Radiomic Features on Prostatic Multiparametric Magnetic Resonance Imaging Enable Progression Risk in Patients on Active Surveillance: A Pilot Study
Author(s): Angelo Totaro, Valerio Di Paola, Marco Campetella, Eros Scarciglia, Luca Boldrini, Riccardo Manfredi, Pierfrancesco Bassi, Lorenzo Elia, Riccardo Gigli, Federica Perillo, Davide Cusamano, Antonio Cretì, Sebastiano Vocino, Francesco Pinto, Emilio Sacco
Purpose: Active Surveillance (AS) for not clinically significant prostate cancer (ncs-PCa) has increased worldwide in the last 10 years. However, about 30-40% patients experience progression to clinically significant prostate cancer (csPCa). To date, there is no validated protocol for followup of AS patients. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. Quantitative radiomics allows for the extraction of more advanced shape features. Our aim was to assess which shape features derived from MRI index lesions correlate with risk progression to csPCa, in patients on AS.
Materials and Methods: We retrospectively enrolled 55 patients’ ncs-PCa, according to Epstein criteria. All patients underwent internal AS protocol: PSA every 3 months; MRI at 9 months and then yearly; confirmatory biopsy at 12 months. Diagnosis of ISUP GG>2 or highvolume ISUP GG1 disease (defined as increase >20% volume disease from baseline biopsy) on confirmatory biopsy were considered as progression to csPCa. All MRIs were evaluated and manually contoured by 2 expert radiologists at our institution. Statistical analysis evaluated 270 radiomic features, extracted from lesion index after tridimensional segmentation. Predictive models were created with the best performing radiomic features and clinical variables, considering the area under the Receiver Operating Characteristic (ROC) curve (AUC).
Results: Progression rate to csPCa was 40% (22/55), at median follow up of 2 years. Univariate analysis showed 10 radiomic features that were found to be significantly associated to progression to csPCa (5 extracted from DWI and 5 from T2w MR maps). The predictive model with the two best performing radiomic features showed an AUC of 0.76 (95% CI of 0.62-0.89) in predicting progression to csPCa, with a sensitivity of 76.2% and a specificity of 66.7%. On multivariate analysis, age