Differential Diagnosis of Jejunum and Ileum Tumours Based on Video Capsule Endoscopy Data Using Mathematical Analysis

Aim. The aim of this study was to develop an algorithm for identifying various types of jejunum and ileum tumour lesions based on video capsule endoscopy (VCE), which can be used by physicians in the process of decision making.

Materials and methods. In the study, we analysed data on the examination and treatment of 65 patients (35 men and 30 women aged 18–80 years (mean age 46 ± 28 years)), who underwent VCE in the City Clinical Hospital No. 31 and in the Clinica K+31 during the period October 2008 — April 2017. The indications for VCE were a search for a reason of gastrointestinal bleeding, the anaemia of unknown etiology and suspected tumour of the small intestine. According to the VCE results, 181 cases of various changes in the jejunum and ileum were revealed. Each tumour object had been histologically verified before our study. Capsule endoscopy was performed using equipment produced by Olympus (Japan), MicroCam Intromedic (Korea), PillCam Given Imaging (Israel), OMOM Chongqing Jinshan Science & Technology (China).

Results. Following expert interviews, 30 signs and their gradations were identified for assessing the type of lesion in the jejunum and ileum using video capsule endoscopy images. Among them, 8 were found to be statistically significant (affecting the division of objects into groups): patient gender, gut wall/lumen deformation, path of intestinal folds, polypoid changes, vascular pattern, mucosal regularity, mucosal lobulation and colour. Using a Bayesian heterogeneous diagnostic procedure and the calculation of diagnostic factors, a three-level algorithm has been developed for the differential diagnosis of jejunum and ileum lesions.

Conclusions. The application of the proposed algorithm in clinical practice will not only allow the presence or absence of the jejunum or ileum tumour lesion to be verified, but also the type of this lesion to be determined with an accuracy of more than 86%. The developed diagnostic algorithm can support decision making by the clinician within the task of differentiating jejunum or ileum tumour lesions into three main types: benign epithelial tumours, benign non-epithelial tumours and malignant tumours. Differential diagnosis of the type of jejunum or ileum tumour lesion using the proposed diagnostic algorithm facilitates not only the development of a treatment tactic for managing such patients (dynamic observation, conservative therapy, operative treatment), but also the determination of terms (emergency, urgent, planned) and methods (endometrial luminal, laparoscopic, laparotomic) of surgical treatment. 

1. Yaitsky N.A., Sednev A.V. Tumours of the small intestine in the practice of the surgeon. In: Saveliev V.S., ed. 80 Lectures on Surgery. Moscow: Litterra, 2008 (In Rus.).

2. Drozdzal M., Seguí S., Vitrià J., Malagelada C., Azpiroz F., Radeva P. Adaptable image cuts for motility inspection using WCE. Comput Med Imaging Graph. 2013;37(1):72–80. DOI: 10.1016/j.compmedimag.2012.09.002

3. Iakovidis D.K., Tsevas S., Polydorou A. Reduction of capsule endoscopy reading times by unsupervised image mining. Comput Med Imaging Graph. 2010;34(6):471–8. DOI: 10.1016/j.compmedimag.2009.11.005

4. Satrya G.B., Muhammad R.U., Shin S.Y. Detection of small colon bleeding in wireless capsule endoscopy videos. Comput Med Imaging Graph. 2016;54:16–26. DOI: 10.1016/j.compmedimag.2016.09.005

5. Szczypinski P., Klepaczko A., Pazurek M., Daniel P. Texture and color based image segmentation and pathology detection in capsule endoscopy videos. Comput Methods Programs Biomed. 2014;113(1):396–411. DOI: 10.1016/j. cmpb.2012.09.004

6. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [Электронный ресурс]. https://www.R-project.org

7. Glantz S. Biomedical statistics. Moscow: Practica, 1998 (In Rus.).