Radiomics for lung cancer diagnosis, management, and future prospects.

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Lung cancer is a leading cause of global cancer mortality, presenting diagnostic and treatment challenges.
• Radiomics offers a promising avenue for improving lung cancer management through quantitative image analysis.

Purpose of the Study:

• To review the role of radiomics in lung cancer diagnosis and management.
• To highlight key applications and future directions of radiomics in oncology.

Main Methods:

• Review of radiomics techniques, including handcrafted features and deep learning.
• Analysis of artificial intelligence (AI) model performance in lung cancer detection, histology prediction, and staging.

Main Results:

• AI-driven radiomics demonstrates superior specificity and sensitivity in various lung cancer applications.
• Radiomics aids in enhancing diagnostic accuracy, predicting treatment response, and personalizing patient care.

Conclusions:

• Radiomics, particularly with AI integration, is poised to redefine clinical workflows for lung cancer.
• Future developments like large language models and explainable AI (XAI) promise further advancements in precision oncology.