Usefulness of texture features of apparent diffusion coefficient maps in predicting chemoradiotherapy response in muscle-invasive bladder cancer.

OBJECTIVES

To examine the usefulness of the texture analysis (TA) of apparent diffusion coefficient (ADC) maps in predicting the chemoradiotherapy (CRT) response of muscle-invasive bladder cancer (MIBC).

METHODS

We reviewed 45 MIBC patients who underwent cystectomy after CRT. CRT response was assessed through histologic evaluation of cystectomy specimens. Two radiologists determined the volume of interest for the index lesions on ADC maps of pretherapeutic 1.5-T MRI and performed TA using the LIFEx software. Forty-six texture features (TFs) were selected based on their contribution to the prediction of CRT sensitivity. To evaluate diagnostic performance, diagnostic models from the selected TFs were created using random forest (RF) and support vector machine (SVM), respectively.

RESULTS

Twenty-three patients achieved pathologic complete response (pCR) to CRT. The feature selection identified first quartile ADC (Q1 ADC), gray-level co-occurrence matrix (GLCM) correlation, and GLCM homogeneity as important in predicting CRT response. Patients who achieved pCR showed significantly lower Q1 ADC and GLCM correlation values (0.66 × 10 mm/s and 0.53, respectively) than those who did not (0.81 × 10 mm/s and 0.70, respectively; p < 0.05 for both). The AUCs of the RF and SVM models incorporating the selected TFs were 0.82 (95% confidence interval [CI]: 0.67-0.97) and 0.96 (95% CI: 0.91-1.00), respectively, and the AUC of the SVM model was better than that of the mean ADC value (0.76, 95% CI: 0.61-0.90; p = 0.0037).

CONCLUSION

TFs can serve as imaging biomarkers in MIBC patients for predicting CRT sensitivity. TAs of ADC maps can potentially optimize patient selection for CRT.

KEY POINTS

• Texture analysis of ADC maps and feature selection identified important texture features for classifying pathologic tumor response in patients with muscle-invasive bladder cancer. • The machine learning model incorporating the texture features set, which included first quartile ADC, GLCM correlation, and GLCM homogeneity, showed high performance in predicting chemoradiotherapy response. • Texture features could serve as imaging biomarkers that optimize eligible patient selection for chemoradiotherapy in muscle-invasive bladder cancer.