Radiomics for Classification of Lung Cancer Histological Subtypes Based on Nonenhanced Computed Tomography.
Objectives: To evaluate the performance of using radiomics method to classify lung cancer histological subtypes based on nonenhanced computed tomography images.Materials and Methods: 278 patients with pathologically confirmed lung cancer were collected, including 181 nonsmall cell lung cancer (NSCLC...
| Publicado en: | Academic Radiology Vol. 26; no. 9; pp. 1245 - 1253 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Sep2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137947629&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137947629 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10766332 T4X jtl: Academic Radiology issn: 10766332 maglogo: N pubinfo: dt: Sep2019 vid: 26 iid: 9 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 137947629 137947629 NLM30502076 137947629 10.1016/j.acra.2018.10.013 NLM30502076 137947629 ppf: 1245 ppct: 8 formats: tig: atl: Radiomics for Classification of Lung Cancer Histological Subtypes Based on Nonenhanced Computed Tomography. aug: au: E, Linning Lu, Lin Li, Li Yang, Hao Schwartz, Lawrence H. Zhao, Binsheng affil: Department of Radiology, Shanxi DAYI Hospital, Taiyuan, Shanxi, China sug: subj: Tomography, X-Ray Computed Methods Lung Neoplasms Pathology Lung Neoplasms Carcinoma, Non-Small-Cell Lung Classification Adenocarcinoma Algorithms Adenocarcinoma Classification Carcinoma, Squamous Cell Classification Carcinoma, Non-Small-Cell Lung Radiometry Carcinoma, Squamous Cell Pathology Human ROC Curve Male Carcinoma, Non-Small-Cell Lung Pathology Lung Neoplasms Classification Female Aged Aged, 80 and Over Retrospective Design Middle Age Adenocarcinoma Pathology Adult Carcinoma, Squamous Cell Pharmacokinetics Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Adult: 19-44 years Male Female ab: Objectives: To evaluate the performance of using radiomics method to classify lung cancer histological subtypes based on nonenhanced computed tomography images.Materials and Methods: 278 patients with pathologically confirmed lung cancer were collected, including 181 nonsmall cell lung cancer (NSCLC) and 97 small cell lung cancers (SCLC) patients. Among the NSCLC patients, 88 patients were adenocarcinomas (AD) and 93 patients were squamous cell carcinomas (SCC). In total, 1695 quantitative radiomic features (QRF) were calculated from the primary lung cancer tumor in each patient. To build radiomic classification model based on the extracted QRFs, several machine-learning algorithms were applied sequentially. First, unsupervised hierarchical clustering was used to exclude highly correlated QRFs; second, the minimum Redundancy Maximum Relevance feature selection algorithm was employed to select informative and nonredundant QRFs; finally, the Incremental Forward Search and Support Vector Machine classification algorithms were used to combine the selected QRFs and build the model. In our work, to study the phenotypic differences among lung cancer histological subtypes, four classification models were built. They were models of SCLC vs NSCLC, SCLC vs AD, SCLC vs SCC, and AD vs SCC. The performance of the classification models was evaluated by the area under the receiver operating characteristic curve (AUC) estimated by three-fold cross-validation.Results: The AUC (95% confidence interval) for the model of SCLC vs NSCLC was 0.741(0.678, 0.795). For the models of SCLC vs AD and SCLC vs SCC, the AUCs were 0.822(0.755, 0.875) and 0.665(0.583, 0.738), respectively. The AUC for the model of AD vs SCC was 0.655(0.570, 0.731). Several QRFs ("Law_15," "LoG_Uniformity," "GLCM_Contrast," and "Compactness Factor") that characterize tumor heterogeneity and shape were selected as the significant features to build the models.Conclusion: Our results show that phenotypic differences exist among different lung cancer histological subtypes on nonenhanced computed tomography image. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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