A multimodal deep learning radiomics model for predicting degenerative meniscus tear after arthroscopy

Abstract

Background Degenerative meniscus tears are often accompanied by varying degrees of osteoarthritis, making the prognostic outcome of arthroscopic partial meniscectomy (APM) difficult to predict. Our research objective is to develop and validate a multimodal deep learning radiology (MDLR) model based on the integration of multimodal data using deep learning radiology (DLR) scores from preoperative magnetic resonance imaging (MRI) images and clinical variables. Materials and methods From February 2020 to February 2022, 452 eligible patients with degenerative meniscus tear who underwent APM were retrospectively enrolled in cohorts. DLR features were extracted from MRI of the patient’s knee. Then, an MDLR model was used for the patient prognosis after arthroscopy. The MDLR model for prognostic risk stratification incorporated DLR signatures and clinical variable. Results The standalone DLR model performed poorly, with a micro average receiver operating characteristic (ROC) curve and macro average ROC line of 0.780 and 0.765 in the training set, 0.747 and 0.747 in the validation set, and 0.720 and 0.732 in the test set, respectively, for predicting postoperative outcomes in degenerative meniscus tears. Multivariate analysis identified gender, height, weight, duration of pain, ESR, and VAS as indicators of poor prognosis. After combining the above clinical features, the performance of the MDLR model has been significantly improved, with the best performance achieved under the Light Gradient Boosting Machine (GBM) algorithm. The micro average ROC curve and macro average ROC line of this model for predicting the postoperative effect of degenerative meniscus tear were 0.917 and 0.919 in the training set, 0.874 and 0.882 in the validation set, and 0.921 and 0.951 in the test set, respectively. With these variables, the MDLR model provides four levels of prognosis for arthroscopic partial meniscectomy: Poor, pain relief 0–25%, Average, pain relief 25–50%, Good, pain relief 50–75%, Excellent, pain relief 75–100%. Conclusion A tool based on MDLR was developed to consider that the pain exacerbation time is an important prognosis factor for arthroscopic partial meniscectomy in degenerative meniscus tear patients. MDLR showed outstanding performance for the prognostic efficiency stratification of degenerative meniscus tear patients who underwent arthroscopic partial meniscectomy and may help physicians with therapeutic decision making and surveillance strategy selection in clinical practice.