Evaluation of the parameters such as tumor microenvironment (TME) and tumor budding (TB) is one of the most important steps in colorectal cancer (CRC) diagnosis and cancer development prognosis. In recent years, artificial intelligence (AI) has been successfully used to solve such problems. In this paper, we summarize the latest data on the use of artificial intelligence to predict tumor microenvironment and tumor budding in histological scans of patients with colorectal cancer. We performed a systematic literature search using 2 databases (Medline and Scopus) with the following search terms: ("tumor microenvironment" OR "tumor budding") AND ("colorectal cancer" OR CRC) AND ("artificial intelligence" OR "machine learning " OR "deep learning"). During the analysis, we gathered from the articles performance scores such as sensitivity, specificity, and accuracy of identifying TME and TB using artificial intelligence. The systematic review showed that machine learning and deep learning successfully cope with the prediction of these parameters. The highest accuracy values in TB and TME prediction were 97.7% and 97.3%, respectively. This review led us to the conclusion that AI platforms can already be used as diagnostic aids, which will greatly facilitate the work of pathologists in detection and estimation of TB and TME as instruments and second-opinion services. A key limitation in writing this systematic review was the heterogeneous use of performance metrics for machine learning models by different authors, as well as relatively small datasets used in some studies.

PDT using PSs based on polycationic derivatives of synthetic bacteriochlorin against Lewis lung carcinoma provides effective inhibition of tumor growth with an increase in the lifespan and survival of mice in the group. PDT with polycationic photosensitizers destroys CSCs and tumor neovascularization, and activates the desmoplastic reaction. These results open up new opportunities for increasing the effectiveness of treatment and reducing the incidence of relapses and metastases after PDT.

Lung cancer occupies a leading position in the structure of global cancer morbidity and mortality, due to the biological properties of the key pool of tumor cells — cancer stem cells (CSCs). The effects of SARS-CoV2 on tumor CSCs and its niche have not been studied. Objective. To study CSCs in lung adenocarcinomas after COVID19. Material and methods. Surgical material from lung adenocarcinoma from 12 patients who had a new coronavirus infection and from 12 patients who did not have COVID19 was examined. Analysis of clinical and anamnestic data, macroscopic and microscopic examination of tumor samples and adjacent intact tissue, immunohistochemical reactions using antibodies to virus proteins and CSC markers ALDH1, CD133 and CD34 were performed. Results. Adenocarcinoma samples from patients in the main group showed a significant increase in the number of cells expressing the CSCs markers ALDH1, CD133 and CD34 compared to adenocarcinoma samples from control group patients without SARS-CoV2 infection. We found an increase in the number of CSCs in patients with adenocarcinoma metastasis in lymph nodes in both the main and control groups. CSCs of lung adenocarcinomas from SARS-CoV2 survivors contain virus proteins Nucleocapside and Spike protein. Conclusions. We found an increase in the number of CSCs with expression of ALDH1, CD133 and CD34 in lung adenocarcinoma in patients with new coronavirus infection. Increased number of ALDH1+, CD133+ CD34+ CSCs in tumor tissue enhance the metastatic potential of lung adenocarcinoma. The Nucleocapsid and Spike proteins of SARS-CoV2 virus are detectable in lung tissue from patients with new coronavirus infection, both in adenocarcinoma cells, CSCs, and in type II pneumocytes, macrophages, and endothelial cells, suggesting prolonged persistence of the virus proteins and probably the virus.

Lymphovascular invasion (LVI) in lung cancer is a significant prognostic factor that influences treatment and outcomes, yet its reliable detection is challenging due to interobserver variability. This study aims to develop a deep learning model for LVI detection using whole slide images (WSIs) and evaluate its effectiveness within a pathologist's information system. Experienced pathologists annotated blood vessels and invading tumor cells in 162 WSIs of non-mucinous lung adenocarcinoma sourced from two external and one internal datasets. Two models were trained to segment vessels and identify images with LVI features. DeepLabV3+ model achieved an Intersection-over-Union of 0.8840 and an area under the receiver operating characteristic curve (AUC-ROC) of 0.9869 in vessel segmentation. For LVI classification, the ensemble model achieved a F1-score of 0.9683 and an AUC-ROC of 0.9987. The model demonstrated robustness and was unaffected by variations in staining and image quality. The pilot study showed that pathologists' evaluation time for LVI detecting decreased by an average of 16.95%, and by 21.5% in "hard cases". The model facilitated consistent diagnostic assessments, suggesting potential for broader applications in detecting pathological changes in blood vessels and other lung pathologies.