Objective:

To develop a combined model based on GPT-Radscore and clinical predictive factors for non-invasive prediction of lymphovascular invasion (LVI) status in invasive lung adenocarcinoma (LUAD), emphasizing the clinical significance of LVI in treatment decisions.

Key Findings:

• LVI is a significant prognostic factor in lung cancer, influencing treatment decisions and patient outcomes.
• Current LVI detection methods are invasive and subjective, highlighting the need for non-invasive alternatives that can be integrated into clinical practice.
• CT radiomics can potentially predict LVI by analyzing intratumoral and peritumoral textures, offering a novel approach to risk assessment.

Interpretation:

The study suggests that a peritumor-based radiomics model could enhance the prediction of LVI in LUAD, providing a non-invasive assessment method that may improve patient management and treatment strategies.

Limitations:

• The study is retrospective and may have selection bias, particularly in patient selection.
• The reliance on specific imaging techniques may limit generalizability to broader populations.
• Pathological confirmation of LVI remains the gold standard, which may not always align with radiomic predictions, potentially affecting clinical applicability.

Conclusion:

Developing a non-invasive predictive model for LVI in LUAD could significantly aid in risk stratification and treatment planning, although further validation is necessary to confirm its clinical utility.