Deep learning-based prediction of HER2 status from breast diffusion-weighted MRI

Abstract

Background and objective The accurate classification of human epidermal growth factor receptor 2 (HER2) status is very important for the diagnosis and treatment of breast cancer. However, the biopsy, the current gold standard for differentiating HER2 status, is invasive and time-consuming. To overcome these drawbacks, a novel deep learning model was developed to differentiate HER2-negative and HER2-positive status in breast cancer solely based on diffusion-weighted imaging (DWI). Materials and methods This retrospective study included 239 women patients confirmed with breast cancer from two local medical centers. A hybrid CNN-Transformer DL model was proposed, which took DWI images (the ADC maps, DWI images with b = 0 s/mm² and b = 800 s/mm²) as inputs and output the classification of HER2-negative and HER2-positive status. Classification by the proposed DL model was quantitatively compared to the classification by the other benchmark DL models and two clinical experts. Results Data of the 239 patients (mean age, 49.4 ± 10.0 years) were separated into a training set (n = 156), an internal test set (n = 39), and an external test set (n = 44). On the internal test set, the proposed DL model performed numerically better than the best benchmark DL model (area under the curve [AUC]: 0.93 vs. 0.89; accuracy: 0.90 vs. 0.85). On the external test set, the proposed model also performed numerically better than the best benchmark model (AUC: 0.91 vs. 0.87; accuracy: 0.84 vs. 0.82), and significantly better than the two clinical experts (AUC: 0.91 vs. 0.65 vs. 0.63; accuracy: 0.84 vs. 0.61 vs. 0.57). Conclusion This study demonstrates the promise of combining DWI and DL for the classification of HER2 status in breast cancer, and it may potentially serve as a non-invasive adjunct or decision-support tool.