Prediction of pathological nodal involvement by CT-based Radiomic features of the primary tumor in patients with clinically node-negative peripheral lung adenocarcinomas.
Prediction of pathological nodal involvement by CT-based Radiomic features of the primary tumor in patients with clinically node-negative peripheral lung adenocarcinomas.
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基于 CT 的临床淋巴结阴性周围型肺腺癌患者原发肿瘤的放射组学特征预测病理淋巴结受累
DOI：
10.1002/mp.12901
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发表时间：
2018-06
期刊：
影响因子：
3.8
通讯作者：
Gillies RJ
中科院分区：
文献类型：
--
作者：
Liu Y;Kim J;Balagurunathan Y;Hawkins S;Stringfield O;Schabath MB;Li Q;Qu F;Liu S;Garcia AL;Ye Z;Gillies RJ
探讨基于计算机断层扫描（CT）的原发性肿瘤放射组学特征预测临床淋巴结阴性（N 0）周围型肺腺癌病理淋巴结受累的潜力。回顾性分析187例经术前CT扫描并接受系统淋巴结清扫的临床N 0周围型肺腺癌患者。提取了219个原发性肺肿瘤的定量三维放射学特征，同时对9个放射学语义特征进行了评价。单因素和多因素Logistic回归分析用于探讨这些特征在预测病理性淋巴结受累中的作用。比较多因素logistic回归模型的ROC曲线下面积（AUC）。153例患者的病理N 0状态，34例患者的病理淋巴结转移。在单因素分析中，裂隙附着和17个放射学特征与病理性淋巴结受累显著相关。多变量分析显示，胸膜回缩（p=0.048）和裂隙附着（p=0.023）的语义特征是病理性淋巴结受累的重要预测因子（AUC=0.659）;放射学特征F185（直方图SD层1）（p=0.0001）是病理性淋巴结受累的独立预后因素（AUC= 0.73）。结合放射组学特征和语义特征建立的逻辑回归模型显示最高AUC为0.758（95%CI：0.685-0.831），5倍交叉验证法计算的AUC值为0.737（95%CI：0.73 - 0.744）。通过语义学和放射组学描述原发性肺肿瘤的特征可以提供临床N 0周围型肺腺癌病理淋巴结受累的信息。
To investigate the potential of computed-tomography (CT) based radiomic features of primary tumors to predict pathological nodal involvement in clinically node-negative (N0) peripheral lung adenocarcinomas. 187 patients with clinical N0 peripheral lung adenocarcinomas who underwent preoperative CT scan and subsequently received systematic lymph node dissection were retrospectively reviewed. 219 quantitative 3D radiomic features of primary lung tumor were extracted; meanwhile, 9 radiological semantic features were evaluated. Univariate and multivariate logistic regression analysis were used to explore the role of these features in predicting pathological nodal involvement. The areas under the ROC curves (AUCs) were compared between multivariate logistic regression models. 153 patients had pathological N0 status and 34 had pathological lymph node metastasis. On univariate analysis, fissure attachment and 17 radiomic features were significantly associated with pathological nodal involvement. Multivariate analysis revealed that semantic features of pleural retraction (p=0.048) and fissure attachment (p=0.023) were significant predictors of pathological nodal involvement (AUC=0.659); and the radiomic feature F185 (Histogram SD Layer 1) (p=0.0001) was an independent prognostic factor of pathological nodal involvement (AUC= 0.73). A logistic regression model produced from combining radiomic feature and semantic feature showed the highest AUC of 0.758 (95% CI: 0.685-0.831), and the AUC value computed by 5-fold cross-validation method was 0.737 (95% CI: 0.73 – 0.744). Features derived on primary lung tumor described by semantic and radiomic could provide information of pathological nodal involvement in clinical N0 peripheral lung adenocarcinomas.
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DOI：
10.1016/j.radonc.2016.04.004
发表时间：
2016-06
期刊：
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
影响因子：
--
作者：
Coroller TP;Agrawal V;Narayan V;Hou Y;Grossmann P;Lee SW;Mak RH;Aerts HJ
通讯作者：
Aerts HJ
影响因子：
3.7
作者：
Grove O;Berglund AE;Schabath MB;Aerts HJ;Dekker A;Wang H;Velazquez ER;Lambin P;Gu Y;Balagurunathan Y;Eikman E;Gatenby RA;Eschrich S;Gillies RJ
通讯作者：
Gillies RJ
影响因子：
5
作者：
Balagurunathan, Yoganand;Gu, Yuhua;Gillies, Robert J.
通讯作者：
Gillies, Robert J.
影响因子：
3.4
作者：
Doddoli, C;Aragon, A;Thomas, P
通讯作者：
Thomas, P
影响因子：
20.4
作者：
Goldstraw, Peter;Chansky, Kari;Bolejack, Vanessa
通讯作者：
Bolejack, Vanessa