Combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT

• Published in: European radiology 2021-09, Vol.31 (9), p.6825-6834
• Main Authors: Chee, Choong Guen, Yoon, Min A, Kim, Kyung Won, Ko, Yusun, Ham, Su Jung, Cho, Young Chul, Park, Bumwoo, Chung, Hye Won
• Format: Article
• Language: English
• Subjects: Calibration; Compression; Computed tomography; Diagnostic Radiology; Feature extraction; Fractures; Image contrast; Image enhancement; Imaging; Internal Medicine; Interventional Radiology; Malignancy; Medicine; Medicine & Public Health; Model accuracy; Musculoskeletal; Neuroradiology; Parameters; Patients; Radiology; Radiomics; Regression analysis; Regression models; Training; Ultrasound; Vertebrae
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-021-07832-x

Objectives To develop and validate a combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT. Methods One hundred sixty-five patients with vertebral compression fractures were allocated to training ( n = 110 [62 acute benign and 48 malignant fractures]) and validation ( n = 55 [30 acute benign and 25 malignant fractures]) cohorts. Radiomics features ( n = 144) were extracted from non-contrast-enhanced CT images. Radiomics score was constructed by applying least absolute shrinkage and selection operator regression to reproducible features. A combined radiomics-clinical model was constructed by integrating significant clinical parameters with radiomics score using multivariate logistic regression analysis. Model performance was quantified in terms of discrimination and calibration. The model was internally validated on the independent data set. Results The combined radiomics-clinical model, composed of two significant clinical predictors (age and history of malignancy) and the radiomics score, showed good calibration (Hosmer-Lemeshow test, p > 0.05) and discrimination in both training (AUC, 0.970) and validation (AUC, 0.948) cohorts. Discrimination performance of the combined model was higher than that of either the radiomics score (AUC, 0.941 in training cohort and 0.852 in validation cohort) or the clinical predictor model (AUC, 0.924 in training cohort and 0.849 in validation cohort). The model stratified patients into groups with low and high risk of malignant fracture with an accuracy of 98.2% in the training cohort and 90.9% in the validation cohort. Conclusions The combined radiomics-clinical model integrating clinical parameters with radiomics score could predict malignancy in vertebral compression fractures on CT with high discriminatory ability. Key Points • A combined radiomics-clinical model was constructed to predict malignancy of vertebral compression fractures on CT by combining clinical parameters and radiomics features. • The model showed good calibration and discrimination in both training and validation cohorts. • The model showed high accuracy in the stratification of patients into groups with low and high risk of malignant vertebral compression fractures.