Breath Fingerprint of Colorectal Cancer Patients Based on the Gas Chromatography-Mass Spectrometry Analysis.

The human body emits a multitude of volatile organic compounds (VOCs) via tissues and various bodily fluids or exhaled breath. These compounds collectively create a distinctive chemical profile, which can potentially be employed to identify changes in human metabolism associated with colorectal cancer (CRC) and, consequently, facilitate the diagnosis of this disease. The main goal of this study was to investigate and characterize the VOCs' chemical patterns associated with the breath of CRC patients and controls and identify potential expiratory markers of this disease. For this purpose, gas chromatography-mass spectrometry was applied. Collectively, 1656 distinct compounds were identified in the breath samples provided by 152 subjects. Twenty-two statistically significant VOCs (p-xylene; hexanal; 2-methyl-1,3-dioxolane; 2,2,4-trimethyl-1,3-pentanediol diisobutyrate; hexadecane; nonane; ethylbenzene; cyclohexanone; diethyl phthalate; 6-methyl-5-hepten-2-one; tetrahydro-2H-pyran-2-one; 2-butanone; benzaldehyde; dodecanal; benzothiazole; tetradecane; 1-dodecanol; 1-benzene; 3-methylcyclopentyl acetate; 1-nonene; toluene) were observed at higher concentrations in the exhaled breath of the CRC group. The elevated levels of these VOCs in CRC patients' breath suggest the potential for these compounds to serve as biomarkers for CRC.

The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis.

Colorectal cancer (CRC) is the third most common malignancy and the second most common cause of cancer-related deaths worldwide. While CRC screening is already part of organized programs in many countries, there remains a need for improved screening tools. In recent years, a potential approach for cancer diagnosis has emerged via the analysis of volatile organic compounds (VOCs) using sensor technologies. The main goal of this study was to demonstrate and evaluate the diagnostic potential of a table-top breath analyzer for detecting CRC. Breath sampling was conducted and CRC vs. non-cancer groups (105 patients with CRC, 186 non-cancer subjects) were included in analysis. The obtained data were analyzed using supervised machine learning methods (i.e., Random Forest, C4.5, Artificial Neural Network, and Naïve Bayes). Superior accuracy was achieved using Random Forest and Evolutionary Search for Features (79.3%, sensitivity 53.3%, specificity 93.0%, AUC ROC 0.734), and Artificial Neural Networks and Greedy Search for Features (78.2%, sensitivity 43.3%, specificity 96.5%, AUC ROC 0.735). Our results confirm the potential of the developed breath analyzer as a promising tool for identifying and categorizing CRC within a point-of-care clinical context. The combination of MOX sensors provided promising results in distinguishing healthy vs. diseased breath samples. Its capacity for rapid, non-invasive, and targeted CRC detection suggests encouraging prospects for future clinical screening applications.

Role of pre-existing incomplete intestinal metaplasia in gastric adenocarcinoma: A retrospective case series analysis.

BACKGROUND: Risk stratification for patients with gastric precancerous lesions for endoscopic surveillance remains controversial. AIM: To analysis of patients having developed gastric adenocarcinoma during the period of follow-up. METHODS: We conducted a retrospective study on patients having undergone upper endoscopy prior to the development of gastric adenocarcinoma. The presence and stage of precancerous lesions as well as subtype of intestinal metaplasia at the baseline endoscopy got evaluated. Literature mini-review was performed. RESULTS: Out of 1681 subjects in the Biobank, gastric adenocarcinoma was detected in five cases in whom previous endoscopy data with biopsies either from the corpus or antral part were available. All of the patients had incomplete intestinal metaplasia during the baseline endoscopy; all three subjects in whom intestinal metaplasia subtyping was performed according to Filipe et al, had Type III intestinal metaplasia. Two of the five cases had low Operative Link on Gastritis Assessment (OLGA) and Operative Link on Gastritis Intestinal Metaplasia Assessment (OLGIM) stages (I-II) at the baseline. CONCLUSION: The presence of incomplete intestinal metaplasia, in particular, that of Type III is a better predictor for gastric adenocarcinoma development than OLGA/OLGIM staging system. Subtyping of intestinal metaplasia have an important role in the risk stratification for surveillance decisions.

The Role of an Artificial Intelligence Method of Improving the Diagnosis of Neoplasms by Colonoscopy.

BACKGROUND: Colorectal cancer (CRC) is the third most common cancer worldwide. Colonoscopy is the gold standard examination that reduces the morbidity and mortality of CRC. Artificial intelligence (AI) could be useful in reducing the errors of the specialist and in drawing attention to the suspicious area. METHODS: A prospective single-center randomized controlled study was conducted in an outpatient endoscopy unit with the aim of evaluating the usefulness of AI-assisted colonoscopy in PDR and ADR during the day time. It is important to understand how already available CADe systems improve the detection of polyps and adenomas in order to make a decision about their routine use in practice. In the period from October 2021 to February 2022, 400 examinations (patients) were included in the study. One hundred and ninety-four patients were examined using the ENDO-AID CADe artificial intelligence device (study group), and 206 patients were examined without the artificial intelligence (control group). RESULTS: None of the analyzed indicators (PDR and ADR during morning and afternoon colonoscopies) showed differences between the study and control groups. There was an increase in PDR during afternoon colonoscopies, as well as ADR during morning and afternoon colonoscopies. CONCLUSIONS: Based on our results, the use of AI systems in colonoscopies is recommended, especially in circumstances of an increase of examinations. Additional studies with larger groups of patients at night are needed to confirm the already available data.

Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection.

BACKGROUND: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). METHODS: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. RESULTS: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. CONCLUSIONS: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.

Factors influencing participation in preventive interventions for gastric cancer: the results from the GISTAR study.

OBJECTIVES: Search-and-treat strategy for Helicobacter pylori and surveillance of patients with precancerous lesions are recommended to decrease the burden of gastric cancer in high-risk areas. We aimed to evaluate the acceptance of the target population to these strategies. METHODS: We applied a search-and-treat strategy combined with biomarker screening (pepsinogens I and II, gastrin-17) for atrophic gastritis to healthy individuals aged 40-64 years within the GISTAR Pilot study. Different means of invitation were evaluated - direct telephone calls, letters of invitation via the general practitioners. Participants with altered biomarker results were invited to undergo upper gastrointestinal endoscopy. H.pylori positive individuals were offered eradication therapy. Data on the compliance to the treatment and reasons for noncompliance were collected via telephone. RESULTS: Altogether 3453 participants were enrolled. The attendance of women participants was 1.9 times higher although active invitation strategies were mainly targeting men. The yield for the telephone invitations was higher than for mail-delivered invitations (2.1 calls vs. 7.7 letters required to recruit one study subject). Out of 661 individuals reached with the invitation to undergo upper endoscopy, 520 (78.7%) attended the procedure. Out of 1185 study subjects eligible for eradication, 810 (68.4%) accepted it. Of those having received the medication, 765(94.4%) completed it. The reasons for nonparticipation were the overall misconception of the importance of screening, busy schedule and others. CONCLUSIONS: While only the minority of the target population participated in the gastric cancer prevention strategy, relatively high compliance was seen among the participants. The acceptance rate and the identified reasons for refusing to participate in our study indicate that there is a need to raise gastric cancer awareness and its existent preventive strategies within the general population for their successful implementation in the community.