Predicting pathological lymph node status in clinical stage IA peripheral lung adenocarcinoma

OBJECTIVES: Even with current diagnostic technology, it is difficult to accurately predict pathological lymph node status (PLNS). This study aimed to develop a prediction model of PLNS in peripheral adenocarcinoma with a dominant solid component, based on clinical and radiological factors on thin-section computed tomography, to identify patients to whom wedge resection or other local therapies could be applied. METHODS: Of 811 patients enrolled in a prospective multi-institutional study (JCOG0201), 420 patients with clinical stage IA peripheral lung adenocarcinoma having a dominant solid component were included. Multivariable logistic regression was performed to develop a model based on clinical and centrally reviewed radiological factors. Leave-one-out cross-validation and external validation analyses were performed, using independent data from 221 patients. Sensitivity, specificity and concordance statistics were calculated to evaluate diagnostic performance. RESULTS: The formula for calculating the probability of pathological lymph node metastasis included the following variables: tumour diameter (including ground-glass opacity), consolidation-to-tumour ratio and density of solid component. The concordance statistic was 0.8041. When the cut-off value associated with the risk of incorrectly predicting negative pathological lymph node metastasis (pN-) was 4.9%, diagnostic sensitivity and specificity in predicting PLNS were 95.7% and 46.0%, respectively. The concordance statistic for the external validation set was 0.7972, and diagnostic sensitivity and specificity in predicting PLNS were 95.4% and 40.5%, respectively. CONCLUSIONS: The proposed model is clinically useful and successfully predicts pN- in patients with clinical stage IA peripheral lung adenocarcinoma with a dominant solid component.

Automated summary

The study aimed to develop a prediction model of pathological lymph node status (PLNS) in peripheral adenocarcinoma with a dominant solid component, based on clinical and radiological factors. The model showed high diagnostic performance in predicting PLNS, successfully identifying patients who could benefit from wedge resection or other local therapies.