Nomograms for intraoperative prediction of lymph node metastasis in clinical stage IA lung adenocarcinoma

Cancer Medicine(2023)

Cited 1|Views2
No score
Abstract
Abstract Background Accurate prediction of lymph node metastasis (LNM) is critical for selecting optimal surgical procedures in early‐stage lung adenocarcinoma (LUAD). This study aimed to develop nomograms for intraoperative prediction of LNM in clinical stage IA LUAD. Methods A total of 1227 patients with clinical stage IA LUADs on computed tomography (CT) were enrolled to construct and validate nomograms for predicting LNM (LNM nomogram) and mediastinal LNM (LNM‐N2 nomogram). Recurrence‐free survival (RFS) and overall survival (OS) were compared between limited mediastinal lymphadenectomy (LML) and systematic mediastinal lymphadenectomy (SML) in the high‐ and low‐risk groups for LNM‐N2, respectively. Results Three variables were incorporated into the LNM nomogram and the LNM‐N2 nomogram, including preoperative serum carcinoembryonic antigen (CEA) level, CT appearance, and tumor size. The LNM nomogram showed good discriminatory performance, with C‐indexes of 0.879 (95% CI, 0.847–0.911) and 0.880 (95% CI, 0.834–0.926) in the development and validation cohorts, respectively. The C‐indexes of the LNM‐N2 nomogram were 0.812 (95% CI, 0.766–0.858) and 0.822 (95% CI, 0.762–0.882) in the development and validation cohorts, respectively. LML and SML had similar survival outcomes among patients with low risk of LNM‐N2 (5‐year RFS, 88.1% vs. 89.5%, P p = 0.790; 5‐year OS, 96.0% vs. 93.0%, p = 0.370). However, for patients with high risk of LNM‐N2, LML was associated with worse survival (5‐year RFS, 64.0% vs. 77.4%, p = 0.036; 5‐year OS, 66.0% vs. 85.9%, p = 0.038). Conclusions We developed and validated nomograms to predict LNM and LNM‐N2 intraoperatively in patients with clinical stage IA LUAD on CT. These nomograms may help surgeons to select optimal surgical procedures.
More
Translated text
Key words
lymph node metastasis,lung adenocarcinoma,intraoperative prediction,nomograms
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined