Procedure-Specific Risk Prediction for Recurrence in Patients Undergoing Lobectomy or Sublobar Resection for Small...

Area of Science:

• Oncology
• Surgical Oncology
• Biostatistics

Background:

• Early-stage lung adenocarcinoma (ADC) recurrence after resection poses a significant clinical challenge.
• Accurate prognostication and identification of patients who may benefit from adjuvant chemotherapy (ACT) are crucial for improving outcomes.

Purpose of the Study:

• To develop and validate procedure-specific risk prediction models for recurrence following resection of early-stage lung ADC.
• To investigate the utility of these risk prediction models in identifying patients who could benefit from ACT.

Main Methods:

• Developed procedure-specific risk prediction models using multivariable regression based on clinicopathologic variables in patients who underwent resection for small (≤2 cm) lung ADC.
• Validated models internally and externally using independent cohorts.
• Assessed the accuracy of risk prediction using a concordance index.
• Applied lobectomy risk prediction to a propensity score-matched cohort of stage II-III patients to compare survival with or without ACT based on risk scores.

Main Results:

• Identified key predictors for recurrence, including micropapillary pattern, solid pattern, lymphovascular invasion, and necrosis, varying slightly between lobectomy and sublobar resection.
• Both internal and external validation demonstrated good discrimination (concordance index ranging from 0.73 to 0.79).
• In stage II-III patients, ACT significantly reduced lung cancer-specific death risk in high-risk patients (subhazard ratio 0.43, p=0.001) but not in low-risk patients.

Conclusions:

• Procedure-specific risk prediction models are effective for prognostication and stratifying patients with resected small lung ADC.
• These models can guide decisions regarding adjuvant chemotherapy, particularly in high-risk patient groups.