Computer Vision-Assisted Spatial Analysis of Mitoses and Vasculature in Lung Cancer

Area of Science:

• Digital pathology
• Artificial intelligence
• Computational pathology

Background:

• Lung cancer exhibits significant microstructural heterogeneity across histological types.
• Digital pathology and AI offer tools for morphological analysis and distinguishing tissue patterns.

Purpose of the Study:

• To utilize AI-driven computer vision models for quantitative analysis of tumor vascularization and proliferation in lung cancer.
• To identify distinct trophic patterns indicative of lung cancer aggressiveness.

Main Methods:

• Employed LVI-PathNet (SegFormer) for vascular detection and Mito-PathNet (RetinaNet + CNN ensemble) for mitotic figure detection on whole-slide images.
• Calculated morphometric features of tumor vascularization and proliferation.
• Visualized results using segmented and gradient heatmaps.

Main Results:

• Achieved high performance metrics for vessel segmentation (IoU=0.96, FBeta=0.98, AUC-ROC=0.98) and mitotic figure detection (Specificity=0.96, Sensitivity=0.97).
• Identified five distinct trophic patterns: proliferative-vascular, hypoxic, proliferative, vascular, and inactive, correlating with aggressiveness.

Conclusions:

• Quantitative characteristics for each lung cancer histological type were identified.
• These patterns may serve as potential biomarkers for guiding therapeutic strategies, including antiangiogenic and hypoxia-targeted therapies.