# Advances in modelling the risk of benign and malignant lung nodules

## TL;DR

This paper reviews progress in predicting whether lung nodules are benign or malignant, emphasizing the potential of combining clinical, imaging, and AI tools for better accuracy.

## Contribution

The paper highlights multimodal integration as a novel approach to improve lung nodule risk assessment beyond traditional methods.

## Key findings

- Multimodal models combining clinical, imaging, and biomarker data achieve AUC >0.90 in lung nodule risk assessment.
- AI-driven tools enhance diagnostic precision by capturing features not visible to the human eye.
- Generalizability and standardization remain key challenges for current predictive models.

## Abstract

Lung nodules are critical indicators for early lung cancer detection, yet accurately distinguishing between benign and malignant lesions remains a clinical challenge. This review summarizes advances in predictive models for lung nodule risk assessment, spanning classical clinical-imaging models, biomarker-based approaches, and artificial intelligence (AI)-driven tools. While classical models provide a foundational framework, their performance often varies across populations. Biomarkers and AI models significantly enhance diagnostic precision by capturing molecular and imaging features imperceptible to the human eye. However, issues such as generalizability, standardization, and data security persist. The most promising direction lies in multimodal integration, combining clinical, imaging, biomarker, and AI data to achieve superior accuracy with an area under the curve (AUC) >0.90. Future efforts should focus on multi-center validation, standardized biomarker assays, and data secure, scalable AI systems to translate these innovations into routine clinical practice, enabling personalized and early lung cancer diagnosis.