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Development and evaluation of a ceMDCT-based preoperative risk stratification model to predict disease-free survival after radical surgery in patients with gastric cancer

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Abstract

Purpose

To develop and evaluate a preoperative risk stratification model for predicting disease-free survival (DFS) based on contrast-enhanced multidetector computed tomography (ceMDCT) images in patients with gastric cancer (GC) undergoing radical surgery.

Methods

We retrospectively enrolled patients with GC who underwent ceMDCT followed by radical surgery. A preoperative risk stratification model was constructed (including risk factor selection, risk status scoring, and risk level assignment) using Cox proportional hazard regression and log-rank analyses in the training cohort; the model was tested in the validation cohort. A nomogram was used to compare the preoperative risk stratification model with a postoperative DFS prediction model.

Results

A total of 462 patients (training/validation: 271/191) were included. The ceMDCT features of T category (score of 0 or 2), N category (0, 1, 2, or 3), extramural vessel invasion (0 or 2), and tumor location (0 or 1) were selected to construct the preoperative risk stratification model, with 4 risk levels defined based on risk score. There were significant differences in DFS among the risk levels in both cohorts (p < 0.001). The predictive value of the preoperative model was similar to that of the postoperative model, with concordance indices of 0.791 (95% CI, 0.743–0.837) and 0.739 (95% CI, 0.666–0.812), respectively, in the training cohort and 0.762 (95% CI, 0.696–0.828) and 0.738 (95% CI, 0.684–0.792), respectively, in the validation cohort.

Conclusion

A preoperative risk stratification model based on ceMDCT images could be used to predict DFS and thus classify GC cases into various risk levels.

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References

  1. Bu, Z., J. Ji (2013) A current view of gastric cancer in China. Translational Gastrointestinal Cancer 1–4.

  2. Ychou, M., et al., Perioperative chemotherapy compared with surgery alone for resectable gastroesophageal adenocarcinoma: an FNCLCC and FFCD multicenter phase III trial. J Clin Oncol, 2011. 29(13): p. 1715-1721.

    Article  CAS  Google Scholar 

  3. Cunningham, D., et al., Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer. N Engl J Med, 2006. 355(1): p. 11-20.

    Article  CAS  Google Scholar 

  4. National Comprehensive Cancer Network. Gastric Cancer, Version 2019. . 2019 March 14, 2020]; Available from: https://www.nccn.org/professionals/physician_gls/pdf/gastric.pdf.

  5. Smyth, E.C., et al., Gastric cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2016. 27(suppl 5): p. v38-v49.

    Article  CAS  Google Scholar 

  6. Wang, F.H., et al., The Chinese Society of Clinical Oncology (CSCO): clinical guidelines for the diagnosis and treatment of gastric cancer. Cancer Commun (Lond), 2019. 39(1): p. 10.

    Article  Google Scholar 

  7. Japanese Gastric Cancer Association, Japanese gastric cancer treatment guidelines 2014 (ver. 4). Gastric Cancer, 2017. 20(1): p. 1-19.

    Article  Google Scholar 

  8. Bando, E., et al., Validation of the prognostic impact of the new tumor-node-metastasis clinical staging in patients with gastric cancer. Gastric Cancer, 2019. 22(1): p. 123-129.

    Article  Google Scholar 

  9. Reddavid, R., et al., Neoadjuvant chemotherapy for gastric cancer. Is it a must or a fake? World J Gastroenterol, 2018. 24(2): p. 274-289.

    Article  Google Scholar 

  10. Park, S., et al., Prospective Evaluation of Changes in Tumor Size and Tumor Metabolism in Patients with Advanced Gastric Cancer Undergoing Chemotherapy: Association and Clinical Implication. J Nucl Med, 2017. 58(6): p. 899-904.

    Article  CAS  Google Scholar 

  11. Cheng, J., et al., CT-Detected Extramural Vessel Invasion and Regional Lymph Node Involvement in Stage T4a Gastric Cancer for Predicting Progression-Free Survival. AJR Am J Roentgenol, 2019: p. 1-7.

    Article  Google Scholar 

  12. Huang, J.Y., et al., Borrmann type IV gastric cancer should be classified as pT4b disease. J Surg Res, 2016. 203(2): p. 258-67.

