Keywords: Diagnosis/Prediction, Cancer
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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