Predictability and Utility of Contrast-Enhanced CT on Occult Lymph Node Metastasis for Patients with Clinical Stage IA-IIA Lung Adenocarcinoma: A Double-Center Study

•Computed tomography provides the most detailed imaging information, and consequently is used as a routine imaging procedure for the staging of TNM in lung cancer patients, which could noninvasively predict the occult lymph node metastasis (OLM). •We found that tumors with a larger overall and solid component size, bronchovascular bundle thickening, lobulation, spiculation, solid texture and obstructive change were more likely to develop lymph nodes metastasis, of which larger consolidation diameter, bronchovascular bundle thickening, lobulation, and obstructive change were the independent predictors of OLM. •Unfortunately, we could not discovered any significant association between computed tomography enhancement features of primary tumor with OLM. Rationale and Objectives With the advantage of minimizing damage and preserving more functional lung tissue, limited surgery is considered depend on the lymph node (LN) involvement situation. However, occult lymph node metastasis (OLM) may be ignored by limited surgery and become a risk factor for local recurrence after surgical resection. The aim of this study was to assess the risk factors for OLM based on computed tomography enhanced image in patients with clinical lung adenocarcinoma (ADC). Materials and Methods From January 2016 to July 2022, 707 patients with clinical stage IA-IIA ADC underwent lobectomy with systematic LN dissection and were divided into training and validation group based on different institution. Univariate analysis followed by multivariable logistic regression were performed to estimate different risk factors of OLM. A predictive model was established with visual nomogram and external validation, and evaluated in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results Fifty-nine patients were diagnosed with OLM (11.9%), and four independent predictors of LN involvement were identified: larger consolidation diameter (odds ratio [OR], 2.35, 95% confidence interval [CI]: 1.06, 5.22, p = 0.013), bronchovascular bundle thickening (OR, 1.99, 95% CI: 1.00, 3.95, p = 0.049), lobulation (OR, 2.92, 95% CI: 1.22, 6.99, p = 0.016) and obstructive change (OR, 1.69, 95% CI: 1.17, 6.16, p = 0.020). The model showed good calibration (Hosmer–Lemeshow goodness–of–fit, p = 0.816) with an AUC of 0.821 (95% CI: 0.775, 0.853). For the validation group, the AUC was 0.788 (95% CI: 0.732, 0.806). Conclusion Our predictive model can non-invasively assess the risk of OLM in patients with clinical stage IA-IIA ADC, enable surgeons perform an individualized prediction preoperatively, and assist the clinical decision–making procedure. With the advantage of minimizing damage and preserving more functional lung tissue, limited surgery is considered depend on the lymph node (LN) involvement situation. However, occult lymph node metastasis (OLM) may be ignored by limited surgery and become a risk factor for local recurrence after surgical resection. The aim of this study was to assess the risk factors for OLM based on computed tomography enhanced image in patients with clinical lung adenocarcinoma (ADC). From January 2016 to July 2022, 707 patients with clinical stage IA-IIA ADC underwent lobectomy with systematic LN dissection and were divided into training and validation group based on different institution. Univariate analysis followed by multivariable logistic regression were performed to estimate different risk factors of OLM. A predictive model was established with visual nomogram and external validation, and evaluated in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Fifty-nine patients were diagnosed with OLM (11.9%), and four independent predictors of LN involvement were identified: larger consolidation diameter (odds ratio [OR], 2.35, 95% confidence interval [CI]: 1.06, 5.22, p = 0.013), bronchovascular bundle thickening (OR, 1.99, 95% CI: 1.00, 3.95, p = 0.049), lobulation (OR, 2.92, 95% CI: 1.22, 6.99, p = 0.016) and obstructive change (OR, 1.69, 95% CI: 1.17, 6.16, p = 0.020). The model showed good calibration (Hosmer–Lemeshow goodness–of–fit, p = 0.816) with an AUC of 0.821 (95% CI: 0.775, 0.853). For the validation group, the AUC was 0.788 (95% CI: 0.732, 0.806). Our predictive model can non-invasively assess the risk of OLM in patients with clinical stage IA-IIA ADC, enable surgeons perform an individualized prediction preoperatively, and assist the clinical decision–making procedure.