    Article  Google Scholar 

  13. Smith, N.J., et al., Prognostic significance of magnetic resonance imaging-detected extramural vascular invasion in rectal cancer. Br J Surg, 2008. 95(2): p. 229-36.

    Article  CAS  Google Scholar 

  14. An, J.Y., et al., Borrmann type IV: an independent prognostic factor for survival in gastric cancer. J Gastrointest Surg, 2008. 12(8): p. 1364-9.

    Article  Google Scholar 

  15. Kim, T.U., et al., Prognostic Value of Computed Tomography-Detected Extramural Venous Invasion to Predict Disease-Free Survival in Patients With Gastric Cancer. J Comput Assist Tomogr, 2016. 41(3): p. 430.

    Article  Google Scholar 

  16. Iasonos, A., et al., How to build and interpret a nomogram for cancer prognosis. J Clin Oncol, 2008. 26(8): p. 1364-1370.

    Article  Google Scholar 

  17. Amin, M.B., et al., AJCC Cancer Staging Manual, 8th ed. 2017, Chicago, IL: American College of Surgeons.

  18. De Manzoni, G., et al., The Italian Research Group for Gastric Cancer (GIRCG) guidelines for gastric cancer staging and treatment: 2015. Gastric Cancer, 2017. 20(1): p. 20-30.

    Article  Google Scholar 

  19. Seevaratnam, R., et al., How useful is preoperative imaging for tumor, node, metastasis (TNM) staging of gastric cancer? A meta-analysis. Gastric Cancer, 2012. 15(1): p. S3-S18.

    Article  Google Scholar 

  20. Tan, C.H., et al., Extramural venous invasion by gastrointestinal malignancies: CT appearances. Abdom Imaging, 2011. 36(5): p. 491-502.

    Article  Google Scholar 

  21. Shah, M.A., et al., Molecular classification of gastric cancer: a new paradigm. Clin Cancer Res, 2011. 17(9): p. 2693-2701.

    Article  CAS  Google Scholar 

  22. Yan, C., et al., Preoperative Gross Classification of Gastric Adenocarcinoma: Comparison of Double Contrast-Enhanced Ultrasound and Multi-Detector Row CT. Ultrasound Med Biol, 2016. 42(7): p. 1431-40.

    Article  Google Scholar 

  23. Alatengbaolide, et al., Lymph node ratio is an independent prognostic factor in gastric cancer after curative resection (R0) regardless of the examined number of lymph nodes. Am J Clin Oncol, 2013. 36(4): p. 325-330.

    Article  CAS  Google Scholar 

  24. Feng, F., et al., Prognostic value of differentiation status in gastric cancer. BMC Cancer, 2018. 18(1): p. 865.

    Article  Google Scholar 

  25. von Rahden, B.H., et al., Lymphatic vessel invasion as a prognostic factor in patients with primary resected adenocarcinomas of the esophagogastric junction. J Clin Oncol, 2005. 23(4): p. 874-879.

    Article  Google Scholar 

  26. Saito, H., et al., Macroscopic tumor size as a simple prognostic indicator in patients with gastric cancer. Am J Surg, 2006. 192(3): p. 296-300.

    Article  Google Scholar 

  27. Sullivan, L.M., J.M. Massaro, and R.B. D'Agostino, Sr, Presentation of multivariate data for clinical use: The Framingham Study risk score functions. Stat Med, 2004. 23(10): p. 1631-60.

    Article  Google Scholar 

  28. Landis, J.R. and G.G. Koch, The measurement of observer agreement for categorical data. Biometrics, 1977. 33: p. 159-174.

    Article  CAS  Google Scholar 

  29. Battersby, N.J., et al., Prospective Validation of a Low Rectal Cancer Magnetic Resonance Imaging Staging System and Development of a Local Recurrence Risk Stratification Model: The MERCURY II Study. Ann Surg, 2016. 263(4): p. 751-60.

    Article  Google Scholar 

  30. Giganti, F., et al., Preoperative locoregional staging of gastric cancer: is there a place for magnetic resonance imaging? Prospective comparison with EUS and multidetector computed tomography. Gastric Cancer, 2015. 19(1): p. 216-225.

