HPN: A Multimodal Neural Network Model for Non-invasive HER2 Status Assessment in Breast Cancer Patients

Background HER2-positive breast cancer is known for its aggressive behavior and poorer prognosis in the absence of anti-HER2 therapy. Current assessments of HER2+ highlight the need for non-invasive diagnostic tools. This study introduces a multimodal approach called the HER2 Prediction Network (HPN) to noninvasively predict HER2 status, thereby supporting the precise administration of HER2-targeted therapies. Methods A cohort of 482 breast cancer patients were enrolled from Peking Union Medical College Hospital. HPN was developed by ResNet and Transformer, utilizing clinicopathological and ultrasound data collected from breast cancer patients. After training, this model could differentiate HER2-zero, HER2-low and HER2-positive breast cancer patient and detect HER2 status in different peritumoral regions. Findings The HPN demonstrated robust performance in HER2 expression identification of breast cancer patients. It achieved an Accuracy of 0.76 and an Area Under the Curve(AUC) of 0.86. Detections for different peritumoral regions have all shown favorable results(AUC _1.2x =0.85, AUC _1.4x =0.85 AUC _1.6x =0.86). Conclusion The HPN provided a non-invasive method for assessing HER2 expression, thereby facilitating decision-making regarding the intervention of HER2-targeted therapy.