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.

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.

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.

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.

Background and Objectives: Colorectal cancer (CRC) incidence is rapidly emerging among individuals <50 years, termed as early-onset colorectal cancer (EOCRC). This study aimed to probe variations in tumorigenic pathology and relevant manifestations (polyp and adenoma incidence) between suspected cases of EOCRC and late-onset CRC (LOCRC; ≥50 years of age). Materials and Methods: Between September 2022 and February 2023, colonoscopy-based screening data from 1653 patients were included in this study. All eligible participants were divided into two groups, depending upon patient age, where Group 1 consisted of 1021 patients aged <50 years while Group 2 consisted of 632 patients aged ≥ 50 years. Polyp samples were collected when identified peri-procedurally and characterized according to World Health Organization criteria. Results: Polyp detection rate was 42% for the <50-year age group, while this was 76% for the ≥50-year age group. Additionally, the <50-year age group predominated in hyperplastic polyp manifestation, particularly within the rectum and sigmoid colon. In addition, the ≥50-year age group had increased prevalence of serrated polyps and differing adenoma manifestations. Conclusions: This investigation served to highlight the importance of age stratification for CRC colonoscopy-based screening effectiveness, with particular reference to evaluations that are based on polyp localization within differing colon regions.

Background: Colorectal adenomas are key precancerous lesions and a major target for colorectal cancer prevention. While gut microbiome alterations are well described in colorectal cancer, microbial composition and functional capacity at the adenoma stage remain poorly understood. Emerging metagenomic data suggest early adenomas are associated with loss of microbial metabolic functions supporting epithelial and immune homeostasis. Objectives: To investigate the association between gut microbiome composition and functional pathways and the presence of colorectal adenomas in patients undergoing routine colonoscopy. Materials and methods: This cross-sectional case–control study included adult patients undergoing routine colonoscopy. Participants were enrolled based on strict inclusion and exclusion criteria to minimize confounding factors such as inflammatory bowel disease, prior colorectal surgery, and recent antibiotic or probiotic use. Fecal samples were collected prior to bowel preparation, and gut microbiome taxonomic composition and functional pathways were analyzed using shotgun metagenomic sequencing. Results: A total of 136 participants were included, of whom 56 had colorectal adenomas. Alpha diversity indices did not differ significantly between adenoma-positive and adenoma-negative groups. In contrast, beta diversity analysis revealed significant differences in overall microbial community structure. Descriptive genus-level differences suggested features of dysbiosis in adenoma-positive patients, including higher relative abundance of Bacteroides and Prevotella and lower abundance of Faecalibacterium and Anaerostipes . Differential abundance analysis identified a single species-level feature, UBA7597 sp003448195, enriched in the adenoma group. Functional profiling showed reduced microbial pathways related to menaquinone (vitamin K₂) biosynthesis, Stickland fermentation, and short-chain fatty acid (propionate) production in patients with adenomas. Conclusions: The presence of colorectal adenomas was associated with reduced microbial metabolic functions linked to vitamin K₂ biosynthesis, amino acid fermentation, and propionate production, alongside compositional shifts toward a less functionally robust gut microbiome. These findings indicate that early colorectal neoplasia is accompanied by functional microbiome alterations that may serve as markers of adenoma-associated dysbiosis and provide insight into early metabolic changes in the colonic microenvironment.