AI Medical Compendium Journal:
JNCI cancer spectrum

Showing 1 to 10 of 16 articles

Moving Forward in the Next Decade: Radiation Oncology Sciences for Patient-Centered Cancer Care.

JNCI cancer spectrum
In a time of rapid advances in science and technology, the opportunities for radiation oncology are undergoing transformational change. The linkage between and understanding of the physical dose and induced biological perturbations are opening entire...

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer.

JNCI cancer spectrum
BACKGROUND: New biomarkers of risk may improve breast cancer (BC) risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images into epithelium, fibrous stroma, and fat. We applied our method...

Randomized Phase II Trial of Exercise, Metformin, or Both on Metabolic Biomarkers in Colorectal and Breast Cancer Survivors.

JNCI cancer spectrum
BACKGROUND: Observational data support inverse relationships between exercise or metformin use and disease outcomes in colorectal and breast cancer survivors, although the mechanisms underlying these associations are not well understood.

Deoxyribonuclease I Activity, Cell-Free DNA, and Risk of Liver Cancer in a Prospective Cohort.

JNCI cancer spectrum
BACKGROUND: Circulating cell-free DNA (cfDNA) is a proposed latent biomarker for several cancers, including liver cancer. Deoxyribonucleases (DNases) facilitate the timely and efficient degradation of cfDNA, leading us to hypothesize that DNase I and...

Cirrus: An Automated Mammography-Based Measure of Breast Cancer Risk Based on Textural Features.

JNCI cancer spectrum
BACKGROUND: We applied machine learning to find a novel breast cancer predictor based on information in a mammogram.

AI meets informed consent: a new era for clinical trial communication.

JNCI cancer spectrum
Clinical trials are fundamental to evidence-based medicine, providing patients with access to novel therapeutics and advancing scientific knowledge. However, patient comprehension of trial information remains a critical challenge, as registries like ...

The use of large language models to enhance cancer clinical trial educational materials.

JNCI cancer spectrum
BACKGROUND: Adequate patient awareness and understanding of cancer clinical trials is essential for trial recruitment, informed decision making, and protocol adherence. Although large language models (LLMs) have shown promise for patient education, t...

A novel machine learning-based cancer-specific cardiovascular disease risk score among patients with breast, colorectal, or lung cancer.

JNCI cancer spectrum
BACKGROUND: Cancer patients have up to a 3-fold higher risk for cardiovascular disease (CVD) than the general population. Traditional CVD risk scores may be less accurate for them. We aimed to develop cancer-specific CVD risk scores and compare them ...