Predicting exploratory thoracotomy in non-small cell lung cancer: a computed tomography based nomogram approach

Area of Science:

• Thoracic oncology
• Radiology
• Surgical oncology

Background:

• Non-small cell lung cancer (NSCLC) poses a significant global health burden.
• Surgical resection is a primary treatment for NSCLC, but exploratory thoracotomy without complete resection impacts prognosis.
• Predicting the need for exploratory thoracotomy is crucial for optimizing patient outcomes.

Purpose of the Study:

• To develop a predictive model for exploratory thoracotomy risk in central NSCLC patients.
• To analyze the impact of CT imaging features on postoperative survival after neoadjuvant therapy.
• To identify key factors influencing outcomes in central NSCLC patients undergoing surgery.

Main Methods:

• Retrospective review of clinical and radiological data from central NSCLC patients (January 2017-June 2023).
• Multivariate regression analysis to identify independent risk factors for exploratory thoracotomy.
• Development and validation of a nomogram model using CT imaging features (tumor area, vascular deformation).
• Cox regression analysis to assess disease-free survival (DFS) and overall survival (OS).

Main Results:

• A nomogram incorporating tumor area and vascular deformation demonstrated strong predictive performance for exploratory thoracotomy.
• A tumor area <250 mm² on critical CT slices correlated with improved DFS and OS post-neoadjuvant therapy and R0 resection.
• Postoperative immunotherapy may enhance survival in patients who underwent exploratory thoracotomy.

Conclusions:

• CT imaging post-neoadjuvant therapy is vital for predicting exploratory thoracotomy risk in central NSCLC.
• CT-derived metrics, specifically tumor area, are critical for assessing postoperative survival.
• The developed nomogram offers a valuable tool for risk stratification and treatment planning in central NSCLC.