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Musculoskeletal Radiology Value of radiomics nomogram based on T1WI for pretreatment prediction of relapse within 1 year in osteosarcoma: a multicenter study Chen Haimei, Liu Jin, Cheng Zixuan, Quan Xianyue, Wang Xiaohong, Deng Yu, Lu Ming, Zhou Quan, Yang Wei, Xiang Zhiming, Li Shaolin, Liu Zaiyi, Zhao Yinghua Published on 2020-09-10 Cite as Chin J Radiol, 2020,54(09): 874-881. DOI: 10.3760/cma.j.cn112149-20200512-00675 Abstract Objective To explore the value of a radiomics nomogram based on T1WI for prediction of the relapse of osteosarcoma after surgery within 1 year from multicenter data. Methods The imaging and clinical data of 107 patients with pathologica1ly confirmed osteosarcoma who received neoadjuvant chemotherapy before surgery from 6 hospitals from January 2009 to October 2017 were retrospectively analyzed. A training cohort consisted of 75 patients from firstly enrolled 4 hospitals and an independent validation cohort of 32 patients from other 2 hospitals. Pretreatment T1WI was used to extract radiomics features. Least absolute shrinkage and selection operator (LASSO) regression was applied to reduce the dimension and then the radiomics signature was constructed to predict the relapse of osteosarcoma after surgery within 1 year in training cohort. Independent clinical risk factors were screened using one-way logistic regression, and then a radiomics nomogram incorporated the radiomics signature and MRI characteristics was developed by multivariate logistic regression. The predictive nomogram was evaluated using receiver operating characteristic (ROC) curve in the training cohort, and validated in the independent validation cohort. The calibration curve was used to evaluate the agreement between prediction and actual observation and the decision curve was used to demonstrate the clinical usefulness. Results Based on T1WI from multicenter institutions, the radiomics signature was built using 2 valuable selected features that were significantly associated with relapse within 1 year. Two selected features included 1 gray-level co-occurrence matrices (GLCM) feature (L_G_1.0_GLCM_homogeneity1, LASSO coefficient 3.122) and 1 gray-level run length matrix (GLRLM) feature (GLRLM_RP, LASSO coefficient -2.474). The prediction nomogram including radiomics signature and MRI characteristics (joint invasion and perivascular involvement) showed good discrimination with the area under the ROC curve of 0.884 and 0.821 in the training and validation cohorts, respectively. The calibration curve showed that the nomogram achieved good agreement between prediction and actual observation. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful when the threshold probability was greater than 21%. Conclusion The radiomics nomogram based on T1WI can be used as a non-invasive quantitative tool to predict relapse of osteosarcoma within 1 year before treatment, which provides support for clinical decision-making in osteosarcoma. Key words: Osteosarcoma; Recurrence; Magnetic resonance imaging; Radiomics; Nomogram Contributor Information Chen Haimei Department of Radiology, the Third Affiliated Hospital of Southern Medical University, Academy of Orthopedics Guangdong Province, Guangzhou 510630, China Liu Jin Department of Radiology, the Third Affiliated Hospital of Southern Medical University, Academy of Orthopedics Guangdong Province, Guangzhou 510630, China Cheng Zixuan Department of Radiology, Guangdong Provincial People′s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China Quan Xianyue Department of Radiology, Zhujiang Hospital of Southern Medical University, Guangzhou 510282, China Wang Xiaohong Department of Radiology, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China Deng Yu Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China Lu Ming Department of Bone Oncology, the Third Affiliated Hospital of Southern Medical University, Academy of Orthopedics Guangdong Province, Guangzhou 510630, China Zhou Quan Department of Radiology, the Third Affiliated Hospital of Southern Medical University, Academy of Orthopedics Guangdong Province, Guangzhou 510630, China Yang Wei Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China Xiang Zhiming Department of Radiology, Panyu Central Hospital of Guangzhou, Guangzhou 511400, China Li Shaolin Department of Radiology, the Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai 519000, China Liu Zaiyi Department of Radiology, Guangdong Provincial People′s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China Zhao Yinghua Department of Radiology, the Third Affiliated Hospital of Southern Medical University, Academy of Orthopedics Guangdong Province, Guangzhou 510630, China |
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