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.
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.
复制标题
脂肪饱和 T2 加权 MRI 序列的放射组学分析用于预测接受新辅助放射治疗的四肢和躯干软组织肉瘤的预后
DOI：
10.3389/fonc.2021.710649
复制
发表时间：
2021
影响因子：
4.7
通讯作者：
Jin J
中科院分区：
文献类型：
--
作者：
Chen S;Li N;Tang Y;Chen B;Fang H;Qi S;Lu N;Yang Y;Song Y;Liu Y;Wang S;Li YX;Jin J
建立新辅助放射治疗四肢和躯干软组织肉瘤（STS）的预后预测放射组学模型。本研究纳入了62例在新辅助放疗前接受磁共振成像（MRI）的四肢和躯干STS患者。经过肿瘤分割和预处理，提取了851个放射组学特征。根据最小绝对收缩和选择算子（LASSO）方法构建放射组学评分。使用对数秩检验和考克斯比例风险回归模型进行生存分析（无病生存期; DFS）。采用对数秩检验和考克斯回归模型建立诺模图模型。采用Harrell一致性指数（C指数）、校正曲线和受试者工作特征（ROC）曲线分析预后因素。通过决策曲线分析（DCA）评估模型的临床实用性。单因素生存分析显示肿瘤位置（p = 0.032）、临床分期（p = 0.022）、肿瘤大小（p = 0.005）和放射组学评分与DFS相关（p <0.05）。多变量分析显示肿瘤位置、肿瘤大小和放射组学评分是DFS的独立预后因素（p <0.05）。基于多变量分析的临床-放射组学组合模型显示出对DFS的最佳预测能力（C指数：0.781;曲线下面积：0.791）。DCA显示，在0.12至0.38之间的阈值概率范围内，相对于2年DFS事件的预测，使用基于放射组学评分的列线图与更好的获益相关。治疗前MRI的放射组学评分是四肢和躯干STS患者DFS的独立预后因素。基于放射组学评分的诺模图可提高STS患者的预后分层能力，从而有助于STS患者的个体化治疗。
To create a prognostic prediction radiomics model for soft tissue sarcoma (STS) of the extremities and trunk treated with neoadjuvant radiotherapy. 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). 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. 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.
登录
查看更多内容
DOI：
10.1080/23808993.2016.1164013
发表时间：
2016
影响因子：
1.2
作者：
Parekh V;Jacobs MA
通讯作者：
Jacobs MA
影响因子：
3.3
作者：
Crombe, Amandine;Le Loarer, Francois;Kind, Michele
通讯作者：
Kind, Michele
影响因子：
5.7
作者：
Peeken, Jan C.;Bernhofer, Michael;Combs, Stephanie E.
通讯作者：
Combs, Stephanie E.
影响因子：
5.6
作者：
Schneider, Nina;Strauss, Dirk C.;Thway, Khin
通讯作者：
Thway, Khin
影响因子：
254.7
作者：
Gamboa, Adriana C.;Gronchi, Alessandro;Cardona, Kenneth
通讯作者：
Cardona, Kenneth