DWI and ADC Habitat Imaging in Predicting HER2 Expression Status in Bladder Cancer: A Retrospective Study

Background: Human epidermal growth factor receptor 2 (HER2) antibody-coupled drugs have shown promising clinical benefits in patients with bladder cancer (BCa). HER2 expression status is generally detected clinically using postoperative pathological immunohistochemistry (IHC), but preoperative non-invasive detection of BCa HER2 expression status remains to be sought. The aim of this study was to investigate the value of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) habitat imaging in predicting the expression of HER2 in BCa. Methods: This retrospective study included 232 BCa patients (November 2022–February 2024) with HER2 status confirmed by immunohistochemistry. The K-means clustering algorithm is used to re-segment the region of interest. Based on the spatial distribution of the habitat map, the histogram features of each subregion were extracted. Based on the Spearman correlation coefficient (> 0.7) feature screening results, a support vector machine (SVM) classification model was established to predict the expression of HER2 in BCa. The discrimination ability of the model was evaluated by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC), and the diagnostic performance of the model was comprehensively evaluated by combining the calibration curve and the decision curve. Results: Randomly divided patients into training cohort (N = 148, median age 68.66 years; 121men), validation (N = 47, median age 69.12 years; 39 men), and test cohort (N = 37, median age 67.92 years; 32men) according to the ratio of 6:2:2. Based on the contour coefficient, K = 2 is finally selected as the clustering parameter to cluster the DWI and ADC images into two subregions. A total of 80 features were extracted from the four sub-regions of the two sequences. After screening, an SVM prediction model was constructed from the remaining 17 features. In the SVM model, the AUC of the training set was 0.88 (95% CI: 0.82–0.93), the validation set was 0.85 (95% CI: 0.72–0.94), and the test set was 0.84 (95% CI: 0.88–0.94). Conclusion: MRI-based habitat analysis can help distinguish heterogeneous regions of BCa and effectively predict HER2 expression status of BCa. Clinical trial number: Not applicable.