Radiopathomic Graph Deep Learning for Multiscale Spatial-Contextual Modeling of Intratumoral Heterogeneity to Predict Breast Cancer Response to Neoadjuvant Therapy

Radiology Artificial Intelligence 2026 Open access

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Authors: Li Zhou, Xin-Yi Wang, Ye Xu, Hong‐Xia Zhang, Xinxin Yang, Ruiqi Jin, Xi‐Qiao Sang, Yue-min ZHU, Hongxue Meng, Zi‐Xiang Kuai
DOI: 10.1148/ryai.250760
Cited by: 1
References: 35

Abstract

A radiopathomic graph deep learning system performed well for explainable prediction of pathologic complete response to neoadjuvant therapy in breast cancer through intratumoral heterogeneity spatial-contextual modeling at the tissue and cellular scales.

Fields of Study & Subjects

Radiomics and Machine Learning in Medical Imaging Breast Cancer Treatment Studies MRI in cancer diagnosis

Publication details

Journal / source
Radiology Artificial Intelligence
Publisher
Radiological Society of North America (RSNA)
Volume
8
Issue
3
Pages
e250760–e250760
Publication date
8 April 2026
Language
EN