View in EDS

A pilot study of AI-assisted reading of prostate MRI in Organized Prostate Cancer Testing

Saved in:
Bibliographic Details
Title: A pilot study of AI-assisted reading of prostate MRI in Organized Prostate Cancer Testing
Authors: Thimansson, Erik, Zackrisson, Sophia, Jäderling, Fredrik, Alterbeck, Max, Jiborn, Thomas, Bjartell, Anders, Wallström, Jonas
Contributors: Lund University, Faculty of Medicine, Department of Translational Medicine, Radiology Diagnostics, Malmö, Lunds universitet, Medicinska fakulteten, Institutionen för translationell medicin, Diagnostisk radiologi, Malmö, Originator, Lund University, Profile areas and other strong research environments, Other Strong Research Environments, LUCC: Lund University Cancer Centre, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Övriga starka forskningsmiljöer, LUCC: Lunds universitets cancercentrum, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), EpiHealth: Epidemiology for Health, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), EpiHealth: Epidemiology for Health, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Photon Science and Technology, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Avancerade ljuskällor, Originator, Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Light and Materials, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Ljus och material, Originator, Lund University, Faculty of Medicine, Department of Translational Medicine, Urological cancer, Malmö, Lunds universitet, Medicinska fakulteten, Institutionen för translationell medicin, Urologisk cancerforskning, Malmö, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), eSSENCE: The e-Science Collaboration, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), eSSENCE: The e-Science Collaboration, Originator, Lund University, Faculty of Medicine, Department of Laboratory Medicine, Division of Translational Cancer Research, Lunds universitet, Medicinska fakulteten, Institutionen för laboratoriemedicin, Avdelningen för translationell cancerforskning, Originator
Source: Acta Oncologica. 63:816-821
Subject Terms: Medical and Health Sciences, Clinical Medicine, Radiology and Medical Imaging, Medicin och hälsovetenskap, Klinisk medicin, Radiologi och bildbehandling
Description: Objectives: To evaluate the feasibility of AI-assisted reading of prostate magnetic resonance imaging (MRI) in Organized Prostate cancer Testing (OPT). Methods: Retrospective cohort study including 57 men with elevated prostate-specific antigen (PSA) levels ≥3 µg/L that performed bi-parametric MRI in OPT. The results of a CE-marked deep learning (DL) algorithm for prostate MRI lesion detection were compared with assessments performed by on-site radiologists and reference radiologists. Per patient PI-RADS (Prostate Imaging-Reporting and Data System)/Likert scores were cross-tabulated and compared with biopsy outcomes, if performed. Positive MRI was defined as PI-RADS/Likert ≥4. Reader variability was assessed with weighted kappa scores. Results: The number of positive MRIs was 13 (23%), 8 (14%), and 29 (51%) for the local radiologists, expert consensus, and DL, respectively. Kappa scores were moderate for local radiologists versus expert consensus 0.55 (95% confidence interval [CI]: 0.37–0.74), slight for local radiologists versus DL 0.12 (95% CI: −0.07 to 0.32), and slight for expert consensus versus DL 0.17 (95% CI: −0.01 to 0.35). Out of 10 cases with biopsy proven prostate cancer with Gleason ≥3+4 the DL scored 7 as Likert ≥4. Interpretation: The Dl-algorithm showed low agreement with both local and expert radiologists. Training and validation of DL-algorithms in specific screening cohorts is essential before introduction in organized testing.
Access URL: https://doi.org/10.2340/1651-226X.2024.40475
Database: SwePub
Description
Abstract:Objectives: To evaluate the feasibility of AI-assisted reading of prostate magnetic resonance imaging (MRI) in Organized Prostate cancer Testing (OPT). Methods: Retrospective cohort study including 57 men with elevated prostate-specific antigen (PSA) levels ≥3 µg/L that performed bi-parametric MRI in OPT. The results of a CE-marked deep learning (DL) algorithm for prostate MRI lesion detection were compared with assessments performed by on-site radiologists and reference radiologists. Per patient PI-RADS (Prostate Imaging-Reporting and Data System)/Likert scores were cross-tabulated and compared with biopsy outcomes, if performed. Positive MRI was defined as PI-RADS/Likert ≥4. Reader variability was assessed with weighted kappa scores. Results: The number of positive MRIs was 13 (23%), 8 (14%), and 29 (51%) for the local radiologists, expert consensus, and DL, respectively. Kappa scores were moderate for local radiologists versus expert consensus 0.55 (95% confidence interval [CI]: 0.37–0.74), slight for local radiologists versus DL 0.12 (95% CI: −0.07 to 0.32), and slight for expert consensus versus DL 0.17 (95% CI: −0.01 to 0.35). Out of 10 cases with biopsy proven prostate cancer with Gleason ≥3+4 the DL scored 7 as Likert ≥4. Interpretation: The Dl-algorithm showed low agreement with both local and expert radiologists. Training and validation of DL-algorithms in specific screening cohorts is essential before introduction in organized testing.
ISSN:0284186X
DOI:10.2340/1651-226X.2024.40475