Radiomics Analysis of Fat-Saturated T2-Weighted MRI Sequences for the Prediction of Prognosis in Soft Tissue Sarcoma of the Extremities and Trunk Treated With Neoadjuvant Radiotherapy

被引：16

作者：

Chen, Silin ^{[1
]}

Li, Ning ^{[1
,2
,3
]}

Tang, Yuan ^{[1
]}

Chen, Bo ^{[1
]}

Fang, Hui ^{[1
]}

Qi, Shunan ^{[1
]}

Lu, Ninging ^{[1
]}

Yang, Yong ^{[1
]}

Song, Yongwen ^{[1
]}

Liu, Yueping ^{[1
]}

Wang, Shulian ^{[1
]}

Li, Ye-Xiong ^{[1
]}

Jin, Jing ^{[1
,2
,3
]}

机构：

[1] Chinese Acad Med Sci & Peking Union Med Coll, Canc Hosp, Natl Clin Res Ctr Canc, Natl Canc Ctr,Dept Radiat Oncol, Beijing, Peoples R China

[2] Chinese Acad Med Sci & Peking Union Med Coll, Canc Hosp, Natl Clin Res Ctr Canc, Natl Canc Ctr,Dept Radiat Oncol, Shenzhen, Peoples R China

[3] Chinese Acad Med Sci & Peking Union Med Coll, Shenzhen Hosp, Shenzhen, Peoples R China

来源：

FRONTIERS IN ONCOLOGY | 2021年 / 11卷

基金：

中国国家自然科学基金;

关键词：

sarcoma; neoadjuvant therapy; magnetic resonance imaging; radiomic; prognosis; POSTOPERATIVE RADIOTHERAPY; RADIATION-THERAPY; RISK; DIAGNOSIS; FEATURES; MARGIN; MODEL; PET;

D O I：

10.3389/fonc.2021.710649

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Purpose To create a prognostic prediction radiomics model for soft tissue sarcoma (STS) of the extremities and trunk treated with neoadjuvant radiotherapy.</p> Methods This study included 62 patients with STS of the extremities and trunk who underwent magnetic resonance imaging (MRI) before neoadjuvant radiotherapy. After tumour segmentation and preprocessing, 851 radiomics features were extracted. The radiomics score was constructed according to the least absolute shrinkage and selection operator (LASSO) method. Survival analysis (disease-free survival; DFS) was performed using the log-rank test and Cox's proportional hazards regression model. The nomogram model was established based on the log-rank test and Cox regression model. Harrell's concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve analysis were used to evaluate the prognostic factors. The clinical utility of the model was assessed by decision curve analysis (DCA).</p> Results The univariate survival analysis showed that tumour location (p = 0.032), clinical stage (p = 0.022), tumour size (p = 0.005) and the radiomics score were correlated with DFS (p < 0.05). The multivariate analysis showed that tumour location, tumour size, and the radiomics score were independent prognostic factors for DFS (p < 0.05). The combined clinical-radiomics model based on the multivariate analysis showed the best predictive ability for DFS (C-index: 0.781; Area Under Curve: 0.791). DCA revealed that the use of the radiomics score-based nomogram was associated with better benefit gains relative to the prediction of 2-year DFS events than other models in the threshold probability range between 0.12 and 0.38.</p> Conclusion The radiomics score from pretreatment MRI is an independent prognostic factor for DFS in patients with STS of the extremities and trunk. The radiomics score-based nomogram could improve prognostic stratification ability and thus contribute to individualized therapy for STS patients.</p>

引用

页数：9

共 45 条

[1] Harmonization of radiomic feature distributions: impact on classification of hepatic tissue in CT imaging [J].