Abstract Background Magnetic resonance imaging (MRI) is widely used for the evaluation of knee injuries, however, the accuracy of MRI in classifying multiple ligament knee injuries (MLKIs) remains unknown. This study aimed to investigate the accuracy of MRI in diagnosing and classifying acute traumatic MLKIs, we hypothesize that MRI had high accuracy in detecting and classifying MLKIs. Methods The clinical data of 97 patients who were diagnosed with acute traumatic MLKIs and managed by multi-ligament reconstruction between 2012 and 2020 were retrospectively reviewed. The MR images were read by two experienced radiologists and results were compared with intraoperative findings, which were considered as the reference for the identification of injured structures. The value of MRI in detecting injuries of anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), medial collateral ligament (MCL), lateral collateral ligament (LCL), and meniscus was evaluated by calculating the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and kappa coefficients analysis. The value of MRI in classifying MLKIs was evaluated by calculating the agreement between MRI and intraoperative findings. Results For detecting the specific injured structures in MLKIs, MRI had high sensitivity (90.7% for ACL, 90.4% for PCL, and moderate specificity (63.6% for ACL, 50% for PCL) in detecting cruciate ligament injuries, moderate sensitivity (79.1% for MCL, 55.6% for LCL) and specificity (46.7% for MCL, 68.4% for LCL) in detecting collateral ligament injuries, fair sensitivity (61.5%) and low specificity (39.4%) in the diagnosis of injuries to the meniscus. For classifying the MIKIs, MRI had a moderate agreement with intraoperative findings in classifying KD-V (kappa value = 0.57), poor agreement in the KD-I (kappa value = 0.39) and KD-IIIM (kappa value = 0.31), meaningless in the KD-II and KD-IIIL (kappa value
Find this article in full text from Springer Verlag. 
Full Text Finder