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September 17, 2025

Value of predicting HER-2 and Ki-67 expression status in breast cancer based on multiparametric MRI intratumor combined with peritumor radiomics

Puntos clave

Accurate preoperative prediction of HER-2 and Ki-67 expression can improve treatment decisions in breast cancer.
A random forest machine learning algorithm was utilized to create radiomics models for breast cancer prediction.
Multiparametric MRI combined with peritumor radiomics offers a promising approach for non-invasive prediction.
The study emphasizes the importance of precise biomarkers like HER-2 and Ki-67 in guiding personalized therapy.

Resumen

Motivation: There is an urgent need to find a non-invasive method that can accurately predict HER-2 and Ki-67 expression status in breast cancer. Goal(s): Establishment of multiparametric MRI intratumor combined with peritumor radiomics models for preoperative prediction of HER-2 and Ki-67 expression status in breast cancer. Approach: A two-center retrospective study. Results: A random forest （RF） machine learning algorithm was used to construct eight radiomics models for preoperative predict HER-2 and Ki-67 expression status in breast cancer: intratumoral radiomics models, intratumoral combined with peritumoral (3-mm) radiomics models, and multisequence fusion radiomics models. Impact: Accurate preoperative prediction of HER-2 and Ki-67 expression status in breast cancer is expected to provide a reference for precise and personalized treatment decisions in later stages of clinical practice.

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