Pathologic categorization of lung nodules: Radiomic descriptors of CT attenuation distribution patterns of solid and subsolid nodules in low-dose CT.
Pathologic categorization of lung nodules: Radiomic descriptors of CT attenuation distribution patterns of solid and subsolid nodules in low-dose CT.
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DOI：
10.1016/j.ejrad.2020.109106
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发表时间：
2020-08
影响因子：
3.3
通讯作者：
Wei, Jun
中科院分区：
文献类型：
--
作者：
Zhou, Chuan;Chan, Heang-Ping;Chughtai, Aamer;Hadjiiski, Lubomir M.;Kazerooni, Ella A.;Wei, Jun
Develop a quantitative image analysis method to characterize the heterogeneous patterns of nodule components for the classification of pathological categories of nodules. With IRB approval and permission of the National Lung Screening Trial (NLST) project, 103 subjects with low dose CT (LDCT) were used in this study. We developed a radiomic quantitative CT attenuation distribution descriptor (qADD) to characterize the heterogeneous patterns of nodule components and a hybrid model (qADD+) that combined qADD with subject demographic data and radiologist-provided nodule descriptors to differentiate aggressive tumors from indolent tumors or benign nodules with pathological categorization as reference standard. The classification performances of qADD and qADD+ were evaluated and compared to the Brock and the Mayo Clinic Models by analysis of the area under the receiver operating characteristic curve (AUC). The radiomic features were consistently selected into qADDs to differentiate pathological invasive nodules from (1) preinvasive nodules, (2) benign nodules, and (3) the group of preinvasive and benign nodules, achieving test AUCs of 0.847±0.002, 0.842±0.002 and 0.810±0.001, respectively. The qADD+ obtained test AUCs of 0.867±0.002, 0.888±0.001 and 0.852±0.001, respectively, which were higher than both the Brock and the Mayo Clinic Models. The pathologic invasiveness of lung tumors could be categorized according to the CT attenuation distribution patterns of the nodule components manifested on LDCT images, and the majority of invasive lung cancers could be identified at baseline LDCT scans.
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影响因子：
4.6
作者：
Oakden-Rayner L;Carneiro G;Bessen T;Nascimento JC;Bradley AP;Palmer LJ
通讯作者：
Palmer LJ
影响因子：
--
作者：
Swensen, SJ;Silverstein, MD;Edell, ES
通讯作者：
Edell, ES
DOI：
10.1038/modpathol.2012.106
发表时间：
2012-12
期刊：
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
影响因子：
--
作者：
Thunnissen E;Beasley MB;Borczuk AC;Brambilla E;Chirieac LR;Dacic S;Flieder D;Gazdar A;Geisinger K;Hasleton P;Ishikawa Y;Kerr KM;Lantejoul S;Matsuno Y;Minami Y;Moreira AL;Motoi N;Nicholson AG;Noguchi M;Nonaka D;Pelosi G;Petersen I;Rekhtman N;Roggli V;Travis WD;Tsao MS;Wistuba I;Xu H;Yatabe Y;Zakowski M;Witte B;Kuik DJ
通讯作者：
Kuik DJ
DOI：
10.1097/jto.0b013e318206a221
发表时间：
2011-02
期刊：
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
影响因子：
--
作者：
Travis WD;Brambilla E;Noguchi M;Nicholson AG;Geisinger KR;Yatabe Y;Beer DG;Powell CA;Riely GJ;Van Schil PE;Garg K;Austin JH;Asamura H;Rusch VW;Hirsch FR;Scagliotti G;Mitsudomi T;Huber RM;Ishikawa Y;Jett J;Sanchez-Cespedes M;Sculier JP;Takahashi T;Tsuboi M;Vansteenkiste J;Wistuba I;Yang PC;Aberle D;Brambilla C;Flieder D;Franklin W;Gazdar A;Gould M;Hasleton P;Henderson D;Johnson B;Johnson D;Kerr K;Kuriyama K;Lee JS;Miller VA;Petersen I;Roggli V;Rosell R;Saijo N;Thunnissen E;Tsao M;Yankelewitz D
通讯作者：
Yankelewitz D
影响因子：
5
作者：
Petrou, Myria;Quint, Leslie E.;Baker, Laurence H.
通讯作者：
Baker, Laurence H.