A CT-based radiomics nomogram for distinguishing between benign and malignant bone tumours
Open Access
- 6 February 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Cancer Imaging
- Vol. 21 (1), 1-10
- https://doi.org/10.1186/s40644-021-00387-6
Abstract
Background We sought to evaluate the performance of a computed tomography (CT)-based radiomics nomogram we devised in distinguishing benign from malignant bone tumours. Methods Two hundred and six patients with bone tumours were spilt into two groups: a training set (n = 155) and a validation set (n = 51). A feature extraction process based on 3D Slicer software was used to extract the radiomics features from unenhanced CT images, and least absolute shrinkage and selection operator logistic regression was used to calculate the radiomic score to generate a radiomics signature. A clinical model comprised demographics and CT features. A radiomics nomogram combined with the clinical model and the radiomics signature was constructed. The performance of the three models was comprehensively evaluated from three aspects: identification ability, accuracy, and clinical value, allowing for generation of an optimal prediction model. Results The radiomics nomogram comprised clinical and radiomics signature features. The nomogram model displayed good performance in training and validation sets with areas under the curve of 0.917 and 0.823, respectively. The areas under the curve, decision curve analysis, and net reclassification improvement showed that the radiomics nomogram model could obtain better diagnostic performance than the clinical model and achieve greater clinical net benefits than the clinical and radiomics signature models alone. Conclusions We constructed a combined nomogram comprising a clinical model and radiomics signature as a noninvasive preoperative prediction method to distinguish between benign and malignant bone tumours and assist treatment planning.Keywords
This publication has 33 references indexed in Scilit:
- Radiography in the Initial Diagnosis of Primary Bone TumorsAmerican Journal of Roentgenology, 2013
- Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?Insights into Imaging, 2012
- Radiomics: Extracting more information from medical images using advanced feature analysisEuropean Journal of Cancer, 2012
- Imaging of Primary Malignant Bone Tumors (Nonhematological)Radiologic Clinics of North America, 2011
- Impact of PET and CT in PET/CT Studies for Staging and Evaluating Treatment Response in Bone and Soft Tissue SarcomasClinical Nuclear Medicine, 2009
- Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance imagesMagnetic Resonance in Medicine, 2007
- Decision Curve Analysis: A Novel Method for Evaluating Prediction ModelsMedical Decision Making, 2006
- User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliabilityNeuroImage, 2006
- Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression dataBioinformatics, 2005
- The incidence and relevance of bone sclerosis in orbital pseudotumourClinical Radiology, 1996