Development of a radiomic signature for predicting response to neoadjuvant chemotherapy in muscle-invasive bladder cancer

Abstract

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.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

“…S. J. Choi et al developed a radiomic-based model for predicting the response of MIBC patients to neoadjuvant chemotherapy (NAC), achieving an AUC value of 0.75 for the validation set [ 147 ]. In a similar line, A. Parmar et al used a predictive radiomic signature for MIBC patients’ response to NAC, reaching an AUC value of 0.63 in terms of discriminating the patients into responders and non-responders [ 148 ]. These findings indicate that our model’s predictive performance is satisfactory.…”

Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder cancer

“…Five studies mainly used MVAC as NAC. Seven studies considered ypT0 as responders, and one study (21) considered ≤ypT1 as responders. The accuracy of the algorithms ranged from 0.69 +/-0.08 to 0.80 +/-0.04.…”

“…Suartz et al AI for predicting response to neoadjuvant chemotherapy in MIBC 8 © 2024 Canadian Urological Association the response to neoadjuvant chemotherapy in muscle-invasive bladder cancer before any treatment (21).…”

Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies

“…However, this disease management significantly impacts the quality of life (QOL), as RC affects continence, sexual function, fertility, and bowel function [98]. Predictive biomarkers are critical to identifying patients who will respond to NAC so that potential toxicities from cytotoxic chemotherapy can be limited in patients who are unlikely to derive benefit [99]. Emerging immunotherapy alternatives include the development of antibodies directly targeting tumor cells, ICI antibodies, and chimeric antigen receptor T-cell therapies [13].…”

“…Radiomic features can further quantify the spatial heterogeneity of mpMRI metrics and be useful in predicting treatment response to NAC in patients with MIBC, providing a decision-support tool for personalized management [99]. The mpMRI-based radiomics nomogram has shown the potential to be a noninvasive tool for quantitatively predicting tumor response to NAC in patients with MIBC [26].…”