds transformative potential. Howe...

Thymus Composition, Disease Control, and Toxicity in Locally Advanced Lung Cancer

Thymic involution, characterized by adipose replacement of functional thymic tissue, is a broadly recognized feature of age-related immunosenescence. ...

Advancing Breast Cancer Detection: A Comprehensive Evaluation of Machine Learning Models on Mammogram Imaging

Breast cancer, which is among the top causes of cancer-related deaths in women worldwide, demonstrates the importance of effective and rapid diagnosti...

AI-Powered Radiotherapy for Resource-Limited Settings: Advancing Cervical and Prostate Cancer Treatment Planning with the Radiation Planning Assistant (RPA)

Radiotherapy treatment planning is a resource-intensive process characterized by multiple manual steps and clinical hand-offs that contribute to treat...

Prospective Evaluation of AI Risk Stratification for Triaging Expedited Screening Mammogram Interpretation

To prospectively evaluate the feasibility and performance of expedited screening mammogram interpretation for women identified as high-risk by a deep ...

Utilizing Experimental Cognitive Assessments and Machine Learning to Advance Prediction of Cognitive Impairment in Breast Cancer Survivors: A Preliminary Study

Up to 80% of women breast cancer survivors (BCS), particularly those treated with chemotherapy, report persistent cognitive impairment. Several meta-a...

Spatial Structure of Tumor and Immune Cells Shape Outcomes in ER⁺HER2⁻ and Triple-Negative Breast Cancer

Immune infiltration is prognostic in triple-negative breast cancer (TNBC), but its role in ER⁺/HER2⁻ disease remains unclear, and conventional scoring...

IHGAMP: Pan-cancer HRD prediction from routine H&E whole-slide images using foundation models

Homologous recombination deficiency (HRD) confers sensitivity to poly (ADP-ribose) polymerase (PARP) inhibitors and platinum-based chemotherapy, repre...

Operational Survival Deficit of Neoadjuvant Chemotherapy in Early-Stage Breast Cancer: A Target Trial Emulation and Causal Machine Learning Study

Neoadjuvant chemotherapy (NAC) is the standard of care for locally advanced breast cancer. However, the disconnect between efficacy in randomized tria...

Integrating Artificial Intelligence and Precision Medicine to Characterize JAK-STAT Pathway Alterations in FOLFOX-Treated Colorectal Cancer in Disproportionately Affected Groups

Early-onset colorectal cancer (EOCRC) continues to rise, with the steepest increases observed among Hispanic/Latino (H/L) populations, underscoring th...

Artificial Intelligence-Driven Precision Oncology Uncovers Prognostic Significance of RTK-RAS Alterations in FOLFOX-Treated Early-Onset Colorectal Cancer

The incidence of early-onset colorectal cancer (EOCRC; <50 years) is rising rapidly among populations. Although alterations in the RTK-RAS signaling p...

Non-temporal tree-based models outperform temporal deep learning models in the prediction of chemotherapy-induced side effects from longitudinal laboratory data

The increasing availability of electronic health records (EHRs) provides opportunities to apply machine learning (ML) methods in support of clinical d...

Personalized Risk Stratification in Colon Cancer using Radiomic-Based Predictive Models

Colon Cancer (CC) is among the most frequently diagnosed malignancies and a leading cause of cancer-related death worldwide. Five-year survival varies...

Conversational Artificial Intelligence-Based Integration of Clinical and Genomic Data Identifies MAPK Alterations in Colorectal Cancer

Colorectal cancer (CRC) exhibits marked heterogeneity across age, ancestry, and treatment context, underscored by the rising incidence of early-onset ...

Sensitive and Specific Early-Stage Breast Cancer Detection using Deep Proteome Profiling from Plasma

Proteome-guided liquid biopsy tests hold immense promise for the future of early cancer detection. Our previous published work has shown strong perfor...