Structural and functional radiomics for lung cancer.

Area of Science:

• Oncology
• Medical Imaging
• Data Science

Background:

• Lung cancer is a leading cause of cancer deaths globally.
• Precision medicine and radiological imaging are crucial for lung cancer management.
• Radiomics, utilizing quantitative image features, offers advanced insights beyond semantic analysis.

Purpose of the Study:

• To review the current applications of radiomics in lung cancer.
• To highlight radiomics' role in detection, diagnosis, and prognosis.
• To discuss challenges and future directions for radiomics in clinical practice.

Main Methods:

• Review of structural and functional radiomics applications.
• Analysis of radiomic features for pathology, gene mutation, and treatment prediction.
• Exploration of radiomics in treatment response and prognosis assessment.

Main Results:

• Radiomics enables enhanced detection and diagnosis of lung cancer.
• It aids in predicting treatment strategies and patient prognosis.
• Radiomics facilitates the evaluation of treatment response.

Conclusions:

• Radiomics shows significant potential in advancing lung cancer care.
• Key limitations include data scarcity, lack of standardization, and reproducibility issues.
• Future work should focus on multi-modal data integration and improved model training for clinical implementation.