High-risk breast lesions: a combined intratumoral and peritumoral radiomics nomogram model to predict pathologic upgrade and reduce unnecessary surgical excision

Front Oncol. 2024 Dec 18:14:1479565. doi: 10.3389/fonc.2024.1479565. eCollection 2024.

Abstract

Objective: This study aimed to develop a nomogram that combines intratumoral and peritumoral radiomics based on multi-parametric MRI for predicting the postoperative pathological upgrade of high-risk breast lesions and sparing unnecessary surgeries.

Methods: In this retrospective study, 138 patients with high-risk breast lesions (January 1, 2019, to January 1, 2023) were randomly divided into a training set (n=96) and a validation set (n=42) at a 7:3 ratio. The best-performing MRI sequence for intratumoral radiomics was selected to develop individual and combined radiomics scores (Rad-Scores). The best Rad-Score was integrated with independent clinical and radiological risk factors by a nomogram. The diagnostic performance of the nomogram was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, along with accuracy, specificity, and sensitivity analysis.

Results: The nomogram based on the combined intratumoral and peritumoral Rad-Score of the dynamic contrast-enhanced MRI and clinical-radiological features achieved superior diagnostic efficacy in the training (AUC=0.914) and validation set (AUC=0.867) compared to other models. It also achieved a specificity and accuracy of 85.1% and 82.3% during training and 66.7% and 76.2% during validation.

Conclusion: The nomogram encapsulating the combined intratumoral and peritumoral radiomics demonstrated superior diagnostic efficacy in postoperative pathological upgrades of high-risk breast lesions, enabling clinicians to make more informed decisions about interventions and follow-up strategies.

Keywords: breast; high-risk; magnetic resonance imaging; nomograms; radiomics.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Shenzhen Science and Technology Research Fund through Project (Grant Number GJHZ20220913142613025).