Breast Cancer

AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center Study

Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary artery disease. We used artificial intelligence (AI) to automatically quantify hepatic tissue measures for identifying HS from CT attenuation correction (CTAC) scans during myocardial perfusion imaging (MPI) and evaluate their added prognostic value for...

Light weight deep learning-based auto-quantification system for bright-filed HER2 dual in situ hybridization image analysis

The evaluation of erb-b2 receptor tyrosine kinase 2 (ERBB2 or HER2) gene amplification status through Dual in Situ Hybridization (DISH) currently relies on manual assessment by pathologists. There are several deep learning-based algorithms for H&E or ISH analysis. However, DISH analysis tools are still lacking. We developed a fully automated deep learning-based quantification system to assist path...

The African Breast Imaging Dataset for Equitable Cancer Care: Protocol for an Open Mammogram and Ultrasound Breast Cancer Detection Dataset

Breast cancer is one of the most common cancers globally. Its incidence in Africa has increased sharply, surpassing that in high-income countries. Mor...

Deep Learning-Assisted Skeletal Muscle Radiation Attenuation at C3 Predicts Survival in Head and Neck Cancer

Head and neck cancer (HNC) patients face an increased risk of malnutrition due to lifestyle, tumor localization, and treatment effects. While skeletal...

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 an...

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 ...

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 ...

A Glioma Stem Cell–Associated Transcriptomic Program Predicts Survival Across Adult and Pediatric High-Grade Gliomas

High-grade gliomas (HGGs), including adult glioblastoma (GBM) and pediatric diffuse intrinsic pontine gliomas (DIPGs), are sustained by glioma stem ce...

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...