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

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.