A CT-based radiomics nomogram for predicting histologic grade and outcome in chondrosarcoma

• Published in: Cancer imaging 2024-04, Vol.24 (1), p.50-50, Article 50
• Main Authors: Li, Xiaoli, Shi, Xianglong, Wang, Yanmei, Pang, Jing, Zhao, Xia, Xu, Yuchao, Li, Qiyuan, Wang, Ning, Duan, Feng, Nie, Pei
• Format: Article
• Language: English
• Subjects: Bone cancer; Bone Neoplasms - diagnostic imaging; Calcification; Chondrosarcoma; Chondrosarcoma - diagnostic imaging; Computed tomography; Construction; CT imaging; Diagnostic imaging; Evaluation; Health risk assessment; Heterogeneity; Histology; Humans; Medical imaging; Medical imaging equipment; Medical prognosis; Medical records; Metastasis; Nomograms; Outcome; Patients; Performance prediction; Radiomics; Rankings; Regression analysis; Retrospective Studies; Scalloping; Software; Surgery; Survival; Survival analysis; Tomography, X-Ray Computed; Tumors; Variance analysis
• ISSN: 1470-7330; 1740-5025; 1470-7330
• DOI: 10.1186/s40644-024-00695-7

The preoperative identification of tumor grade in chondrosarcoma (CS) is crucial for devising effective treatment strategies and predicting outcomes. The study aims to build and validate a CT-based radiomics nomogram (RN) for the preoperative identification of tumor grade in CS, and to evaluate the correlation between the RN-predicted tumor grade and postoperative outcome. A total of 196 patients (139 in the training cohort and 57 in the external validation cohort) were derived from three different centers. A clinical model, radiomics signature (RS) and RN (which combines significant clinical factors and RS) were developed and validated to assess their ability to distinguish low-grade from high-grade CS with area under the curve (AUC). Additionally, Kaplan-Meier survival analysis was applied to examine the association between RN-predicted tumor grade and recurrence-free survival (RFS) of CS. The predictive accuracy of the RN was evaluated using Harrell's concordance index (C-index), hazard ratio (HR) and AUC. Size, endosteal scalloping and active periostitis were selected to build the clinical model. Three radiomics features, based on CT images, were selected to construct the RS. Both the RN (AUC, 0.842) and RS (AUC, 0.835) were superior to the clinical model (AUC, 0.776) in the validation set (P = 0.003, 0.040, respectively). A correlation between Nomogram score (Nomo-score, derived from RN) and RFS was observed through Kaplan-Meier survival analysis in the training and test cohorts (log-rank P