A comprehensive algorithm to distinguish between MPLC and IPM in multiple lung tumors patients

Area of Science:

• Pulmonology
• Oncology
• Medical Imaging

Background:

• Computed tomography (CT) scans facilitate frequent diagnosis of multiple lung nodules.
• Distinguishing intrapulmonary metastasis (IPM) from multiple primary lung cancer (MPLC) remains a clinical challenge.
• Accurate classification is crucial for optimizing therapeutic strategies in multifocal lung carcinomas.

Purpose of the Study:

• To propose and validate a comprehensive algorithm for differentiating IPM from MPLC.
• To investigate the prognostic implications of accurate classification in patients undergoing surgical resection.

Main Methods:

• Retrospective analysis of 576 patients with multiple lung carcinomas (April 2009-December 2017).
• Development of a multidisciplinary algorithm integrating histologic, molecular, and imaging data for nodule classification.
• Survival analysis using Kaplan-Meier method and Cox proportional hazards model.

Main Results:

• The algorithm achieved classification consistency of 0.65 (Martini and Melamed criteria) and 0.72 (ACCP criteria).
• Patients with MPLC (n=405) had significantly different clinical features compared to IPM (n=171).
• Independent primary tumors were associated with better overall survival (OS) than IPM (HR=3.99, P<0.001); nodal involvement and radiotherapy were independent prognostic factors.

Conclusions:

• The developed algorithm is a valuable tool for precise clinical decision-making in classifying multifocal lung tumors.
• Accurate differentiation between MPLC and IPM impacts patient prognosis.
• Absence of lymph node metastasis and radiotherapy correlated with a better prognosis.