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Background Multiparametric magnetic resonance imaging (mpMRI) for prostate cancer detection without careful patient selection may lead to excessive resource utilization and costs. Objective To develop and validate a clinical tool for predicting the presence of high-risk lesions on mpMRI. Design, setting, and participants Four tertiary care centers were included in this retrospective and prospective study (BiRCH Study Collaborative). Statistical models were generated using 1269 biopsy-naive, prior negative biopsy, and active surveillance patients who underwent mpMRI. Using age, prostate-specific antigen, and prostate volume, a support vector machine model was developed for predicting the probability of harboring Prostate Imaging Reporting and Data System 4 or 5 lesions. The accuracy of future predictions was then prospectively assessed in 214 consecutive patients. Outcome measurements and statistical analysis Receiver operating characteristic, calibration, and decision curves were generated to assess model performance. Results and limitations For biopsy-naive and prior negative biopsy patients (n = 811), the area under the curve (AUC) was 0.730 on internal validation. Excellent calibration and high net clinical benefit were observed. On prospective external validation at two separate institutions (n = 88 and n = 126), the machine learning model discriminated with AUCs of 0.740 and 0.744, respectively. The final model was developed on the Microsoft Azure Machine Learning platform (birch.azurewebsites.net). This model requires a prostate volume measurement as input. Conclusions In patients who are naive to biopsy or those with a prior negative biopsy, BiRCH models can be used to select patients for mpMRI. Patient summary In this multicenter study, we developed and prospectively validated a calculator that can be used to predict prostate magnetic resonance imaging (MRI) results using patient age, prostate-specific antigen, and prostate volume as input. This tool can aid health care professionals and patients to make an informed decision regarding whether to get an MRI.

Twenty-three cadaver forearm specimens were mounted on a Plexiglas acrylic plastic base for study of the trapeziometacarpal joint. Extrinsic and intrinsic motors were loaded to simulate lateral pinch, and pressure-sensitive film was used to record joint contact patterns in various functional positions. The palmar compartment of the trapeziometacarpal joint was the primary contact area during flexion adduction of the thumb ray in lateral pinch. Simulation of dynamic pinch and release produced dorsal enlargement of the contact pattern, suggesting physiologic translation of the metacarpal on the trapezium. Detachment of the palmar beak ligament resulted in dorsal translation of the contact area, producing a pattern similar to that of cartilage degeneration seen in the osteoarthritic joint. End-stage osteoarthritic specimens had a nonfunctional beak ligament and demonstrated a pathologic total contact pattern of joint congruity that was unaffected by further manipulations of the beak ligament. This work defines the contact patterns in the normal and diseased trapeziometacarpal joint and establishes the important influence of the palmar beak ligament on those contact areas.