    Article  Google Scholar 

  31. Cheng, J., et al., The prognostic significance of extramural venous invasion detected by multiple-row detector computed tomography in stage III gastric cancer. Abdominal Radiology, 2016. 41(7): p. 1219-1226.

    Article  Google Scholar 

  32. Hirabayashi, S., et al., Development and external validation of a nomogram for overall survival after curative resection in serosa-negative, locally advanced gastric cancer. Ann Oncol, 2014. 25(6): p. 1179-84.

    Article  CAS  Google Scholar 

  33. Park, C.J., et al., Prognostic significance of preoperative CT findings in patients with advanced gastric cancer who underwent curative gastrectomy. PLoS One, 2018. 13(8): p. e0202207.

    Article  Google Scholar 

  34. Li, W., et al., Prognostic value of computed tomography radiomics features in patients with gastric cancer following curative resection. Eur Radiol, 2018.

    Article  Google Scholar 

  35. Zhang, W., et al., Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer. Radiother Oncol, 2020. 145: p. 13-20.

    Article  CAS  Google Scholar 

  36. Li, J., et al., Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer. Front Oncol, 2020. 10: p. 552270.

    Article  Google Scholar 

  37. Zhang, L., et al., A deep learning risk prediction model for overall survival in patients with gastric cancer: A multicenter study. Radiother Oncol, 2020. 150: p. 73-80.

    Article  Google Scholar 

  38. Jiang, Y., et al., Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer. EBioMedicine, 2018. 36: p. 171-182.

    Article  Google Scholar 

  39. Han, D.-S., et al., Nomogram predicting long-term survival after d2 gastrectomy for gastric cancer. J Clin Oncol, 2012. 30(31): p. 3834-3840.

    Article  Google Scholar 

  40. Kattan, M.W., et al., Postoperative nomogram for disease-specific survival after an R0 resection for gastric carcinoma. J Clin Oncol, 2003. 21(19): p. 3647-3650.

    Article  Google Scholar 

  41. Tan, P. and K.G. Yeoh, Genetics and Molecular Pathogenesis of Gastric Adenocarcinoma. Gastroenterology, 2015. 149(5): p. 1153-1162.e3.

    Article  CAS  Google Scholar 

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Acknowledgements

We appreciate Xiaohua Zhou, Ph.D (azhou@bicmr.pku.edu.cn) for statistical consulting of this article. We also appreciate Ms. Megan Griffiths (megangriffiths97@gmail.com) for conducting a linguistic revision of this article.

Funding

This work was supported by the National Natural Science Foundation of China (Grant Number 81901819) and Peking University People's Hospital Research and Development Funds (RDX2019-01).

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Author notes

  1. Caizhen Feng and Jin Cheng have contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Peking University People’s Hospital, 11 Xizhimen South St, Beijing, 100044, China

    Caizhen Feng, Jin Cheng, Xiao Zeng, Nan Hong & Yi Wang

  2. Department of Pathology, Peking University People’s Hospital, 11 Xizhimen South St, Beijing, 100044, China

    Yinli Zhang

  3. Department of Gastrointestinal Surgery, Peking University People’s Hospital, 11 Xizhimen South St, Beijing, 100044, China

    Yingjiang Ye

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  1. Caizhen Feng
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Contributions

CF Investigation, Methodology, Writing—Original draft preparation; JC Conceptualization, Original draft preparation, Methodology, Funding acquisition; ZX Methodology; YZ Investigation; YY, NH Supervision; YW Conceptualization, Writing—Reviewing and Editing.

Corresponding author

Correspondence to Yi Wang.

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The authors declare no conflict of interest.

Ethical approval

This retrospective study was approved by our local institutional review boards, which waived the requirement for obtaining informed consent.

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Feng, C., Cheng, J., Zeng, X. et al. Development and evaluation of a ceMDCT-based preoperative risk stratification model to predict disease-free survival after radical surgery in patients with gastric cancer. Abdom Radiol 46, 4079–4089 (2021). https://doi.org/10.1007/s00261-021-03049-0

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