Haimei Chen1, Jin Liu2, Zixuan Cheng3, Xing Lu4, Xiaohong Wang5, Ming Lu6, Shaolin Li7, Zhiming Xiang8, Quan Zhou9, Zaiyi Liu10, Yinghua Zhao11. 1. Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics. Guangdong Province), Guangzhou, Guangdong 510630, China. Electronic address: 1225051257@qq.com. 2. Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics. Guangdong Province), Guangzhou, Guangdong 510630, China. Electronic address: 602046738@qq.com. 3. Department of Radiology, Guangzhou Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080,China. Electronic address: czxtlbb@126.com. 4. Sigma Technologies Inc, San Diego, CA 92130, United States. Electronic address: lvxing@12sigma.ai. 5. Department of Radiology, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong 510630, China. Electronic address: wxhcrz@163.com. 6. Department of Oncology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, Guangdong 510630, China. Electronic address: luming0613@gmail.com. 7. Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong 519000, China. Electronic address: lishaolin1963@163.com. 8. Department of Radiology, Panyu Central Hospital of Guangzhou, Guangzhou, Guangdong 511400, China. Electronic address: xzmgz@126.com. 9. Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics. Guangdong Province), Guangzhou, Guangdong 510630, China. Electronic address: zhouquan3777@smu.edu.cn. 10. Department of Radiology, Guangzhou Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080,China. Electronic address: zyliu@163.com. 11. Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics. Guangdong Province), Guangzhou, Guangdong 510630, China. Electronic address: zyh7258957@163.com.
Abstract
PURPOSE: To develop and externally validate an MR-based radiomics nomogram from retrospective multicenter datasets for pretreatment prediction of early relapse (≤ 1 year) in osteosarcoma after surgical resection. METHODS: This multicenter study retrospectively enrolled 93 patients (training cohort: 62 patients from four hospitals; validation cohort: 31 patients from two hospitals) with clinicopathologically confirmed osteosarcoma who received neoadjuvant chemotherapy and surgical resection at six hospitals between January 2009 and October 2017. Radiomics features were extracted from contrast-enhanced fat-suppressed T1-weighted (CE FS T1-w) images. Least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection and radiomics signature construction. The radiomics nomogram that incorporated the radiomics signature and subjective MRI-assessed candidate predictors was developed to predict early relapse with a multivariate logistic regression model in the training cohort and validated in the external validation cohort. The performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness. RESULTS: The radiomics signature comprised six selected features and achieved favorable prediction efficacy. The radiomics nomogram incorporating the radiomics signature and subjective MRI-assessed candidate predictors (joint invasion and perivascular involvement) from the multicenter datasets achieved better discrimination in the training cohort (C-index：0.907, 95 % CI: 0.838-0.977) and external validation cohort (C-index: 0.811, 95 % CI: 0.653-0.970), and good calibration. Decision curve analysis suggested that the combined nomogram was clinically useful. CONCLUSION: The proposed MRI-based radiomics nomogram could provide a non-invasive tool to predict early relapse of osteosarcoma, which has the potential to improve personalized pretreatment management of osteosarcoma.
PURPOSE: To develop and externally validate an MR-based radiomics nomogram from retrospective multicenter datasets for pretreatment prediction of early relapse (≤ 1 year) in osteosarcoma after surgical resection. METHODS: This multicenter study retrospectively enrolled 93 patients (training cohort: 62 patients from four hospitals; validation cohort: 31 patients from two hospitals) with clinicopathologically confirmed osteosarcoma who received neoadjuvant chemotherapy and surgical resection at six hospitals between January 2009 and October 2017. Radiomics features were extracted from contrast-enhanced fat-suppressed T1-weighted (CE FS T1-w) images. Least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection and radiomics signature construction. The radiomics nomogram that incorporated the radiomics signature and subjective MRI-assessed candidate predictors was developed to predict early relapse with a multivariate logistic regression model in the training cohort and validated in the external validation cohort. The performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness. RESULTS: The radiomics signature comprised six selected features and achieved favorable prediction efficacy. The radiomics nomogram incorporating the radiomics signature and subjective MRI-assessed candidate predictors (joint invasion and perivascular involvement) from the multicenter datasets achieved better discrimination in the training cohort (C-index：0.907, 95 % CI: 0.838-0.977) and external validation cohort (C-index: 0.811, 95 % CI: 0.653-0.970), and good calibration. Decision curve analysis suggested that the combined nomogram was clinically useful. CONCLUSION: The proposed MRI-based radiomics nomogram could provide a non-invasive tool to predict early relapse of osteosarcoma, which has the potential to improve personalized pretreatment management of osteosarcoma.
Authors: Lawrence M White; Angela Atinga; Ali M Naraghi; Katherine Lajkosz; Jay S Wunder; Peter Ferguson; Kim Tsoi; Anthony Griffin; Masoom Haider Journal: Skeletal Radiol Date: 2022-07-01 Impact factor: 2.199