The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine the prognosis and aggressiveness of the tumor. Traditional manual evaluation methods are time consuming and subject to inter-observer variability. Blood vessel detection is a perfect task for artificial intelligence, which is capable of rapid analyzing thousands of tissue structures in whole slide images. The development of computer vision solutions requires the segmentation of tissue regions, the extraction of features and the training of machine learning models. In this review, we focus on the methodologies employed by researchers to identify blood vessels and vascular invasion across a range of tumor localizations, including breast, lung, colon, brain, renal, pancreatic, gastric and oral cavity cancers. Contemporary models herald a new era of computational pathology in morphological diagnostics.

Proton beam therapy (PBT) is a rapidly advancing modality of hadron therapy. The primary advantage of proton therapy lies in a unique depth-dose distribution characterized by the Bragg peak, which enables a highly targeted irradiation of the area limited to the tumor, while minimizing the impact on healthy tissues. However, a broader clinical adoption of the ion beam therapy is limited by both economic and radiobiological constraints. One of the possible ways to increase the relative biological effectiveness (RBE) of proton therapy involves the use of radiosensitizers. Background/Objectives: In this work, we investigated the efficacy of using colloidal solutions of lanthanum hexaboride (LaB₆) nanoparticles (NPs) coated with polyacrylic acid (PAA) as sensitizers to increase the antitumor biological effectiveness of proton irradiation. This material has not yet been studied extensively so far, despite its promising physical and chemical properties and several reports on its biocompatibility. Methods: LaB₆ NPs were synthesized by femtosecond pulsed laser ablation, functionalized with PAA and characterized. The safety of NPs was evaluated in vitro using a Live/Dead assay on cell cultures: EMT6/P, BT-474, and in vivo in Balb/c mice after intravenous (i.v.) administration. The efficacy of binary proton therapy was evaluated in vitro on cell cultures: EMT6/P, BT-474, and in vivo in the model of human ductal carcinoma of the mammary gland BT-474 in female Nu/j mice after intratumoral (i.t.) administration at a dose of 2.0 mg/mouse and local proton irradiation (fractional exposure of 31 Gy + 15 Gy). The biodistribution of LaB₆-PAA NPs in the animal body was also evaluated. Results: Significant enhancement in cancer cell death following proton beam irradiation was demonstrated in vitro on EMT6/P, BT-474 cell lines. Although the antitumor efficacy observed in vivo was comparatively lower—likely due to the high sensitivity of the BT-474 xenografts—both proton monotherapy and binary treatment were well tolerated. Conclusions: LaB₆-PAA NPs show promise as efficient sensitizers capable of enhancing the biological efficacy of proton therapy, offering a potential path forward for improving therapeutic outcomes.

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.

Fractional laser photothermolysis, long established in dermatology, enables controlled microthermal injury that stimulates repair without scarring, but its potential in oral tissue regeneration has not been systematically explored. In this study, we conducted the first controlled experimental evaluation of a 1550 nm erbium fiber laser for oral mucosa regeneration. Thirty-two rabbits underwent fractional photothermolysis at energy levels of 70, 100 and 130 kJ, with gingival biopsies collected at 1, 14, 28 and 42 days for histological and immunohistochemical assessment of epithelial repair, stromal remodeling, inflammation and angiogenesis. All energy modes produced microcoagulation columns followed by progressive epithelial thickening, fibroblast proliferation and neoangiogenesis. The 70 kJ mode occasionally led to residual fibrosis, whereas higher energies (100–130 kJ) promoted effective connective tissue remodeling and de novo tissue formation without scarring. Complete epithelial recovery occurred within two weeks, indicating a safe and optimal interval for repeated exposure. Overall, the results demonstrate that 1550 nm fractional photothermolysis is a safe and effective method to induce regenerative responses in oral tissues, establishing a foundation for its translational application in periodontal and peri-implant regeneration.

Background/Objectives: Lung cancer is characterized by a significant microstructural heterogenicity among different histological types. Artificial intelligence and digital pathology instruments can facilitate morphological analysis by introducing calculated metrics allowing for the distinguishment of different tissue patterns. Methods: We used computer vision models to calculate a number of morphometric features of tumor vascularization and proliferation. We used two frameworks to process whole-slide images: (1) LVI-PathNet framework for vascular detection, based on the SegFormer architecture; and (2) Mito-PathNet framework for mitotic figure detection, based on the RetinaNet detector and an ensemble classification model. The results were visualized in the segmented and gradient heatmaps. Results: SegFormer for vessel segmentation achieved the following quality metrics: IoU = 0.96, FBeta-score = 0.98, and AUC-ROC = 0.98. RetinaNet + CNN ensemble achieved the following quality metrics: specificity = 0.96 and sensitivity = 0.97. The analysis of the obtained parameters allowed us to identify trophic patterns of lung cancer according to the degree of aggressiveness, which can serve as potential targets for therapy, including proliferative-vascular, hypoxic, proliferative, vascular, and inactive. Conclusions: The analysis of the obtained parameters allowed us to identify distinct quantitative characteristics for each histological type of lung cancer. These patterns could potentially become markers for therapeutic choices, such as antiangiogenic and hypoxia-induced factor therapy.