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Published in: European Radiology 6/2019

01-06-2019 | Gastrointestinal

Prognostic value of computed tomography radiomics features in patients with gastric cancer following curative resection

Authors: Wuchao Li, Liwen Zhang, Chong Tian, Hui Song, Mengjie Fang, Chaoen Hu, Yali Zang, Ying Cao, Shiyuan Dai, Fang Wang, Di Dong, Rongpin Wang, Jie Tian

Published in: European Radiology | Issue 6/2019

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Abstract

Objectives

The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric cancer who had undergone radical resection.

Methods

A total of 181 patients with gastric cancer who had undergone radical resection were enrolled in this retrospective study. The association between the R-signature and overall survival (OS) was assessed in the primary cohort and verified in the validation cohort. Furthermore, the performance of a radiomics nomogram integrating the R-signature and significant clinicopathological risk factors was evaluated.

Results

The R-signature, which consisted of six imaging features, stratified patients with gastric cancer who had undergone radical resection into two prognostic risk groups in both cohorts. The radiomics nomogram incorporating R-signature and significant clinicopathological risk factors (T stage, N stage, and differentiation) exhibited significant prognostic superiority over clinical nomogram and R-signature alone (Harrell concordance index, 0.82 vs 0.71 and 0.82 vs 0.74, respectively, p < 0.001 in both analyses). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the radiomics nomogram for clinical practice.

Conclusions

The R-signature could be used to stratify patients with gastric cancer following radical resection into high- and low-risk groups. Furthermore, the radiomics nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling.

