Value of radiomics nomogram based on T 1WI for pretreatment prediction of relapse within 1 year in osteosarcoma: a multicenter study
10.3760/cma.j.cn112149-20200512-00675
- VernacularTitle:基于多中心T 1WI影像组学列线图治疗前预测骨肉瘤一年内复发的价值
- Author:
Haimei CHEN
1
;
Jin LIU
;
Zixuan CHENG
;
Xianyue QUAN
;
Xiaohong WANG
;
Yu DENG
;
Ming LU
;
Quan ZHOU
;
Wei YANG
;
Zhiming XIANG
;
Shaolin LI
;
Zaiyi LIU
;
Yinghua ZHAO
Author Information
1. 南方医科大学第三附属医院（广东省骨科研究院）放射科，广州 510630
- From:
Chinese Journal of Radiology
2020;54(9):874-881
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of a radiomics nomogram based on T 1WI 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 T 1WI 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 T 1WI 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 T 1WI 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.
