Background: Current clinical staging methods are unable to accurately define the extent of invasion of localized bladder cancer, which affects the proper use of systemic therapy, surgery, and radiation. Our purpose was to test a novel radiomics approach to identify optimal imaging biomarkers from T2-weighted magnetic resonance imaging (MRI) scans that accurately classify localized bladder cancer into 2 tumor stage groups (≤T2 vs >T2) at both the patient level and within bladder subsectors. Method and Materials: Preoperative T2-weighted MRI scans of 65 consecutive patients followed by radical cystectomy were identified. A 3-layer, shell-like volume of interest (VOI) was defined on each MRI slice: Inner (lumen), middle (bladder wall), and outer (perivesical tissue). An optimal biomarker method was used to identify features from 15,834 intensity and texture properties that maximized the classification of patients into ≤T2 versus >T2 groups. A leave-one-out strategy was used to cross-validate the performance of the identified biomarker feature set at the patient level. The performance of the feature set was then evaluated at the subsector level of the bladder by dividing the VOIs into 8 radial sectors. Results: A total of 9 optimal biomarker features were derived and demonstrated a sensitivity, specificity, accuracy of prediction, and area under a receiver operating characteristic curve of 0.742, 0.824, 0.785, and 0.806, respectively, at the patient level and 0.681, 0.788, 0.763, and 0.813, respectively, at the radial sector level. All 9 selected features were extracted from the middle shell of the VOI and based on texture properties. Conclusions: An approach to select a small, highly independent feature set that is derived from T2-weighted MRI scans that separate patients with bladder cancer into ≤T2 versus >T2 groups at both the patient level and within subsectors of the bladder has been developed and tested. With external validation, this radiomics approach could improve the clinical staging of bladder cancer and optimize therapeutic management.