Performance of Deep Learning and Genitourinary Radiologists in Detection of Prostate Cancer Using 3-T Multiparametric Magnetic Resonance Imaging

Ruiming Cao, Xinran Zhong, Sohrab Afshari, Ely Felker, Voraparee Suvannarerg, Teeravut Tubtawee, Sitaram Vangala, Fabien Scalzo, Steven Raman, Kyunghyun Sung

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Background: Several deep learning-based techniques have been developed for prostate cancer (PCa) detection using multiparametric magnetic resonance imaging (mpMRI), but few of them have been rigorously evaluated relative to radiologists' performance or whole-mount histopathology (WMHP). Purpose: To compare the performance of a previously proposed deep learning algorithm, FocalNet, and expert radiologists in the detection of PCa on mpMRI with WMHP as the reference. Study Type: Retrospective, single-center study. Subjects: A total of 553 patients (development cohort: 427 patients; evaluation cohort: 126 patients) who underwent 3-T mpMRI prior to radical prostatectomy from October 2010 to February 2018. Field Strength/Sequence: 3-T, T2-weighted imaging and diffusion-weighted imaging. Assessment: FocalNet was trained on the development cohort to predict PCa locations by detection points, with a confidence value for each point, on the evaluation cohort. Four fellowship-trained genitourinary (GU) radiologists independently evaluated the evaluation cohort to detect suspicious PCa foci, annotate detection point locations, and assign a five-point suspicion score (1: least suspicious, 5: most suspicious) for each annotated detection point. The PCa detection performance of FocalNet and radiologists were evaluated by the lesion detection sensitivity vs. the number of false-positive detections at different thresholds on suspicion scores. Clinically significant lesions: Gleason Group (GG) ≥ 2 or pathological size ≥ 10 mm. Index lesions: the highest GG and the largest pathological size (secondary). Statistical Tests: Bootstrap hypothesis test for the detection sensitivity between radiologists and FocalNet. Results: For the overall differential detection sensitivity, FocalNet was 5.1% and 4.7% below the radiologists for clinically significant and index lesions, respectively; however, the differences were not statistically significant (P = 0.413 and P = 0.282, respectively). Data Conclusion: FocalNet achieved slightly lower but not statistically significant PCa detection performance compared with GU radiologists. Compared with radiologists, FocalNet demonstrated similar detection performance for a highly sensitive setting (suspicion score ≥ 1) or a highly specific setting (suspicion score = 5), while lower performance in between. Level of Evidence: 3. Technical Efficacy Stage: 2.

Original languageEnglish (US)
Pages (from-to)474-483
Number of pages10
JournalJournal of Magnetic Resonance Imaging
Volume54
Issue number2
DOIs
StatePublished - Aug 2021

Keywords

  • automatic cancer detection
  • deep learning
  • multiparametric MRI
  • prostate cancer

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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