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

## TL;DR

This paper uses computer vision to analyze lung cancer tissue patterns, helping identify potential therapy targets based on vascular and proliferative features.

## Contribution

The study introduces two AI frameworks for analyzing vascular and mitotic features in lung cancer, revealing distinct patterns linked to tumor aggressiveness.

## Key findings

- SegFormer achieved high accuracy in vessel segmentation with IoU = 0.96 and AUC-ROC = 0.98.
- RetinaNet + CNN ensemble showed high specificity (0.96) and sensitivity (0.97) for mitotic detection.
- Distinct trophic patterns were identified, which could guide targeted therapies like antiangiogenic treatment.

## Abstract

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.