Radiomics Based on Multimodal Magnetic Resonance Imaging for the Differential Diagnosis of Benign and Malignant Vertebral Compression Fractures

Overview

Journal Orthop Surg

Specialty Orthopedics

Date 2024 Jul 10

PMID 38982652

Authors

Wei Geng

Jingfen Zhu

Mao Li

Bin Pi

Xiantao Wang

Junhui Xing

Haibo Xu

Huilin Yang

Affiliations

Soon will be listed here.

Abstract

Objectives: Recent studies have indicated that radiomics may have excellent performance and clinical application prospects in the differential diagnosis of benign and malignant vertebral compression fractures (VCFs). However, multimodal magnetic resonance imaging (MRI)-based radiomics model is rarely used in the differential diagnosis of benign and malignant VCFs, and is limited to lumbar. Herein, this study intends to develop and validate MRI radiomics models for differential diagnoses of benign and malignant VCFs in patients.

Methods: This cross-sectional study involved 151 adult patients diagnosed with VCF in The First Affiliated Hospital of Soochow University in 2016-2021. The study was conducted in three steps: (i) the original MRI images were segmented, and the region of interest (ROI) was marked out; (ii) among the extracted features, those features with Pearson's correlation coefficient lower than 0.9 and the top 15 with the highest variance and Lasso regression coefficient less than and more than 0 were selected; (iii) MRI images and combined data were studied by logistic regression, decision tree, random forest and extreme gradient boosting (XGBoost) models in training set and the test set (ratio of 8:2), respectively; and the models were further verified and evaluated for the differential diagnosis performance. The evaluated indexes included area under receiver (AUC) of operating characteristic curve, accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and 95% confidence intervals (CIs). The AUCs were used to assess the predictive performance of different machine learning modes for benign and malignant VCFs.

Results: A total of 1144 radiomics features, and 14 clinical features were extracted. Finally, 12 radiomics features were included in the radiomics model, and 12 radiomics features with 14 clinical features were included in the combined model. In the radiomics model, the differential diagnosis performance in the logistic regression model with the AUC of 0.905 ± 0.026, accuracy of 0.817 ± 0.057, sensitivity of 0.831 ± 0.065, and negative predictive value of 0.813 ± 0.042, was superior to the other three. In the combined model, XGBoost model had the superior differential diagnosis performance with specificity (0.979 ± 0.026) and positive predictive value (0.971 ± 0.035).

Conclusion: The multimodal MRI-based radiomics model performed well in the differential diagnosis of benign and malignant VCFs, which may provide a tool for clinicians to differentially diagnose VCFs.

References

Winter N, Blanke J, Leenings R, Ernsting J, Fisch L, Sarink K . A Systematic Evaluation of Machine Learning-Based Biomarkers for Major Depressive Disorder. JAMA Psychiatry. 2024; 81(4):386-395. PMC: 10782379. DOI: 10.1001/jamapsychiatry.2023.5083. View

Cheung C, Ran A, Wang S, Chan V, Sham K, Hilal S . A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study. Lancet Digit Health. 2022; 4(11):e806-e815. DOI: 10.1016/S2589-7500(22)00169-8. View

Dong S, Zhu J, Yang H, Huang G, Zhao C, Yuan B . Development and Internal Validation of Supervised Machine Learning Algorithm for Predicting the Risk of Recollapse Following Minimally Invasive Kyphoplasty in Osteoporotic Vertebral Compression Fractures. Front Public Health. 2022; 10:874672. PMC: 9108356. DOI: 10.3389/fpubh.2022.874672. View

Gui C, Chen X, Sheikh K, Mathews L, Lo S, Lee J . Radiomic modeling to predict risk of vertebral compression fracture after stereotactic body radiation therapy for spinal metastases. J Neurosurg Spine. 2021; 36(2):294-302. DOI: 10.3171/2021.3.SPINE201534. View

Zhang H, Yuan G, Wang C, Zhao H, Zhu K, Guo J . Differentiation of benign versus malignant indistinguishable vertebral compression fractures by different machine learning with MRI-based radiomic features. Eur Radiol. 2023; 33(7):5069-5076. DOI: 10.1007/s00330-023-09678-x. View

