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A multimodal deep learning radiomics model for predicting degenerative meniscus tear after arthroscopy
Yao He1,2,3,4, Jiaying Wei2,3,4, Yinsong Sun2,3,4
1Department of Orthopedics, Banan Hospital of Chongqing Medical University, Chongqing, China.
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.
Insights
A new multimodal deep learning radiology (MDLR) model integrates MRI data and clinical factors to predict outcomes after arthroscopic partial meniscectomy for degenerative meniscus tears. This tool improves prognostic accuracy, aiding clinical decision-making for patient care.
Area of Science:
- Orthopedics
- Radiology
- Artificial Intelligence
Background:
- Degenerative meniscus tears often coexist with osteoarthritis, complicating prognosis after arthroscopic partial meniscectomy (APM).
- Predicting patient outcomes following APM is challenging due to these comorbidities.
Purpose of the Study:
- To develop and validate a multimodal deep learning radiology (MDLR) model.
- To integrate deep learning radiology (DLR) scores from MRI with clinical variables for improved prognostic accuracy.
Main Methods:
- Retrospective enrollment of 452 patients undergoing APM for degenerative meniscus tears (February 2020 - February 2022).
- Extraction of DLR features from knee MRI scans.
- Development of an MDLR model incorporating DLR signatures and clinical variables for prognostic risk stratification.
Main Results:
- A standalone DLR model showed limited predictive performance (ROC curves ranging from 0.720-0.780).
- Multivariate analysis identified key prognostic indicators: gender, height, weight, pain duration, ESR, and VAS.
- The integrated MDLR model, particularly using Light Gradient Boosting Machine, significantly improved performance (ROC curves up to 0.951) and stratified patients into four prognostic levels.
Conclusions:
- The developed MDLR tool effectively stratifies prognosis for APM in degenerative meniscus tear patients.
- Pain exacerbation time is a crucial prognostic factor identified by the MDLR model.
- MDLR demonstrates significant potential to assist physicians in therapeutic decision-making and surveillance strategies.
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