MRI-Based Prediction of Meniscal Tear Repairability Demonstrates Limited Accuracy and Reliability

<b>Background:</b> While magnetic resonance imaging (MRI) is commonly used to identify meniscal tears, intraoperative assessment typically dictates repairability. This study evaluated whether a simplified MRI-based scoring system could reliably predict meniscal repair versus meniscectomy. <b>Methods:</b> Patients who underwent meniscectomy or meniscal repair between 2010 and 2018 were retrospectively identified. Preoperative MRIs were independently reviewed in a blinded fashion by two radiologists and one orthopedic sports surgeon. Reviewers scored images based on four arthroscopic criteria for tear repairability, with one point awarded for each of the following criteria-(1) proximity within 4 mm of the meniscosynovial junction, (2) length > 10 mm, (3) presence of intact inner meniscal segment, and (4) >50% meniscal thickness. Tears scoring four points were considered repairable. Accuracy, sensitivity, and positive and negative predictive values were calculated against the actual procedure performed. Inter- and intraobserver reliability were evaluated using kappa statistics. The predictive performance of each individual criterion was also analyzed. <b>Results:</b> A total of 202 meniscal tears were included (134 meniscectomies and 68 repairs). Reviewer accuracy in predicting repairability ranged from 48% to 76%. Intraobserver reliability was moderate to substantial (κ = 0.42-0.66), whereas interobserver reliability was poor to moderate (pairwise κ = 0.07-0.43; Fleiss' κ = 0.11). Analysis of individual MRI criteria demonstrated limited predictive value, with most criteria achieving less than 50% accuracy across reviewers. <b>Conclusions:</b> MRI-based prediction of meniscal repairability using arthroscopic criteria demonstrated limited accuracy and poor interobserver reliability. Overall predictive reliability remains insufficient for clinical decision-making. Further investigation, integrating advanced imaging techniques and artificial intelligence, may improve the preoperative assessment of meniscal repairability.