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Zheng Fan,1 Tong Wu,1 Yang Wang,2 Zhuoru Jin,1 Tong Wang,3 Da Liu1 1Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, People’s Republic of China; 2Department of Orthopedics, China Medical University Shenyang Fourth People’s Hospital, Shenyang, People’s Republic of China; 3Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, People’s Republic of ChinaCorrespondence: Da Liu, Department of Orthopedics, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, 110004, People’s Republic of China, Email spinecmu@163.comObjective: The aim of this study is to develop and validate a deep-learning radiomics model for predicting surgical risk factors for lumbar disc herniation (LDH) in young patients to assist clinicians in identifying surgical candidates, alleviating symptoms, and improving prognosis.Methods: A retrospective analysis of patients from two medical centers was conducted. From sagittal and axial MR images, the regions of interest were handcrafted to extract radiomics features. Various machine-learning algorithms were employed and combined with clinical features, resulting in the development of a deep-learning radiomics nomogram (DLRN) to predict surgical risk factors for LDH in young adults. The efficacy of the different models and the clinical benefits of the model were compared.Results: We derived six sets of features, including clinical features, radiomics features (Rad_SAG and Rad_AXI) and deep learning features (DL_SAG and DL_AXI) from sagittal and axial MR images, as well as fused deep-learning radiomics (DLR) features. The support vector machine(SVM) algorithm exhibited the best performance. The area under the curve (AUC) of DLR in the training and testing cohorts of 0.991 and 0.939, respectively, were significantly better than those of the models developed with radiomics(Rad_SAG=0.914 and 0.863, Rad_AXI=0.927 and 0.85) and deep-learning features(DL_SAG=0.959 and 0.818, DL_AXI=0.960 and 0.811). The AUC of DLRN coupled with clinical features(ODI, Pfirrmann grade, SLRT, MMFI, and MSU classification) were 0.994 and 0.941 in the training and testing cohorts, respectively. Analysis of the calibration and decision curves demonstrated good agreement between the predicted and observed outcomes, and the use of the DLRN to predict the need for surgical treatment of LDH demonstrated significant clinical benefits.Conclusion: The DLRN established based on clinical and DLR features effectively predicts surgical risk factors for LDH in young adults, offering valuable insights for diagnosis and treatment.Keywords: deep learning, radiomics, nomogram, lumbar disc herniation, surgical treatment, young adults |
