Computer-aided Detection of Prostate Cancer in Multi-parametric Magnetic Resonance Imaging

PURPOSE Accurate reporting of multi-parametric prostate magnetic resonance imaging (mpMRI) is difficult and requires substantial experience. We investigate the effect of computer-aided diagnosis (CAD) on the diagnostic accuracy of prostate MRI reporting. METHOD AND MATERIALS Two consecutive cohorts of patients were used. One for training/development of the CAD system (347 patients) and one for the prospective evaluation (130 patients). Both cohorts comprise mpMRI and subsequent MR-guided biopsy and pathology. The mpMRIs were ESUR guideline compliant and performed on a Siemens 3T MRI without the use of an endo-rectal coil. Both cohorts were prospectively reported by one of ten radiologists according to the PI-RADS guidelines. Experience of the radiologists ranged from inexperienced to very experienced (1-20 years). The computer-aided diagnosis (CAD) system comprised of a voxel classification stage and a subsequent candidate segmentation and classification stage. Features include quantified T2, ADC, pharmacokinetics, texture and anatomical characteristics. ROC and FROC analysis was used to evaluate performance. For the prospective validation the CAD system assigned a score to each radiologist-identified lesion. Logistic regression combining the radiologist and CAD scores was used to emulate independent, prospective CAD reading. Subsequently, the diagnostic performance in detecting intermediate-to-high-grade cancer of the CAD system alone, the radiologist alone and the radiologist CAD-system combination was evaluated using sensitivity and specificity for the different PI-RADS thresholds. Bootstrapping was used to assess significance. RESULTS FROC analyses showed that the CAD system could detect 82% of all intermediate-to-high-grade lesions at 1 false positive per case. Combined CAD and radiologist score significantly improved the sensitivity at a PI-RADS 4 threshold over the radiologist alone (0.98 for the combination, 0.93 for the radiologist alone, p = 0.029). A significantly improved specificity was found at a PI-RADS threshold of 3 (0.25 versus 0.09, p = 0.013). CONCLUSION CAD can achieve excellent performance. As a second observer to characterize prostate lesions it can improve sensitivity and specificity in discriminating intermediate-to-high-grade cancer. CLINICAL RELEVANCE/APPLICATION Improving the performance of mpMRI in the detection of prostate cancer by CAD can prevent unnecessary biopsies.