Jin Liu1, Tao Lian2, Haimei Chen1, Xiaohong Wang3, Xianyue Quan4, Yu Deng5, Juan Yao6, Ming Lu7, Qiang Ye1, Qianjin Feng2, Yinghua Zhao1. 1. Department of Radiology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong 510630, China. 2. Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China. 3. Department of Radiology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China. 4. Department of Radiology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong 510282, China. 5. Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China. 6. Department of Pathology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong 510630, China. 7. Department of Oncology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong 510630, China.
Abstract
OBJECTIVE: To develop and externally validate a CT-based radiomics nomogram for pretreatment prediction of relapse in osteosarcoma patients within one year. MATERIALS AND METHODS: In this multicenter retrospective study, a total of 80 patients (training cohort: 63 patients from three hospitals; validation cohort: 17 patients from three other hospitals) with osteosarcoma, undergoing pretreatment CT between August 2010 and December 2018, were identified from multicenter databases. Radiomics features were extracted and selected from tumor regions on CT image, and then, the radiomics signature was constructed. The radiomics nomogram that incorporated the radiomics signature and clinical-based risk factors was developed to predict relapse risk with a multivariate Cox regression model using the training cohort and validated using the external validation cohort. The performance of the nomogram was assessed concerning discrimination, calibration, reclassification, and clinical usefulness. RESULTS: Kaplan-Meier curves based on the radiomics signature showed a significant difference between the high-risk and the low-risk groups in both training and validation cohorts (P < 0.001 and P = 0.015, respectively). The radiomics nomogram achieved good discriminant results in the training cohort (C-index: 0.779) and the validation cohort (C-index: 0.710) as well as good calibration. Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinical-based nomogram (P < 0.001). CONCLUSIONS: This multicenter study demonstrates that a radiomics nomogram incorporated the radiomics signature and clinical-based risk factors can increase the predictive value of the osteosarcoma relapse risk, which supports the clinical application in different institutions.
OBJECTIVE: To develop and externally validate a CT-based radiomics nomogram for pretreatment prediction of relapse in osteosarcoma patients within one year. MATERIALS AND METHODS: In this multicenter retrospective study, a total of 80 patients (training cohort: 63 patients from three hospitals; validation cohort: 17 patients from three other hospitals) with osteosarcoma, undergoing pretreatment CT between August 2010 and December 2018, were identified from multicenter databases. Radiomics features were extracted and selected from tumor regions on CT image, and then, the radiomics signature was constructed. The radiomics nomogram that incorporated the radiomics signature and clinical-based risk factors was developed to predict relapse risk with a multivariate Cox regression model using the training cohort and validated using the external validation cohort. The performance of the nomogram was assessed concerning discrimination, calibration, reclassification, and clinical usefulness. RESULTS: Kaplan-Meier curves based on the radiomics signature showed a significant difference between the high-risk and the low-risk groups in both training and validation cohorts (P < 0.001 and P = 0.015, respectively). The radiomics nomogram achieved good discriminant results in the training cohort (C-index: 0.779) and the validation cohort (C-index: 0.710) as well as good calibration. Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinical-based nomogram (P < 0.001). CONCLUSIONS: This multicenter study demonstrates that a radiomics nomogram incorporated the radiomics signature and clinical-based risk factors can increase the predictive value of the osteosarcoma relapse risk, which supports the clinical application in different institutions.
Authors: J Sybil Biermann; Warren Chow; Damon R Reed; David Lucas; Douglas R Adkins; Mark Agulnik; Robert S Benjamin; Brian Brigman; G Thomas Budd; William T Curry; Aarati Didwania; Nicola Fabbri; Francis J Hornicek; Joseph B Kuechle; Dieter Lindskog; Joel Mayerson; Sean V McGarry; Lynn Million; Carol D Morris; Sujana Movva; Richard J O'Donnell; R Lor Randall; Peter Rose; Victor M Santana; Robert L Satcher; Herbert Schwartz; Herrick J Siegel; Katherine Thornton; Victor Villalobos; Mary Anne Bergman; Jillian L Scavone Journal: J Natl Compr Canc Netw Date: 2017-02 Impact factor: 11.908
Authors: G Bacci; S Ferrari; M Mercuri; F Bertoni; P Picci; M Manfrini; A Gasbarrini; C Forni; M Cesari; M Campanacci Journal: Acta Orthop Scand Date: 1998-06
Authors: Ahmad Chaddad; Michael Jonathan Kucharczyk; Paul Daniel; Siham Sabri; Bertrand J Jean-Claude; Tamim Niazi; Bassam Abdulkarim Journal: Front Oncol Date: 2019-05-21 Impact factor: 6.244