Predictors of occult lymph node metastasis in clinical T1 lung adenocarcinoma: a retrospective dual-center study.

in BMC pulmonary medicine by Xiaoxin Huang, Xiaoxiao Huang, Kui Wang, Lijuan Liu, Guanqiao Jin

TLDR

  • The study identified several predictors of occult lymph node metastasis in patients with clinical T1 lung adenocarcinoma, including lobulation, spiculation, solid density, lymphovascular invasion, and CEA levels. These predictors may aid clinicians in optimizing treatment decisions and improving patient outcomes.

Abstract

The optimal surgical strategy for lymph node dissection in lung adenocarcinoma remains controversial. Accurate predicting occult lymph node metastasis (OLNM) in patients with clinical T1 lung adenocarcinoma is essential for optimizing treatment decisions and improving patient outcomes. This study analyzes the relationship between anaplastic lymphoma kinase (ALK) status, clinicopathological characteristics, computed tomography (CT) features, and OLNM in patients with clinical T1 lung adenocarcinoma. A retrospective analysis was conducted on data from patients with clinical T1 lung adenocarcinoma who showed no lymph node metastasis on preoperative CT and underwent surgical resection with lymph node dissection at two centers from January 2016 to December 2023. Univariate and multivariate logistic regression analyses were performed to identify factors associated with OLNM. Among 1138 patients with clinical T1 lung adenocarcinoma, 167 (14.6%) were found to have OLNM, including 55 (4.8%) with pathological N1 status and 112 (9.8%) with pathological N2 status. Multivariate logistic regression analysis identified lobulation, spiculation, solid density, lymphovascular invasion, spread through air spaces (STAS), micropapillary pattern, solid pattern, and carcinoembryonic antigen (CEA) levels as independent positive predictors of OLNM. Furthermore, lobulation, lymphovascular invasion, STAS, micropapillary pattern, solid pattern, CEA levels, and ALK were independent positive predictors of occult N2 lymph node metastasis. The lepidic pattern, however, was identified as an independent negative predictor for OLNM and occult N2 lymph node metastasis. The identified predictors may assist clinicians in evaluating the risk of OLNM in patients with clinical T1 lung adenocarcinoma, potentially guiding more targeted intervention strategies.

Overview

  • The study aimed to investigate the relationship between ALK status, clinicopathological characteristics, CT features, and occult lymph node metastasis (OLNM) in patients with clinical T1 lung adenocarcinoma.
  • The study conducted a retrospective analysis of 1138 patients with clinical T1 lung adenocarcinoma who underwent surgical resection with lymph node dissection between January 2016 and December 2023.
  • The primary objective of the study was to identify predictors of OLNM in patients with clinical T1 lung adenocarcinoma to optimize treatment decisions and improve patient outcomes.

Comparative Analysis & Findings

  • Multivariate logistic regression analysis identified lobulation, spiculation, solid density, lymphovascular invasion, STAS, micropapillary pattern, solid pattern, and CEA levels as independent positive predictors of OLNM.
  • Lobulation, lymphovascular invasion, STAS, micropapillary pattern, solid pattern, CEA levels, and ALK were identified as independent positive predictors of occult N2 lymph node metastasis.
  • The lepidic pattern was identified as an independent negative predictor for OLNM and occult N2 lymph node metastasis.

Implications and Future Directions

  • The identified predictors may assist clinicians in evaluating the risk of OLNM in patients with clinical T1 lung adenocarcinoma, potentially guiding more targeted intervention strategies.
  • Future studies should investigate the predictive values of these factors in different patient populations and explore potential interactions between factors to improve the accuracy of OLNM prediction.
  • The findings of this study have the potential to impact treatment decisions and patient outcomes, and further research is needed to validate and refine the predictors identified in this study.