Deep learning-assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge

Matin Hosseinzadeh; Anindo Saha; Patrick Brand; Ilse Slootweg; Maarten de Rooij; Henkjan Huisman

doi:10.1007/s00330-021-08320-y

Deep learning-assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge

Eur Radiol. 2022 Apr;32(4):2224-2234. doi: 10.1007/s00330-021-08320-y. Epub 2021 Nov 16.

Authors

Matin Hosseinzadeh¹, Anindo Saha¹, Patrick Brand¹, Ilse Slootweg¹, Maarten de Rooij¹, Henkjan Huisman²

Affiliations

¹ Diagnostic Image Analysis Group, Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands.
² Diagnostic Image Analysis Group, Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands. henkjan.huisman@radboudumc.nl.

Abstract

Objectives: To assess Prostate Imaging Reporting and Data System (PI-RADS)-trained deep learning (DL) algorithm performance and to investigate the effect of data size and prior knowledge on the detection of clinically significant prostate cancer (csPCa) in biopsy-naïve men with a suspicion of PCa.

Methods: Multi-institution data included 2734 consecutive biopsy-naïve men with elevated PSA levels (≥ 3 ng/mL) that underwent multi-parametric MRI (mpMRI). mpMRI exams were prospectively reported using PI-RADS v2 by expert radiologists. A DL framework was designed and trained on center 1 data (n = 1952) to predict PI-RADS ≥ 4 (n = 1092) lesions from bi-parametric MRI (bpMRI). Experiments included varying the number of cases and the use of automatic zonal segmentation as a DL prior. Independent center 2 cases (n = 296) that included pathology outcome (systematic and MRI targeted biopsy) were used to compute performance for radiologists and DL. The performance of detecting PI-RADS 4-5 and Gleason > 6 lesions was assessed on 782 unseen cases (486 center 1, 296 center 2) using free-response ROC (FROC) and ROC analysis.

Results: The DL sensitivity for detecting PI-RADS ≥ 4 lesions was 87% (193/223, 95% CI: 82-91) at an average of 1 false positive (FP) per patient, and an AUC of 0.88 (95% CI: 0.84-0.91). The DL sensitivity for the detection of Gleason > 6 lesions was 85% (79/93, 95% CI: 77-83) @ 1 FP compared to 91% (85/93, 95% CI: 84-96) @ 0.3 FP for a consensus panel of expert radiologists. Data size and prior zonal knowledge significantly affected performance (4%, [Formula: see text]).

Conclusion: PI-RADS-trained DL can accurately detect and localize Gleason > 6 lesions. DL could reach expert performance using substantially more than 2000 training cases, and DL zonal segmentation.

Key points: • AI for prostate MRI analysis depends strongly on data size and prior zonal knowledge. • AI needs substantially more than 2000 training cases to achieve expert performance.

Keywords: Artificial intelligence; Deep learning; Prostate cancer; Radiology.

MeSH terms

Deep Learning*
Humans
Image-Guided Biopsy
Magnetic Resonance Imaging / methods
Male
Multiparametric Magnetic Resonance Imaging*
Prostate / pathology
Prostatic Neoplasms* / pathology
Retrospective Studies