Key Points

Radiomics can stratify the gastric cancer patients following radical resection into high- and low-risk groups.
Radiomics can improve the prognostic value of TNM staging system.
Radiomics may facilitate personalized treatment of gastric cancer patients.
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Literature
1.
go back to reference Fitzmaurice C, Dicker D, Pain A et al (2015) The global burden of cancer 2013. JAMA Oncol 1:505–527CrossRefPubMed Fitzmaurice C, Dicker D, Pain A et al (2015) The global burden of cancer 2013. JAMA Oncol 1:505–527CrossRefPubMed
2.
go back to reference Tegels JJ, De Maat MF, Hulsewé KW, Hoofwijk AG, Stoot JH (2014) Improving the outcomes in gastric cancer surgery. World J Gastroenterol 20:13692–13704CrossRefPubMedPubMedCentral Tegels JJ, De Maat MF, Hulsewé KW, Hoofwijk AG, Stoot JH (2014) Improving the outcomes in gastric cancer surgery. World J Gastroenterol 20:13692–13704CrossRefPubMedPubMedCentral
3.
go back to reference Bang YJ, Kim YW, Yang HK et al (2012) Adjuvant capecitabine and oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): a phase 3 open-label, randomised controlled trial. Lancet 379:315–321 Bang YJ, Kim YW, Yang HK et al (2012) Adjuvant capecitabine and oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): a phase 3 open-label, randomised controlled trial. Lancet 379:315–321
5.
go back to reference Bando E, Makuuchi R, Tokunaga M, Tanizawa Y, Kawamura T, Terashima M (2017) Impact of clinical tumor-node-metastasis staging on survival in gastric carcinoma patients receiving surgery. Gastric Cancer 20:448–456CrossRefPubMed Bando E, Makuuchi R, Tokunaga M, Tanizawa Y, Kawamura T, Terashima M (2017) Impact of clinical tumor-node-metastasis staging on survival in gastric carcinoma patients receiving surgery. Gastric Cancer 20:448–456CrossRefPubMed
7.
go back to reference Fridman WH, Mlecnik B, Bindea G, Pagès F, Galon J (2011) Immunosurveillance in human non-viral cancers. Curr Opin Immunol 23:272–278CrossRefPubMed Fridman WH, Mlecnik B, Bindea G, Pagès F, Galon J (2011) Immunosurveillance in human non-viral cancers. Curr Opin Immunol 23:272–278CrossRefPubMed
8.
go back to reference Sohn BH, Hwang JE, Jang HJ et al (2017) Clinical significance of four molecular subtypes of gastric cancer identified by The Cancer Genome Atlas Project. Clin Cancer Res 23:4441–4449 Sohn BH, Hwang JE, Jang HJ et al (2017) Clinical significance of four molecular subtypes of gastric cancer identified by The Cancer Genome Atlas Project. Clin Cancer Res 23:4441–4449
9.
go back to reference Liang P, Ren XC, Gao JB, Chen KS, Xu X (2017) Iodine concentration in spectral CT: assessment of prognostic determinants in patients with gastric adenocarcinoma. AJR Am J Roentgenol 209:1033–1038 Liang P, Ren XC, Gao JB, Chen KS, Xu X (2017) Iodine concentration in spectral CT: assessment of prognostic determinants in patients with gastric adenocarcinoma. AJR Am J Roentgenol ​​209:1033–1038
10.
go back to reference Van Cutsem E, Sagaert X, Topal B, Haustermans K, Prenen H (2016) Gastric cancer. Lancet 388:2654–2664CrossRefPubMed Van Cutsem E, Sagaert X, Topal B, Haustermans K, Prenen H (2016) Gastric cancer. Lancet 388:2654–2664CrossRefPubMed
11.
go back to reference Komori M, Asayama Y, Fujita N et al (2013) Extent of arterial tumor enhancement measured with preoperative MDCT gastrography is a prognostic factor in advanced gastric cancer after curative resection. AJR Am J Roentgenol 201:W253–W261CrossRefPubMed Komori M, Asayama Y, Fujita N et al (2013) Extent of arterial tumor enhancement measured with preoperative MDCT gastrography is a prognostic factor in advanced gastric cancer after curative resection. AJR Am J Roentgenol 201:W253–W261CrossRefPubMed
13.
go back to reference Verma V, Simone CB 2nd, Krishnan S, Lin SH, Yang J, Hahn SM (2017) The rise of radiomics and implications for oncologic management. J Natl Cancer Inst 109:djx055 Verma V, Simone CB 2nd, Krishnan S, Lin SH, Yang J, Hahn SM (2017) The rise of radiomics and implications for oncologic management. J Natl Cancer Inst 109:djx055
14.
go back to reference Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446CrossRefPubMedPubMedCentral Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446CrossRefPubMedPubMedCentral
15.
go back to reference Huang Y, Liu Z, He L et al (2016) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281:947–957CrossRefPubMed Huang Y, Liu Z, He L et al (2016) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281:947–957CrossRefPubMed
18.
go back to reference Giganti F, Antunes S, Salerno A et al (2017) Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol 27:1831–1839CrossRefPubMed Giganti F, Antunes S, Salerno A et al (2017) Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol 27:1831–1839CrossRefPubMed
19.
go back to reference Liu S, Liu S, Ji C et al (2017) Application of CT texture analysis in predicting histopathological characteristics of gastric cancers. Eur Radiol 27:4951–4959CrossRefPubMed Liu S, Liu S, Ji C et al (2017) Application of CT texture analysis in predicting histopathological characteristics of gastric cancers. Eur Radiol 27:4951–4959CrossRefPubMed
20.
go back to reference Weir JP (2005) Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res 19:231–240PubMed Weir JP (2005) Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res 19:231–240PubMed
21.
go back to reference Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Methodol 58:267–288 Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Methodol 58:267–288
22.
go back to reference Bozdogan H (1987) Model selection and Akaike’s information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52:345–370CrossRef Bozdogan H (1987) Model selection and Akaike’s information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52:345–370CrossRef
24.
go back to reference Morris LG, Riaz N, Desrichard A et al (2016) Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget 7:10051PubMedPubMedCentral Morris LG, Riaz N, Desrichard A et al (2016) Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget 7:10051PubMedPubMedCentral
25.
go back to reference Sala E, Mema E, Himoto Y et al (2017) Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol 72:3–10CrossRefPubMed Sala E, Mema E, Himoto Y et al (2017) Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol 72:3–10CrossRefPubMed
26.
go back to reference Zhang J, Fujimoto J, Zhang J et al (2014) Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science 346:256–259CrossRefPubMedPubMedCentral Zhang J, Fujimoto J, Zhang J et al (2014) Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science 346:256–259CrossRefPubMedPubMedCentral
27.
go back to reference Rutman AM, Kuo MD (2009) Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol 70:232–241CrossRefPubMed Rutman AM, Kuo MD (2009) Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol 70:232–241CrossRefPubMed
28.
29.
go back to reference Cao Y, Liu H, Zhang H et al (2017) CXC chemokine receptor 1 predicts postoperative prognosis and chemotherapeutic benefits for TNM II and III resectable gastric cancer patients. Oncotarget 8:20328PubMed Cao Y, Liu H, Zhang H et al (2017) CXC chemokine receptor 1 predicts postoperative prognosis and chemotherapeutic benefits for TNM II and III resectable gastric cancer patients. Oncotarget 8:20328PubMed
30.
Metadata
Title
Prognostic value of computed tomography radiomics features in patients with gastric cancer following curative resection
Authors
Wuchao Li
Liwen Zhang
Chong Tian
Hui Song
Mengjie Fang
Chaoen Hu
Yali Zang
Ying Cao
Shiyuan Dai
Fang Wang
Di Dong
Rongpin Wang
Jie Tian
Publication date
01-06-2019
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 6/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-018-5861-9

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