Copyright
©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastroenterol. Jun 8, 2023; 4(1): 10-27
Published online Jun 8, 2023. doi: 10.35712/aig.v4.i1.10
Risk factor profiles for gastric cancer prediction with respect to Helicobacter pylori: A study of a tertiary care hospital in Pakistan
Shahid Aziz, Simone König, Muhammad Umer, Tayyab Saeed Akhter, Shafqat Iqbal, Maryum Ibrar, Tofeeq Ur-Rehman, Tanvir Ahmad, Alfizah Hanafiah, Rabaab Zahra, Faisal Rasheed
Shahid Aziz, Tanvir Ahmad, Faisal Rasheed, Patients Diagnostic Lab, Isotope Application Division, Pakistan Institute of Nuclear Science and Technology, Islamabad 44000, Pakistan
Shahid Aziz, Rabaab Zahra, Department of Microbiology, Quaid-i-Azam University, Islamabad 45320, Pakistan
Shahid Aziz, Simone König, Interdisciplinary Center for Clinical Research, Core Unit Proteomics, University of Münster, Münster 48149, Germany
Muhammad Umer, Management Information System Division, Pakistan Institute of Nuclear Science and Technology, Islamabad 44000, Pakistan
Tayyab Saeed Akhter, Shafqat Iqbal, Centre for Liver and Digestive Diseases, Holy Family Hospital, Rawalpindi 46300, Pakistan
Maryum Ibrar, Pakistan Scientific and Technological Information Centre, Quaid-i-Azam University, Islamabad 45320, Pakistan
Tofeeq Ur-Rehman, Department of Pharmacy, Quaid-i-Azam University, Islamabad 45320, Pakistan
Alfizah Hanafiah, Faculty of Medicine, Department of Medical Microbiology and Immunology, Universiti Kebangsan Malaysia, Cheras, Kuala Lumpur 56000, Malaysia
Author contributions: Rasheed F and Aziz S contributed to conceptualization; Aziz S contributed to methodology; Umer M contributed to software; Aziz S contributed to validation; Aziz S, König S and Ibrar M contributed to formal analysis; Rasheed F contributed to resources; Akhter ST and Iqbal S contributed to endoscopic procedures; Aziz S and König S contributed to writing – original draft preparation; Aziz S, König S, Ahmad T, Rasheed F, Hanafia A, and Rehman UT contributed to writing – review & editing; König S contributed to visualization; Zahra R and Rasheed F contributed to supervision; Aziz S and Rasheed F contributed to project administration; Aziz S and Rasheed F contributed to funding acquisition.
Institutional review board statement: Ethical approvals were granted from the Ethical Technical Committee, Pakistan Institute of Nuclear Science and Technology (PINSTECH), Islamabad (Ref.-No. PINST/DC-26/2017), the Bioethics Committee, Quaid-i-Azam University, Islamabad, Pakistan (Ref.-No. BBC-FBS-QAU2019-159), and the Institutional Research Forum, Holy Family Hospital, Rawalpindi Medical University, Rawalpindi (Ref.-No. R-40/RMU).
Informed consent statement: The investigators explain the study to each patient and informed written consent was obtained to participate in this research and their clinical data was collected during interview using a questionnaire after endoscopic evaluation. Moreover, patients were also required to give informed consent to the study for analysis and publication of their anonymous clinical data.
Conflict-of-interest statement: All the authors declare no conflict of interest.
Data sharing statement: All the data has been shared in supplementary files.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
https://creativecommons.org/Licenses/by-nc/4.0/ Corresponding author: Shahid Aziz, PhD, Research Fellow, Patients Diagnostic Lab, Isotope Application Division, Pakistan Institute of Nuclear Science and Technology, Nilore, Islamabad 44000, Pakistan. saziz@bs.qau.edu.pk
Received: December 14, 2022
Peer-review started: December 14, 2022
First decision: January 22, 2023
Revised: April 1, 2023
Accepted: April 20, 2023
Article in press: April 20, 2023
Published online: June 8, 2023
ARTICLE HIGHLIGHTS
Research background
Gastric cancer is the 4th main reason for cancer-associated deaths around the globe. Diagnosis mainly depends on histopathological examinations and the number of endoscopic procedures is increasing. Helicobacter pylori (H. pylori) infection is a main risk factor for this cancer.
Research motivation
The increasing prevalence of gastric cancer due to late diagnosis or at an advanced stage was the main cause to conduct this research study to diagnose gastric cancer at an early stage.
Research objectives
The main research objectives of this study were: (1) Diagnosis of H. pylori infection; and (2) Development of gastric cancer prediction model using non-invasive characteristics of enrolled subjects.
Research methods
The 341 dyspeptic patients were enrolled after endoscopic evaluation and metadata was collected using a Likert scale questionnaire. The infection status was determined with the help of three modalities including 13C urea breath test, rapid urease test, and histopathological examinations. A Random Forest (RF) -gastric cancer (GC) prediction model was developed using non-invasive characteristics of patients.
Research results
This study reported a higher frequency of H. pylori infections among enrolled subjects. It was greater in gastric cancer as compared to other groups and also higher in males in comparison with females. Abdominal pain was observed more than other clinical symptoms. The majority of gastric cancer patients experienced symptoms of vomiting with abdominal pain. The multinomial logistic regression model correctly classified 80% of gastric cancer cases. The RF GC predictive model achieved > 80% test accuracy.
Research conclusions
The gastric cancer risk factors were incorporated into a computer model to predict the likelihood of developing gastric cancer with high sensitivity and specificity. The model is dynamic and will be further improved and validated by including new data in future research studies. Its use may reduce unnecessary endoscopic procedures.
Research perspectives
The computer model will predict the likelihood of developing gastric cancer with high sensitivity and specificity. Moreover, it will be helpful in diagnosing other gastric diseases such as gastritis and ulcer and assist gastroenterologists to start palliative therapy to reduce unnecessary endoscopic procedures.
