MRI-based radiomics nomogram for differentiation of solitary metastasis and solitary primary tumor in the spine

BMC Medical Imaging

Research

Bing Kang¹  Ximing Wang¹  Ran Zhang²  Shuai Zhang³  Baosen Zhu⁴  Sha Li⁴  Rongchao Shi⁴  Xinxin Yu⁴  Fangyuan Liu⁴ 

[1] Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong University, No. 324, Jingwu Road, 250021, Jinan, Shandong, China;Huiying Medical Technology Co. Ltd, Beijing, China;School of Medicine, Shandong First Medical University, No. 6699, Qingdao Road, 250024, Jinan, Shandong, China;Shandong Provincial Hospital, Shandong University, No. 44, Wenhua West Road, 250012, Jinan, Shandong, China;

关键词: Spinal tumor;    Solitary spinal metastasis;    Nomogram;    Radiomics;    Magnetic resonance imaging;   

DOI  :  10.1186/s12880-023-00978-8

 received in 2022-08-16, accepted in 2023-01-27,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundDifferentiating between solitary spinal metastasis (SSM) and solitary primary spinal tumor (SPST) is essential for treatment decisions and prognosis. The aim of this study was to develop and validate an MRI-based radiomics nomogram for discriminating SSM from SPST.MethodsOne hundred and thirty-five patients with solitary spinal tumors were retrospectively studied and the data set was divided into two groups: a training set (n = 98) and a validation set (n = 37). Demographics and MRI characteristic features were evaluated to build a clinical factors model. Radiomics features were extracted from sagittal T1-weighted and fat-saturated T2-weighted images, and a radiomics signature model was constructed. A radiomics nomogram was established by combining radiomics features and significant clinical factors. The diagnostic performance of the three models was evaluated using receiver operator characteristic (ROC) curves on the training and validation sets. The Hosmer–Lemeshow test was performed to assess the calibration capability of radiomics nomogram, and we used decision curve analysis (DCA) to estimate the clinical usefulness.ResultsThe age, signal, and boundaries were used to construct the clinical factors model. Twenty-six features from MR images were used to build the radiomics signature. The radiomics nomogram achieved good performance for differentiating SSM from SPST with an area under the curve (AUC) of 0.980 in the training set and an AUC of 0.924 in the validation set. The Hosmer–Lemeshow test and decision curve analysis demonstrated the radiomics nomogram outperformed the clinical factors model.ConclusionsA radiomics nomogram as a noninvasive diagnostic method, which combines radiomics features and clinical factors, is helpful in distinguishing between SSM and SPST.

【 授权许可】

CC BY   
© The Author(s) 2023

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