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Computed Tomography-Based Radiomic Features for Diagnosis of Indeterminate Small Pulmonary Nodules.

基于计算机断层扫描的放射组学特征诊断不确定的肺小结节。

  • 影响因子:1.48
  • DOI:10.1097/RCT.0000000000000976
  • 作者列表:"Liu Q","Huang Y","Chen H","Liu Y","Liang R","Zeng Q
  • 发表时间:2020-01-01
Abstract

OBJECTIVE:This study aimed to determine the potential of radiomic features extracted from preoperative computed tomography to discriminate malignant from benign indeterminate small (≤10 mm) pulmonary nodules. METHODS:A total of 197 patients with 210 nodules who underwent surgical resections between January 2011 and March 2017 were analyzed. Three hundred eighty-five radiomic features were extracted from the computed tomographic images. Feature selection and data dimension reduction were performed using the Kruskal-Wallis test, Spearman correlation analysis, and principal component analysis. The random forest was used for radiomic signature building. The receiver operating characteristic curve analysis was used to evaluate the model performance. RESULTS:Fifteen principal component features were selected for modeling. The area under the curve, sensitivity, specificity, and accuracy of the prediction model were 0.877 (95% confidence interval [CI], 0.795-0.959), 81.8% (95% CI, 72.0%-90.9%), 77.4% (95% CI, 63.9%-89.3%), and 80.0% (95% CI, 72.0%-86.7%) in the validation cohort, respectively. CONCLUSIONS:Computed tomography-based radiomic features showed good discriminative power for benign and malignant indeterminate small pulmonary nodules.

摘要

目的: 本研究旨在确定从术前计算机断层扫描中提取的放射组学特征鉴别恶性和良性不确定小 (≤ 10毫米) 肺结节的潜力。 方法: 分析 2011 年 1 月至 2017 年 3 月接受手术切除的 197 例患者 210 个结节。从计算机断层图像中提取了三百八十五个放射组学特征。使用Kruskal-Wallis检验、Spearman相关分析和主成分分析进行特征选择和数据降维。随机森林用于放射组学签名构建。采用受试者工作特征曲线分析评价模型性能。 结果: 选取 15 个主成分特征进行建模。预测模型的曲线下面积、灵敏度、特异度和准确度分别为 0.877 (95% 可信区间 [CI],0.795-0.959),81.8% (95% CI,72.0%-90.9%),验证队列中分别为 77.4% (95% CI,63.9%-89.3%) 和 80.0% (95% CI,72.0%-86.7%)。 结论: 基于计算机断层扫描的放射组学特征对良恶性不确定小结节显示出良好的鉴别能力。

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影响因子:6.93
发表时间:2020-01-15
DOI:10.1002/ijc.32532
作者列表:["Hata A","Nakajima T","Matsusaka K","Fukuyo M","Morimoto J","Yamamoto T","Sakairi Y","Rahmutulla B","Ota S","Wada H","Suzuki H","Matsubara H","Yoshino I","Kaneda A"]

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影响因子:6.93
发表时间:2020-01-01
DOI:10.1002/ijc.32530
作者列表:["Zhang L","Yang Y","Chai L","Bu H","Yang Y","Huang H","Ran J","Zhu Y","Li L","Chen F","Li W"]

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