EuSoMII Virtual Annual Meeting 2020 Book of Abstracts

摘要

Short Summary: A new artificial intelligence (AI)-based system, designed to support clinical interpretation of prostate MRI, shows promising performance compared to radiology studies and CAD/AI literature. Purpose/Objectives: Investigate whether AI could improve specificity of patient selection for biopsy and biopsy target identification, and assist segmentation, in the prostate cancer diagnostic pathway. Methods and materials: A multi-stage AI-based system was developed for MRI analysis, and trained with the NCI-ISBI 2013 Challenge, PROMISE12 and PROSTATEx datasets, split into training, validation and held-out test sets. Clinically significant prostate cancer (csPCa) was defined as Gleason≥3+4 disease. Accuracy metrics were computed on validation and held-out test sets, and compared with literature on prostate MRI studies and CAD/AI models. Results: To support patient selection for biopsy as a rule-out test, sensitivity identifying patients with csPCa was 93% (95% CI 82-100%), specificity 76% (64-87%), NPV 95% (88-100%), and AUC 0.92 (0.84-0.98), evaluated with biparametric MRI (bpMRI) data from the combined PROSTATEx validation and held-out test sets (prevalence 35%, 80 patients). Performance was higher on the held-out test set (40 patients). Similar AI/CAD publications report 93% sensitivity using held-out/blinded data at specificity between 6%-42%. In major studies, radiologists’ sensitivity per-patient was 88-93%, specificity 18-68%, and NPV 76-97%, with Likert/PI-RADS ≥3 defined as positive. Note that methodological and dataset differences and test set size limit comparisons. For identifying biopsy targets, the AI system detected csPCa lesions with per-lesion sensitivity 94% (85-100%), specificity 71% (61-89%), NPV 97% (93-100%), and AUC 0.89 (0.83-0.95), in the same combined PROSTATEx development validation/test set (128 lesions, 80 patients). Performance was higher on the held-out test set. For prostate gland segmentation to support analysis, PSA density evaluation, and fusion biopsy, the system showed 92% average Dice score, against radiologist ground-truth segmentations, on held-out test cases from the PROMISE12 dataset (10 patients). This is comparable to the state- of-the-art. The AI system performed similarly in the above tasks when evaluated using multiparametric MRI (mpMRI) data. Conclusion: The results suggest the AI system has promising specificity, sensitivity and NPV to help csPCa-free patients avoid biopsy, to support biopsy targeting, and to provide high-quality automated segmentations. Disclosure: AW Rix and E Sala co-founded Lucida Medical, the company developing this AI system. While regulatory approvals are in progress, the system is not currently available and is described here in retrospective research use.