Abstract #0865

Preoperative Prediction of Her2-zero, -low and -overexpression Breast Cancers Using Multiparametric MRI and Machine Learning Modeling

Jiejie Zhou^1,2, Yang Zhang¹, Jinhao Wang³, Yezhi Lin⁴, Ga Young Yoon^2,5, Yan-lin Liu², Jeon-Hor Chen², Hailing Wang³, Meihao Wang¹, and Min-ying Su²

¹First affiliated hospital of Wenzhou Medical University, Wenzhou, China, ²University of California, Irvine, Irvine, CA, United States, ³Guangxi Normal University, Guilin, China, ⁴Wenzhou Medical University, Wenzhou, China, ⁵University of Ulsan College of Medicine, Gangneung Asan Hospital Gangwondo, Gangneung, Korea, Republic of

Synopsis

Keywords: Diagnosis/Prediction, Breast

Motivation: Her2-low breast cancers could benefit from new anti-HER2 therapies.

Goal(s): To construct a preoperative prediction model of HER2 expression levels using multiparametric MRI and machine learning (ML) algorithms.

Approach: 621 patients were investigated. Four ML methods were used to build models based on MRI features to predict HER2 expression levels.

Results: MRI features of multiple lesions, spiculated margin, peritumoral edema and largest diameter were selected to build the models. ML models performed better for predicting HER2-zero vs. HER2-low/-overexpression than HER2-low vs. HER2-overexpression. The best model was KNN of AUC 0.86, sensitivity of 76%, specificity of 73%, and accuracy of 75%.

Impact: MRI features of breast cancer are associated with different HER2 expression levels. MRI-based ML models have the potential to preoperatively predict the HER2 expression status.

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