Health and Technology

, Volume 9, Issue 5, pp 757–763 | Cite as

Application of machine learning techniques to analyze anastomosis integrity after Total gastrectomy for prediction of clinical leakage

  • Sebahattin Celik
  • Ayesha SohailEmail author
  • Shaina Ashraf
  • Arooba Arshad
Original Paper


Intraoperative testing (IT) is used to confirm the integrity of gastrointestinal anastomosis. Clinical trials are available in the literature to support the fact that methylene blue can identify the leaks, and can thus help in minimizing the postoperative ratio of clinical leaks after total gastrectomy. In the recent literature, machine learning tools have been used very successfully to investigate the hypothesis of such complex clinical trials, where incomplete data is available. In this article, data obtained from a clinical study, is analyzed using machine learning, to verify whether or not the methylene blue test can accurately identify the leaks and to predict future outcomes. Furthermore, a comparative study based on most robust machine learning solvers is presented in this article to identify the most appropriate machine learning technique(s) for future applications. We have considered the data (over a period starting from Jan 2007 till Dec 2014) based on the total gastrostomies (TG), where methylene blue test was applied. Data was obtained from 198 patients having gastric cancer. Out of 198, 108 cases went through methylene blue test done by a nasojejunal tube while no test was carried out for rest of 90 cases. Intraoperative leakage rate, mortality rate, length of hospitalization and postoperative clinical leakage rate were the measured outcomes. To analyze the data and to predict whether there will be a leak or not, machine learning techniques were applied and the accuracy was compared. The main objective of this research is to predict the clinical leakage after applying methylene blue test on gastric cancer patients. This objective is successfully achieved by implementing six machine learning approaches. Case specific machine learning approaches are discussed to evaluate post clinical leakage rate and radio leakage rate. From our analysis, we have concluded that the prediction of intraoperative leak, post clinical leak and radio leak is possible with the aid of different machine learning techniques. An important conclusion drawn from this study is that a single machine learning technique can not accurately predict different stages of leak, since the accuracy of the technique depends on the specification of clinical data that varies from stage to stage.


Machine learning Anastomosis Predicting clinical leak Intraoperative leakage rate Forecasting 


Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest.

Ethical approval

Not Applicable.

Informed consent

Not Applicable.


  1. 1.
    Marek V, Durdik Š (2017) Gastric cancer with liver metastasis (gclm) and the importance of dormant cancer stem cells, in Gastric Cancer, InTechGoogle Scholar
  2. 2.
    Alici S, İzmirli M, Doğan E. Yüzüncü yıl üniversitesi tıp fakültesi tıbbi onkoloji bilim dalı’na başvuran kanser hastalarının epidemiyolojik değerlendirilmesi. Türk Onkoloji Dergisi. 2016;21(2):87–97.Google Scholar
  3. 3.
    Artac M (2016) “,” tech. rep.
  4. 4.
    Celik S, Almalı N, Aras A, Yılmaz Ö, Kızıltan R. Intraoperatively testing the anastomotic integrity of esophagojejunostomy using methylene blue. Scand J Surg. 2017;106(1):62–7.CrossRefGoogle Scholar
  5. 5.
    Migita K, Takayama T, Matsumoto S, Wakatsuki K, Enomoto K, Tanaka T, et al. Risk factors for esophagojejunal anastomotic leakage after elective gastrectomy for gastric cancer. J Gastrointest Surg. 2012;16(9):1659–65.CrossRefGoogle Scholar
  6. 6.
    Tegels JJ, De Maat MF, Hulsewé KW, Hoofwijk AG, Stoot JH. Improving the outcomes in gastric cancer surgery. World J Gastroenterol: WJG. 2014;20(38):13692.CrossRefGoogle Scholar
  7. 7.
    Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527–54.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Sohail A, Li Z (2018) Computational approaches in biomedical Nano-engineering. John Wiley & SonsGoogle Scholar
  9. 9.
    Popivanov G, Tabakov M, Mantese G, Cirocchi R, Piccinini I, D’Andrea V, Covarelli P, Boselli C, Barberini F, Tabola R, Pietro U (2018) Surgical treatment of gastrointestinal stromal tumors of the duodenum: a literature review. Translational gastroenterology and hepatology, 3Google Scholar
  10. 10.
    Giuliani A, Romano L, Papale E, Puccica I, Di MF, Salvatorelli A, Cianca G, Schietroma M, Amicucci G (2019) Complications post-laparoscopic sleeve gastric resection: review of surgical technique. Minerva ChirGoogle Scholar
  11. 11.
    Agbelusi O (2014) “Development of a predictive model for survival of hiv/aids patients in south-western nigeria,” Unpublished MPhil Thesis, Obafemi Awolowo University, Ile-Ife, Nigeria Google Scholar
  12. 12.
    Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal. 2015;13:8–17.CrossRefGoogle Scholar
  13. 13.
    Byrne M, Abu-Rustum N, Usiak S, Frame J, Aslam A, Ogden S, et al. Risk prediction model for surgical site infections in patients undergoing open gynecologic cancer surgery following the implementation of a reduction bundle at a comprehensive cancer center. Gynecol Oncol. 2018;149:215.CrossRefGoogle Scholar
  14. 14.
    Abdel-Zaher AM, Eldeib AM. Breast cancer classification using deep belief networks. Expert Syst Appl. 2016;46:139–44.CrossRefGoogle Scholar
  15. 15.
    Gbenga DE, Christopher N, Yetunde DC. Performance comparison of machine learning techniques for breast cancer detection. Nova. 2017;6(1):1–8.Google Scholar
  16. 16.
    Chaurasia V, Pal S (2017) A novel approach for breast cancer detection using data mining techniquesGoogle Scholar
  17. 17.
    Zhang P-W, Chen L, Huang T, Zhang N, Kong X-Y, Cai Y-D. Classifying ten types of major cancers based on reverse phase protein array profiles. PLoS One. 2015;10(3):e0123147.CrossRefGoogle Scholar
  18. 18.
    Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.CrossRefGoogle Scholar
  19. 19.
    Hayward J, Alvarez SA, Ruiz C, Sullivan M, Tseng J, Whalen G. Machine learning of clinical performance in a pancreatic cancer database. Artif Intell Med. 2010;49(3):187–95.CrossRefGoogle Scholar
  20. 20.
    Archetti F, Castelli M, Giordani I, Vanneschi L (2010) “Classification of colon tumor tissues using genetic programming,” In Artificial Life and Evolutionary Computation, pp. 49–58, world scientificGoogle Scholar
  21. 21.
    Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis. Cancer Informat. 2006;2:117693510600200030.CrossRefGoogle Scholar
  22. 22.
    Li Z, Zhang D, Dai Y, Dong J, Wu L, Li Y, et al. Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: a pilot study. Chin J Cancer Res. 2018;30(4):406.CrossRefGoogle Scholar
  23. 23.
    Sherin L, Sohail A, Shujaat S. Time-dependent AI-modeling of the anticancer efficacy of synthesized gallic acid analogues. Comput Biol Chem. 2019;79:137–46.CrossRefGoogle Scholar
  24. 24.
    Sohail A (2019) Inference of biomedical data sets using Bayesian machine learning Biomedical Engineering Google Scholar

Copyright information

© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of General Surgery, Faculty of MedicineYuzuncu Yl UniversityVanTurkey
  2. 2.Department of MathematicsComsats University IslamabadIslamabadPakistan
  3. 3.Department of Computer ScienceComsats University IslamabadIslamabadPakistan

Personalised recommendations