Work

Early peritumoral edema change improve prediction of pathological complete response in breast cancer: a multiparametric magnetic resonance imaging-based model

Year: 2026

Type: article

Abstract: Objective: To evaluate the independent and additive predictive value of peritumoral edema for pathological complete response (pCR) following neoadjuvant chemotherapy (NAC) in patients with breast cancer. Materials and methods: Breast cancer patients who underwent NAC between February 2018 and December 2024 were retrospectively enrolled. Based on treatment response, patients were categorized into pCR and non-pCR groups. Clinicopathological characteristics, peritumoral edema, and changes in MRI features (pre-NAC and after two cycles of NAC) were compared between groups. Variables showing statistical significance were included in multivariate logistic regression to identify independent predictors. The additional contribution of edema to predictive performance was assessed using the DeLong test. Results: A total of 230 patients were included, of whom 96 achieved pCR. Multivariate analysis identified early changes in peritumoral edema, percentage change in apparent diffusion coefficient, percentage change in lesion diameter, and molecular subtype as independent predictors of pCR (P < 0.05). The integration of edema changes with conventional MRI features enhanced predictive accuracy (AUC improved from 0.816-0.844 to 0.851-0.880). A comprehensive prediction model incorporating molecular subtype and MRI features achieved the highest AUC values: 0.899 in the training cohort and 0.882 in the validation cohort. Conclusion: A predictive model incorporating early peritumoral edema changes, apparent diffusion coefficient, lesion diameter, and molecular subtype achieved an AUC of 0.899, indicating robust predictive performance in predicting pCR among breast cancer patients undergoing NAC. This model may facilitate personalized treatment planning and clinical decision-making.

Source: Frontiers in Oncology

DOI: 10.3389/fonc.2026.1671985

Authors: Fuqiang Pan, Yalei Wang, Baoqi Zhang, Yuqing Xin, Heshan Han, Yang Zhang

Institution: Anhui Medical University

Language: English

Topic: MRI in cancer diagnosis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801