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Updated: Jul 15, 2026

Clinical Imaging of Microwave Mammography

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Published on: November 14, 2025

Habitat-Based MRI Radiomics for Predicting Breast-Conserving Surgery Feasibility After Neoadjuvant Chemotherapy in

Jinrui Liu¹, Mingsong Tang¹, Fei Jia¹

¹Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China.

Clinical Breast Cancer

|July 13, 2026

Summary

A new combined model accurately predicts breast-conserving surgery (BCS) eligibility after neoadjuvant chemotherapy (NAC) using radiomics and clinical data. This noninvasive tool aids surgical planning and personalized breast cancer treatment.

Keywords:

Habitat radiomics Machine learning Magnetic resonance imaging Prediction model Radiomic features

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Published on: August 14, 2012

Area of Science:

Oncology
Radiology
Medical Imaging

Background:

Neoadjuvant chemotherapy (NAC) improves breast cancer outcomes, increasing breast-conserving surgery (BCS) rates.
Accurate preoperative prediction of BCS eligibility post-NAC is crucial for treatment planning but currently lacks reliable noninvasive tools.

Purpose of the Study:

To develop and validate a noninvasive model for predicting breast-conserving surgery (BCS) feasibility after neoadjuvant chemotherapy (NAC).
To integrate radiomic features from DCE-MRI with clinical-radiological variables for enhanced prediction accuracy.

Main Methods:

Retrospective multicenter study of 315 breast cancer patients undergoing NAC.
Extraction of habitat radiomics features from DCE-MRI to build a habitat model.
Development of a combined model using radiomic and clinical-radiological predictors, validated across training, internal, and external cohorts.

Main Results:

The combined model achieved superior predictive performance with AUCs of 0.910, 0.839, and 0.755 in the training, internal, and external validation cohorts, respectively.
The habitat radiomics model and the clinical-radiological model showed moderate predictive capabilities individually.
The overall BCS rate in the study cohort was 73.65%.

Conclusions:

A combined model integrating habitat radiomics and clinical-radiological data effectively predicts BCS feasibility after NAC.
This noninvasive approach supports preoperative surgical planning and the development of individualized breast-conserving treatment strategies.