Work

The value of intratumoral and peritumoral radiomics features based on multiparametric MRI for predicting molecular staging of breast cancer

Year: 2025

Type: article

Abstract: Purpose: A model for preoperative prediction of molecular subtypes of breast cancer using tumor and peritumor radiomics features from multiple magnetic resonance imaging (mMRI) sequences, combined with semantic features. Materials and methods: A total of 254 female patients with pathogically confirmed breast cancer were enrolled in this study. Preoperative mMRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE) sequences, covered the entire breast. To analyze the MRI semantic features of different molecular subtypes of breast cancer and identify independent predictive risk factors. Thirty-three binary classification models were established based on the radiomic features of different sequences and peritumoral ranges. The best radiomics model was selected by comparing the performance of the above radiomics models. At the same time, the best sequence and peritumoral extent were extracted from the target features, the radiomics score was calculated, and independent risk factors were predicted. Finally, a nomogram was established for preoperative prediction of Triple-Negative Breast Cancer (TNBC), Hormone Receptor (HR) positive and HER2 negative (HR+/HER2-), and HER2+ molecular staging types of breast cancer. Results: Tumor length, edge enhancement, and peritumoral edema were independent risk factors for predicting the different molecular types of breast cancer. The best MRI sequence was DCE and the best peritumoral margin was 6 mm. The AUC of the nomogram based on the optimal sequence(DCE) and optimal peritumoral range (6 mm) combined with independent risk factors were 0.910, 0.909, and 0.845, respectively. Conclusion: The nomogram based on independent predictors combined with intratumoral and peritumoral radiomics scores can be used as an auxiliary diagnostic tool for molecular subtype prediction in breast cancer.

Source: Frontiers in Oncology

DOI: 10.3389/fonc.2025.1379048

Authors: Yuxuan Han, Manxia Huang, Lizhi Xie, Yuhai Cao, Yang Dong

Institution: Second Affiliated Hospital of Dalian Medical University

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

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