Feng Q, Xu S, Gong X, Wang T, He X, Liao D . An MRI-Based Radiomics Nomogram for Differentiation of Benign and Malignant Vertebral Compression Fracture. Acad Radiol. 2023; 31(2):605-616. DOI: 10.1016/j.acra.2023.07.011. View

Madassery S . Vertebral Compression Fractures: Evaluation and Management. Semin Intervent Radiol. 2020; 37(2):214-219. PMC: 7224975. DOI: 10.1055/s-0040-1709208. View

Aghnia Farda N, Lai J, Wang J, Lee P, Liu J, Hsieh I . Sanders classification of calcaneal fractures in CT images with deep learning and differential data augmentation techniques. Injury. 2020; 52(3):616-624. DOI: 10.1016/j.injury.2020.09.010. View

Zheng Y, Han X, Jia X, Ding C, Zhang K, Li H . Dual-energy CT-based radiomics for predicting invasiveness of lung adenocarcinoma appearing as ground-glass nodules. Front Oncol. 2023; 13:1208758. PMC: 10449576. DOI: 10.3389/fonc.2023.1208758. View

10.

Frighetto-Pereira L, Rangayyan R, Metzner G, de Azevedo-Marques P, Nogueira-Barbosa M . Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images. Comput Biol Med. 2016; 73:147-56. DOI: 10.1016/j.compbiomed.2016.04.006. View

11.

Wang X, You X, Zhang L, Huang D, Aramini B, Shabaturov L . A radiomics model combined with XGBoost may improve the accuracy of distinguishing between mediastinal cysts and tumors: a multicenter validation analysis. Ann Transl Med. 2022; 9(23):1737. PMC: 8743732. DOI: 10.21037/atm-21-5999. View

12.

Kim S, Lee J . Diagnostic Performance of F-18 Fluorodeoxyglucose Positron Emission Tomography or Positron Emission Tomography/Computed Tomography for Differentiation of Benign and Malignant Vertebral Compression Fractures: A Meta-Analysis. World Neurosurg. 2020; 137:e626-e633. DOI: 10.1016/j.wneu.2020.02.085. View

13.

Yan Q, Wang W, Zhao W, Zuo L, Wang D, Chai X . Differentiating nontuberculous mycobacterium pulmonary disease from pulmonary tuberculosis through the analysis of the cavity features in CT images using radiomics. BMC Pulm Med. 2022; 22(1):4. PMC: 8740493. DOI: 10.1186/s12890-021-01766-2. View

14.

Soultanis K, Thano A, Soucacos P . "Outcome of thoracolumbar compression fractures following non-operative treatment". Injury. 2021; 52(12):3685-3690. DOI: 10.1016/j.injury.2021.05.019. View

15.

Bezzi C, Mapelli P, Presotto L, Neri I, Scifo P, Savi A . Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance. Eur J Nucl Med Mol Imaging. 2021; 48(12):4002-4015. DOI: 10.1007/s00259-021-05338-8. View

16.

Xu Z, Zhao L, Yin L, Liu Y, Ren Y, Yang G . MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus. Front Bioeng Biotechnol. 2022; 10:1082794. PMC: 9725113. DOI: 10.3389/fbioe.2022.1082794. View

17.

Pfeifle C, Kohut P, Jarvers J, Spiegl U, Heyde C, Osterhoff G . Does time-to-surgery affect mortality in patients with acute osteoporotic vertebral compression fractures?. BMC Geriatr. 2021; 21(1):714. PMC: 8684218. DOI: 10.1186/s12877-021-02682-0. View

18.

Beall D, De Leacy R . Management of Chronic Vertebral Compression Fractures with Vertebroplasty: Focus on Clinical Symptoms. Radiology. 2023; 308(1):e231243. DOI: 10.1148/radiol.231243. View

19.

Le N, Hung T, Do D, Lam L, Dang L, Huynh T . Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI. Comput Biol Med. 2021; 132:104320. DOI: 10.1016/j.compbiomed.2021.104320. View

20.

Khan M, Jennings J, Baker J, Smolock A, Shah L, Pinchot J . ACR Appropriateness Criteria® Management of Vertebral Compression Fractures: 2022 Update. J Am Coll Radiol. 2023; 20(5S):S102-S124. DOI: 10.1016/j.jacr.2023.02.015. View