Improving membranous urethral length measurements on prostate MRI: a comparison of online training methods
摘要
This study compared three online training methods to enhance diagnostic performance, reader confidence, and interreader agreement in membranous urethral length (MUL) measurements.
Materials and methodsNinety-nine study participants were asked to measure the MUL on 20 test cases at the start of the study and rated their confidence level. Participants were then divided semi-randomly into three training groups: Group A (written instructions), Group B (1-month self-study program with case feedback using a dedicated web platform), and Group C (1-day online expert-led case-based training). Groups B and C worked through an identical set of 40 cases. One week after completing training, participants evaluated the same 20 test cases. Diagnostic performance (using expert reference standard), interreader agreement (intraclass correlation coefficient, ICC), and reader confidence levels were compared before and after training. Participants completed a questionnaire evaluating their perceptions of the training.
ResultsEighty-one participants (82%) completed the study. All groups demonstrated significant improvements in diagnostic performance and confidence (p < 0.001), with similar outcomes across training groups. Interreader agreement improved from ICC 0.52–0.56 pre-training to 0.74–0.80 post-training. Questionnaire responses indicated most found the training appropriate; however, 17% of Group A felt it was insufficient, while 30–35% of Group C found the expert-led training too time-consuming.
ConclusionAll training methods—ranging from minimal, with written instructions only, to more comprehensive case-based programs—effectively improved diagnostic performance, reader confidence and interreader agreement for MUL measurements on prostate MRI. Independent online training offers practical advantages over more time-demanding hands-on sessions, supporting broader clinical implementation of MUL-based individualized continence prediction.
Key Points