Introduction: Neoadjuvant chemotherapy (NAC) for muscle-invasive bladder cancer (MIBC) improves overall survival, but pathological response rates are low. Predictive biomarkers could select those patients most likely to benefit from NAC. Radiomics technology offers a novel, non-invasive method to identify predictive biomarkers. Our study aimed to develop a predictive radiomics signature for response to NAC in MIBC.
Methods: An institutional bladder cancer database was used to identify MIBC patients who were treated with NAC followed by radical cystectomy. Patients were classified into responders and non-responders based on pathological response. Bladder lesions on computed tomography images taken prior to NAC were contoured. Extracted radiomics features were used train a radial basis function support vector machine classifier to learn a prediction rule to distinguish responders from non-responders. The discriminative accuracy of the classifier was then tested using a nested 10-fold cross-validation protocol.
Results: Nineteen patients who underwent NAC followed by radical cystectomy were found to be eligible for analysis. Of these, nine (48%) patients were classified as responders and 10 (52%) as non-responders. Nineteen bladder lesions were contoured. The sensitivity, specificity and discriminative accuracy were 52.9±9.4%, 69.4±8.6%, and 62.1±6.1%, respectively. This corresponded to an area under the curve of 0.63±0.08 (p=0.20).
Conclusions: Our developed radiomics signature demonstrated modest discriminative accuracy; however, these results may have been influenced by small sample size and heterogeneity in image acquisition. Future research using novel methods for computer-based image analysis on a larger cohort of patients is warranted.
Development of a radiomic signature for predicting response to neoadjuvant chemotherapy in muscle-invasive bladder cancer