Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study.
PURPOSE Knowing the genetic phenotype of gastrointestinal stromal tumors (GISTs) is essential for patients who receive therapy with tyrosine kinase inhibitors. The aim of this study was to develop a radiomic algorithm for predicting GISTs with KIT exon 11 mutation. METHODS We enrolled 106 patients (...
| Publicado en: | Diagnostic & Interventional Radiology Vol. 28; no. 1; pp. 29 - 39 |
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| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Galenos Yayinevi Tic. LTD. STI
Jan2022
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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=155219113&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155219113 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13053825 39NM jtl: Diagnostic & Interventional Radiology issn: 13053825 maglogo: N pubinfo: dt: Jan2022 vid: 28 iid: 1 pid: 28155 pub: Galenos Yayinevi Tic. LTD. STI artinfo: ui: 155219113 155219113 NLM35142612 10.5152/dir.2021.21600 NLM35142612 155219113 ppf: 29 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study. aug: au: Bo Liu Hao Liu Lequan Zhang Yancheng Song Shifeng Yang Ziwen Zheng Junjiang Zhao Feng Hou Jian Zhang Liu, Bo Liu, Hao Zhang, Lequan Song, Yancheng Yang, Shifeng Zheng, Ziwen Zhao, Junjiang Hou, Feng Zhang, Jian affil: Department of Colorectal Surgery, Affiliated Hospital of Qingdao University (Pingdu). sug: subj: Gastrointestinal Neoplasms Tomography, X-Ray Computed Mutation Genes Algorithms Scales ab: PURPOSE Knowing the genetic phenotype of gastrointestinal stromal tumors (GISTs) is essential for patients who receive therapy with tyrosine kinase inhibitors. The aim of this study was to develop a radiomic algorithm for predicting GISTs with KIT exon 11 mutation. METHODS We enrolled 106 patients (80 in the training set, 26 in the validation set) with clinicopathologically confirmed GISTs from two centers. Preoperative and postoperative clinical characteristics were selected and analyzed to construct the clinical model. Arterial phase, venous phase, delayed phase, and tri-phase combined radiomics algorithms were generated from the training set based on contrast-enhanced computed tomography (CE-CT) images. Various radiomics feature selection methods were used, namely least absolute shrinkage and selection operator (LASSO); minimum redundancy maximum relevance (mRMR); and generalized linear model (GLM) as a machine-learning classifier. Independent predictive factors were determined to construct preoperative and postoperative radiomics nomograms by multivariate logistic regression analysis. The performances of the clinical model, radiomics algorithm, and radiomics nomogram in distinguishing GISTs with the KIT exon 11 mutation were evaluated by area under the curve (AUC) of the receiver operating characteristics. RESULTS Of 106 patients who underwent genetic analysis, 61 had the KIT exon 11 mutation. The combined radiomics algorithm was found to be the best prediction model for differentiating the expression status of the KIT exon 11 mutation (AUC = 0.836; 95% confidence interval [CI], 0.640-0.951) in the validation set. The clinical model, and preoperative and postoperative radiomics nomograms had AUCs of 0.606 (95% CI, 0.397-0.790), 0.715 (95% CI, 0.506-0.873), and 0.679 (95% CI, 0.468-0.847), respectively, with the validation set. CONCLUSION The radiomics algorithm could distinguish GISTs with the KIT exon 11 mutation based on CE-CT images and could potentially be used for selective genetic analysis to support the precision medicine of GISTs. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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