Neoadjuvant immune-checkpoint inhibitors (ICIs) in muscle-invasive bladder cancer (MIBC) were tested in patient's ineligible for cisplatin-based chemotherapy. The PURE-01 trial (NCT02736266) evaluated three courses of pembrolizumab before radical cystectomy (RC). We developed AI-MIRACLE, an international study assessing artificial intelligence (AI) and multiparametric magnetic resonance imaging (mpMRI) for predicting treatment response.
This multi-institutional study included data acquisition in Italy, and centralized analysis in the United States. Among 112 PURE-01 patients, pre- and post-ICI MRIs were analyzed. T2-weighted signal intensities were standardized for radiomics (Image Biomarker Standardization Initiative-compatible Python-based Computational Environment for Radiological Research (pyCERR)) and deep feature extraction (AI-BLADE toolbox using VGG19). Diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI data underwent model-based analysis. Supervised machine learning algorithms (elastic net, random forest) were trained and cross-validated to predict pathological major response (pMR:ypT<2N0 residual disease) and pathological complete response (pCR: ypT0) pathological response.
The predictive models using post-ICI mpMRI with either a combination of radiomics and DCE-derived features or radiomics alone achieved the same high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.96 for pMR. A shape-based radiomic model achieved an AUC of 0.86 for predicting pCR. These models outperformed benchmark models based on clinical predictors.
Shape-based radiomics, DCE-derived features, and deep features may serve as noninvasive imaging biomarkers for predicting response to neoadjuvant pembrolizumab in MIBC. This imaging-based approach provides a non-invasive assessment of treatment response following neoadjuvant immunotherapy, which may help inform bladder-preserving management decisions prior to definitive surgery.
European urology oncology. 2026 May 29 [Epub ahead of print]
Andrea Necchi, Giorgio Brembilla, Karissa Whiting, Yuki Arita, Oguz Akin, Aditya Apte, Muhammad Awais, Alfonso Lema-Dopico, Ramesh Paudyal, Michele Cosenza, Brigida Anna Maiorano, Valentina Tateo, Antonio Cigliola, Chiara Mercinelli, Francesco De Cobelli, Marinela Capanu, Amita Shukla-Dave, Lawrence H Schwartz
Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy; Department of Medical Oncology, Vita-Salute San Raffaele University, Milan, Italy. Electronic address: ., Department of Medical Oncology, Vita-Salute San Raffaele University, Milan, Italy; Department of Radiology, IRCCS San Raffaele Hospital, Milan, Italy., Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA., Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA., Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA., Department of Radiology, IRCCS San Raffaele Hospital, Milan, Italy., Department of Medical Oncology, IRCCS San Raffaele Hospital, Milan, Italy., Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA. Electronic address: .