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Updated: Sep 19, 2025
Novel Mini-open Transforaminal Lumbar Interbody Fusion
Published on: June 6, 2025
A Study on Predicting the Efficacy of Posterior Lumbar Interbody Fusion Surgery Using a Deep Learning Radiomics Model
Liguang Fang1, Yingyi Pan1, Haige Zheng2
1Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics Guangdong Province), Guangzhou 510630, China (L.F., Y.P., W.Z., J.L., Q.Z.).
Rationale And Objectives:
This study seeks to develop a combined model integrating clinical data, radiomics, and deep learning (DL) for predicting the efficacy of posterior lumbar interbody fusion (PLIF) surgery.
Materials And Methods:
A retrospective review was conducted on 461 patients who underwent PLIF for degenerative lumbar diseases. These patients were partitioned into a training set (n=368) and a test set (n=93) in an 8:2 ratio. Clinical models, radiomics models, and DL models were constructed based on logistic regression and random forest, respectively. A combined model was established by integrating these three models. All radiomics and DL features were extracted from sagittal T2-weighted images using 3D slicer software. The least absolute shrinkage and selection operator method selected the optimal radiomics and DL features to build the models. In addition to analyzing the original region of interest (ROI), we also conducted different degrees of mask expansion on the ROI to determine the optimal ROI. The performance of the model was evaluated by using the receiver operating characteristic curve (ROC) and the area under the ROC curve. The differences in AUC were compared by DeLong test.
Results:
Among the clinical characteristics, patient age, body weight, and preoperative intervertebral distance at the surgical segment are risk factors affecting the fusion outcome. The radiomics model based on MRI with expanded 10 mm mask showed excellent performance (training set AUC=0.814, 95% CI: (0.761-0.866); test set AUC=0.749, 95% CI: [0.631-0.866]). Among all single models, the DL model had the best diagnostic prediction performance, with AUC values of (0.995, 95% CI: [0.991-0.999]) for the training set and (0.803, 95% CI: [0.705-0.902]) for the test set. Compared to all the models, the combined model of clinical features, radiomics features, and DL features had the best diagnostic prediction performance, with AUC values of (0.993, 95% CI: [0.987-0.999]) for the training set and (0.866, 95% CI: [0.778-0.955]) for the test set.
Conclusion:
The proposed clinical feature-deep learning radiomics model can effectively predict the postoperative efficacy of patients undergoing PLIF surgery and has good clinical applicability.
