CT-based radiomic features predict tumor grading and have prognostic value in patients with soft tissue sarcomas treated with neoadjuvant radiation therapy
CT-based radiomic features predict tumor grading and have prognostic value in patients with soft tissue sarcomas treated with neoadjuvant radiation therapy
复制标题
DOI：
10.1016/j.radonc.2019.01.004
复制
发表时间：
2019-06-01
影响因子：
5.7
通讯作者：
Combs, Stephanie E.
中科院分区：
文献类型：
--
作者：
Peeken, Jan C.;Bernhofer, Michael;Combs, Stephanie E.
目的：软组织肉瘤（STS）患者尽管局部复发率低，但全身进展和生存率仍然很低。在这项工作中，我们调查是否定量成像功能（“放射组学”）的放射治疗计划的CT扫描进行治疗前的风险assessment.Methods的预后价值：CT扫描，肿瘤分级和临床信息收集从三个独立的回顾性队列83（TUM），87（UW）和51（麦吉尔）STS患者，分别。经过人工分割和预处理，提取了1358个放射组学特征。特征减少和机器学习建模的预测分级，总生存期（OS），远端（DPFS）和本地（LPFS）无进展survival. Results的外部validation.Results的预测：放射组学模型能够区分3级从非3级STS（在接收器操作者特征曲线（AUC）下的面积：0.64）。放射组学模型能够预测验证队列中的OS（C指数：0.73）、DPFS（C指数：0.68）和LPFS（C指数：0.77）。临床-放射组学组合模型显示对OS的最佳预测（C指数：0.76）。放射组学评分显着相关的单变量和多变量考克斯回归，并允许所有三个endpoints.Conclusion显着的风险分层：这是第一份报告，展示了预后潜力和肿瘤分级分化CT为基础的放射组学。(C)2019 Elsevier B. V.版权所有。
Purpose: In soft tissue sarcoma (STS) patients systemic progression and survival remain comparably low despite low local recurrence rates. In this work, we investigated whether quantitative imaging features ("radiomics") of radiotherapy planning CT-scans carry a prognostic value for pre-therapeutic risk assessment.Methods: CT-scans, tumor grade, and clinical information were collected from three independent retrospective cohorts of 83 (TUM), 87 (UW) and 51 (McGill) STS patients, respectively. After manual segmentation and preprocessing, 1358 radiomic features were extracted. Feature reduction and machine learning modeling for the prediction of grading, overall survival (OS), distant (DPFS) and local (LPFS) progression free survival were performed followed by external validation.Results: Radiomic models were able to differentiate grade 3 from non-grade 3 STS (area under the receiver operator characteristic curve (AUC): 0.64). The Radiomic models were able to predict OS (C-index: 0.73), DPFS (C-index: 0.68) and LPFS (C-index: 0.77) in the validation cohort. A combined clinical-radiomics model showed the best prediction for OS (C-index: 0.76). The radiomic scores were significantly associated in univariate and multivariate cox regression and allowed for significant risk stratification for all three endpoints.Conclusion: This is the first report demonstrating a prognostic potential and tumor grading differentiation by CT-based radiomics. (C) 2019 Elsevier B.V. All rights reserved.