AI and Cancer Research ── Breast ── 2026-11-10

Etching of a tissue slide where small beads cluster tightly in one region and scatter in another, showing position as a way to tell patients apart. — What this means, as an image (AI-generated, GPT Image): Pathologists and oncologists can add where neutrophils sit to existing markers when sorting breast cancer patients.Download image (PNG, 2000×800)

1 Breast papers. Lead: Decoding neutrophil spatial heterogeneity and clinical implications in breast cancer

Journals covered and how papers are chosen: see the index

Breast cancer: treatment, trials and AI

Decoding neutrophil spatial heterogeneity and clinical implications in breast cancer
Cancer Cell · 2026-11-09 · Evidence: Retrospective observational (patient cohorts) with preclinical mechanistic analysis
Abstract
Xu et al. investigate neutrophil spatial heterogeneity by integrating AI-powered computational pathology and single-cell spatial omics in comprehensive breast cancer cohorts. They identify clinically relevant neutrophil spatial patterns and provide mechanistic insights into the underlying biology. Combining neutrophil spatial patterns with established biomarkers further refines patient stratification.
Journal IF-equivalent: 37.8 (OpenAlex 2-year mean citedness, value as of 2026-11-09, retrieved 2026-11-10; not the official Clarivate IF)
Reference: [No authors listed]. Decoding neutrophil spatial heterogeneity and clinical implications in breast cancer. Cancer Cell. [Epub ahead of print]. doi:10.1016/j.ccell.2026.10.005.
Checked: Abstract only

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