Prognostic significance of tumor immune microenvironment and immunotherapy: Novel insights and future perspectives in gastric cancer

Malignant hepatic vascular tumors in adults: Characteristics, diagnostic difficulties and current management

In the gastroenterology field, the impact of artificial intelligence was investigated for the purposes of diagnostics, risk stratification of patients, improvement in quality of endoscopic procedures and early detection of neoplastic diseases, implementation of the best treatment strategy, and optimization of patient prognosis. Computer-assisted diagnostic systems to evaluate upper endoscopy images have recently emerged as a supporting tool in endoscopy due to the risks of misdiagnosis related to standard endoscopy and different expertise levels of endoscopists, time-consuming procedures, lack of availability of advanced procedures, increasing workloads, and development of endoscopic mass screening programs. Recent research has tended toward computerized, automatic, and real-time detection of lesions, which are approaches that offer utility in daily practice. Despite promising results, certain studies might overexaggerate the diagnostic accuracy of artificial systems, and several limitations remain to be overcome in the future. Therefore, additional multicenter randomized trials and the development of existent database platforms are needed to certify clinical implementation. This paper presents an overview of the literature and the current knowledge of the usefulness of different types of machine learning systems in the assessment of premalignant and malignant esophageal lesions via conventional and advanced endoscopic procedures. This study makes a presentation of the artificial intelligence terminology and refers also to the most prominent recent research on computer-assisted diagnosis of neoplasia on Barrett’s esophagus and early esophageal squamous cell carcinoma, and prediction of invasion depth in esophageal neoplasms. Furthermore, this review highlights the main directions of future doctor–computer collaborations in which machines are expected to improve the quality of medical action and routine clinical workflow, thus reducing the burden on physicians.

Background: A considerable number of recent research have used artificial intelligence (AI) in the area of colorectal cancer (CRC). Surgical treatment of CRC still remains the most important curative component. Artificial intelligence in CRC surgery is not nearly as advanced as it is in screening (colonoscopy), diagnosis and prognosis, especially due to the increased complexity and variability of structures and elements in all fields of view, as well as a general shortage of annotated video banks for utilization.Methods: A literature search was made and relevant studies were included in the minireview.Results: The intraoperative steps which, at this moment, can benefit from AI in CRC are: phase and action recognition, excision plane navigation, endoscopy control, real-time circulation analysis, knot tying, automatic optical biopsy and hyperspectral imaging. This minireview also analyses the current advances in robotic treatment of CRC as well as the present possibility of automated CRC robotic surgery.Conclusions: The use of AI in CRC surgery is still at its beginnings. The development of AI models capable of reproducing a colorectal expert surgeon’s skill, the creation of large and complex datasets and the standardization of surgical colorectal procedures will contribute to the widespread use of AI in CRC surgical treatment.

Gastric cancer (GC) remains a major global health burden, with high morbidity and mortality rates, particularly in regions with prevalent Helicobacter pylori (H. pylori) infection. While H. pylori has long been recognized as a primary carcinogenic agent, recent research has underscored the broader contribution of the gastric microbiota to gastric carcinogenesis. Alterations in the microbial community, or dysbiosis, contribute to chronic inflammation, immune modulation, and epithelial transformation through a range of mechanisms, including disruption of mucosal integrity, activation of oncogenic signaling pathways (e.g., PI3K/Akt, NF-κB, STAT3), and epigenetic alterations. Furthermore, microbial metabolites, such as short-chain fatty acids, secondary bile acids, and lactate, play dual roles in either promoting or suppressing tumorigenesis. Oral and gut-derived microbes, translocated to the gastric niche, have been implicated in reshaping the gastric microenvironment and exacerbating disease progression. The composition of the microbiota also influences responses to cancer immunotherapy, suggesting that microbial profiles can serve as both prognostic biomarkers and therapeutic targets. Emerging strategies, such as probiotics, dietary interventions, and fecal microbiota transplantation (FMT), offer new avenues for restoring microbial balance and enhancing therapy response. This review synthesizes current knowledge on the complex interplay between microbiota and gastric cancer development and emphasizes the potential of microbiome modulation in both preventive and therapeutic frameworks.

Background: Colorectal cancer is considered to be triggered by the malignant transformation of colorectal polyps. Early diagnosis and excision of colorectal polyps has been found to lower the mortality and morbidity associated with colorectal cancer.Objective: The aim of this study is to offer a predictive model for the presence of colorectal polyps based on Random Forests machine learning algorithm, using basic patient information and common laboratory test results.Materials and methods: 164 patients were included in the study. The following data was collected: sex, residence, age, diabetes mellitus, body mass index, fasting blood glucose levels, hemoglobin, platelets, total, LDL and HLD cholesterol, triglycerides, serum glutamic-oxaloacetic transaminase, chronic gastritis, presence of colonic polyps at colonoscopy. 80% of patients were included in the training set for creating a Random forests algorithm, 20% were in the test set. External validation was performed on data from 42 patients. The performance of the Random Forests was compared with the performance of a generalized linear model (GLM) and support vector machine (SVM) built and tested on the same datasets.Results: The Random Forest prediction model gave an AUC of 0.820 on the test set. The top five variables in order of importance were: body mass index, platelets, hemoglobin, triglycerides, glutamic-oxaloacetic transaminase. For external validation, the AUC was 0.79. GLM performance in internal validation was an AUC of 0.788, while for external validation AUC-0.65. For SVN, the AUC - 0.785 for internal validation and 0.685 for the external validation dataset.Conclusions: A random forest prediction model was developed using patient's demographic data, medical history and common blood tests results. This algorithm can foresee, with good predictive power, the presence of colonic polyps.