Tubular gastric adenocarcinoma: machine learning-based CT texture analysis for predicting lymphovascular and perineural invasion.
Purpose: Lymphovascular invasion (LVI) and perineural invasion (PNI) are associated with poor prognosis in gastric cancers. In this work, we aimed to investigate the potential role of computed tomography (CT) texture analysis in predicting LVI and PNI in patients with tubular gastric adenocarcinoma...
| Published in: | Diagnostic & Interventional Radiology Vol. 26; no. 6; pp. 515 - 523 |
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| Main Authors: | , , , , , , , , , , , |
| Format: | Journal Article |
| Published: |
Galenos Yayinevi Tic. LTD. STI
Nov2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146957119&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146957119 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13053825 39NM jtl: Diagnostic & Interventional Radiology issn: 13053825 maglogo: N pubinfo: dt: Nov2020 vid: 26 iid: 6 pid: 28155 pub: Galenos Yayinevi Tic. LTD. STI artinfo: ui: 146957119 146957119 NLM32990246 10.5152/dir.2020.19507 NLM32990246 146957119 ppf: 515 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Tubular gastric adenocarcinoma: machine learning-based CT texture analysis for predicting lymphovascular and perineural invasion. aug: au: Yardımcı, Aytül Hande Koçak, Burak Bektaş, Ceyda Turan Sel, İpek Yarıkkaya, Enver Dursun, Nevra Bektaş, Hasan Afşar, Çiğdem Usul Gürsu, Rıza Umar Kılıçkesmez, Özgür Turan Bektaş, Ceyda Usul Afşar, Çiğdem affil: Department of Radiology, İstanbul Training and Research Hospital, İstanbul, Turkey sug: subj: Adenocarcinoma Stomach Neoplasms Adenocarcinoma Surgery Stomach Neoplasms Surgery Probability Tomography, X-Ray Computed Retrospective Design Reproducibility of Results Scales ab: Purpose: Lymphovascular invasion (LVI) and perineural invasion (PNI) are associated with poor prognosis in gastric cancers. In this work, we aimed to investigate the potential role of computed tomography (CT) texture analysis in predicting LVI and PNI in patients with tubular gastric adenocarcinoma (GAC) using a machine learning (ML) approach.Methods: Sixty-eight patients who underwent total gastrectomy with curative (R0) resection and D2-lymphadenectomy were included in this retrospective study. Texture features were extracted from the portal venous phase CT images. Dimension reduction was first done with a reproducibility analysis by two radiologists. Then, a feature selection algorithm was used to further reduce the high-dimensionality of the radiomic data. Training and test splits were created with 100 random samplings. ML-based classifications were done using adaptive boosting, k-nearest neighbors, Naive Bayes, neural network, random forest, stochastic gradient descent, support vector machine, and decision tree. Predictive performance of the ML algorithms was mainly evaluated using the mean area under the curve (AUC) metric.Results: Among 271 texture features, 150 features had excellent reproducibility, which were included in the further feature selection process. Dimension reduction steps yielded five texture features for LVI and five for PNI. Considering all eight ML algorithms, mean AUC and accuracy ranges for predicting LVI were 0.777-0.894 and 76%-81.5%, respectively. For predicting PNI, mean AUC and accuracy ranges were 0.482-0.754 and 54%-68.2%, respectively. The best performances for predicting LVI and PNI were achieved with the random forest and Naive Bayes algorithms, respectively.Conclusion: ML-based CT texture analysis has a potential for predicting LVI and PNI of the tubular GACs. Overall, the method was more successful in predicting LVI than PNI. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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