Dynamic prognostication using conditional survival analysis for patients with operable lung adenocarcinoma

Area of Science:

• Oncology
• Radiology
• Biostatistics

Background:

• Lung adenocarcinoma is a leading cause of cancer mortality.
• Accurate prognostic assessment is crucial for guiding treatment and surveillance strategies.
• Conditional survival (CS) offers a dynamic approach to re-evaluating prognosis over time post-treatment.

Purpose of the Study:

• To evaluate conditional survival in patients with surgically resected lung adenocarcinoma.
• To identify how prognostic factors change in their predictive power over time.
• To inform personalized surveillance plans for lung adenocarcinoma survivors.

Main Methods:

• Conditional survival analysis was performed at multiple time points (0-5 years post-surgery).
• 723 patients with resected lung adenocarcinoma were analyzed.
• Prognostic factors included clinico-demographic, pathologic, and imaging (SUVmax, TDR) characteristics.
• Uni- and multivariable Cox regression analyses were used.

Main Results:

• Three-year conditional overall survival (OS) and disease-free survival (DFS) improved significantly from baseline to 5 years post-surgery.
• Pathologic and radiologic factors (stage, subtype, grade, SUVmax, TDR) maintained prognostic significance over time.
• High SUVmax and low TDR were independent predictors of subsequent 3-year OS and DFS up to 2 years post-surgery.

Conclusions:

• Conditional survival analysis provides valuable insights into the evolving prognosis of lung adenocarcinoma survivors.
• Radiologic features (SUVmax, TDR) are important predictors of long-term outcomes.
• Findings support tailored surveillance strategies, particularly for patients with specific preoperative imaging characteristics.