Computed tomography texture analysis in patients with gastric cancer: a quantitative imaging biomarker for preoperative evaluation before neoadjuvant chemotherapy treatment.

Purpose: The aim of the study is to explore the role of computed tomography texture analysis (CT-TA) for predicting clinical T and N stages and tumor grade before neoadjuvant chemotherapy treatment in gastric cancer (GC) patients during the preoperative period.Materials and Methods: CT images of 114...

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Detalles Bibliográficos
Publicado en:Japanese Journal of Radiology Vol. 38; no. 6; pp. 553 - 561
Autores principales: Yardimci, Aytul Hande, Sel, Ipek, Bektas, Ceyda Turan, Yarikkaya, Enver, Dursun, Nevra, Bektas, Hasan, Afsar, Cigdem Usul, Gursu, Rıza Umar, Yardimci, Veysi Hakan, Ertas, Elif, Kilickesmez, Ozgur
Formato: Journal Article
Publicado: Springer Nature Jun2020
Acceso en línea:Ver este registro en EBSCOhost
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Sumario:Purpose: The aim of the study is to explore the role of computed tomography texture analysis (CT-TA) for predicting clinical T and N stages and tumor grade before neoadjuvant chemotherapy treatment in gastric cancer (GC) patients during the preoperative period.Materials and Methods: CT images of 114 patients with GC were included in this retrospective study. Following pre-processing steps, textural features were extracted using MaZda software in the portal venous phase. We evaluated and analyzed texture features of six principal categories for differentiating between T stages (T1,2 vs T3,4), N stages (N+ vs N-) and grades (low-intermediate vs. high). Classification was performed based on texture parameters with high model coefficients in linear discriminant analysis (LDA).Results: Dimension-reduction steps yielded five textural features for T stage, three for N stage and two for tumor grade. The discriminatory capacities of T stage, N stage and tumor grade were 90.4%, 81.6% and 64.5%, respectively, when LDA algorithm was employed.Conclusion: CT-TA yields potentially useful imaging biomarkers for predicting the T and N stages of patients with GC and can be used for preoperative evaluation before neoadjuvant treatment planning.