A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast-enhanced computed tomography
A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast-enhanced computed tomography
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DOI：
10.3892/or.2020.7497
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发表时间：
2020-04-01
期刊：
影响因子：
4.2
通讯作者：
Long, Wansheng
中科院分区：
文献类型：
--
作者：
Chen, Xiangmeng;Feng, Bao;Long, Wansheng
在本研究中，我们的目的是构建一个放射组学模型，使用对比增强计算机断层扫描（CT）预测胸腺上皮肿瘤（THEORY）的病理侵袭性。我们回顾性分析了来自两家医院的179例组织学确诊的Tendon患者（89例女性）的记录。将82个低风险和97个高风险THBVDNA分配到训练（90个肿瘤），内部验证（49个肿瘤）和外部验证（40个肿瘤）队列。使用最小绝对收缩和选择算子逻辑回归选择从术前对比增强胸部CT提取的放射组学特征。三个预测模型，采用多元逻辑回归分析。分别使用受试者工作特征曲线和DeLong检验评估其性能和临床效用。八个非零系数的放射组学特征被用于开发放射组学评分，其在低风险和高风险乳腺癌之间存在显著差异（P
In the present study, we aimed to construct a radiomics model using contrast-enhanced computed tomography (CT) to predict the pathological invasiveness of thymic epithelial tumors (TETs). We retrospectively reviewed the records of 179 consecutive patients (89 females) with histologically confirmed TETs from two hospitals. The 82 low- and 97 high-risk TETs were assigned to training (90 tumors), internal validation (49 tumors) and external validation (40 tumors) cohorts. Radiomics features extracted from preoperative contrast-enhanced chest CT were selected using least absolute shrinkage and selection operator logistic regression. Three prediction models were developed using multivariate logistic regression analysis. Their performance and clinical utility were assessed using receiver operating characteristic curves and the DeLong test, respectively. Eight radiomics features with non-zero coefficients were used to develop a radiomics score, which significantly differed between low- and high-risk TETs (P