Risk stratification of indeterminate pulmonary nodules

Area of Science:

• Pulmonology
• Oncology
• Radiology

Background:

• Lung cancer is a leading cause of cancer death with low survival rates.
• Early detection through screening improves survival but increases indeterminate pulmonary nodules (IPNs).
• Risk stratification of intermediate-risk IPNs presents a clinical challenge.

Purpose of the Study:

• To review established risk stratification methods for indeterminate pulmonary nodules.
• To explore current research and future directions in IPN risk assessment.
• To address the need for improved diagnostic strategies for lung nodules.

Main Methods:

• Review of current risk stratification techniques including individual risk factors, imaging characteristics, biomarkers, and prediction models.
• Exploration of novel biomarkers and advanced imaging analysis.
• Discussion of radiomics and deep-learning methods for IPN analysis.

Main Results:

• Existing risk stratification strategies for IPNs are suboptimal.
• New molecular and imaging biomarkers show promise for differentiating benign from malignant nodules.
• Radiomics and deep learning offer potential for optimizing IPN risk stratification.

Conclusions:

• Improved risk stratification of indeterminate pulmonary nodules is crucial for effective lung cancer management.
• Novel biomarkers and advanced imaging techniques require further validation.
• Integration into clinical workflows and prospective trials are needed for routine adoption.