Preoperative Risk Assessment of Lymph Node Metastasis in cT1 Lung Cancer: A Retrospective Study from Eastern China

Abstract

Background: Lymph node status of clinical T1 (diameter ≤ 3 cm) lung cancer largely affects the treatment strategies in the clinic. In order to assess lymph node status before operation, we aim to develop a noninvasive predictive model using preoperative clinical information.

Methods: We retrospectively reviewed 924 patients (development group) and 380 patients (validation group) of clinical T1 lung cancer. Univariate analysis followed by polytomous logistic regression was performed to estimate different risk factors of lymph node metastasis between N1 and N2 diseases. A predictive model of N2 metastasis was established with dichotomous logistic regression, externally validated and compared with previous models.

Results: Consolidation size and clinical N stage based on CT were two common independent risk factors for both N1 and N2 metastases, with different odds ratios. For N2 metastasis, we identified five independent predictors by dichotomous logistic regression: peripheral location, larger consolidation size, lymph node enlargement on CT, no smoking history, and higher levels of serum CEA. The model showed good calibration and discrimination ability in the development data, with the reasonable Hosmer-Lemeshow test (p = 0.839) and the area under the ROC being 0.931 (95% CI: 0.906-0.955). When externally validated, the model showed a great negative predictive value of 97.6% and the AUC of our model was better than other models.

Conclusion: In this study, we analyzed risk factors for both N1 and N2 metastases and built a predictive model to evaluate possibilities of N2 metastasis of clinical T1 lung cancers before the surgery. Our model will help to select patients with low probability of N2 metastasis and assist in clinical decision to further management.

Figure 1

Flowchart of patient selection and exclusion.

Figure 2

Nomogram predicting the likelihood of N2 metastasis in early lung cancers (tumor ≤ 3 cm). According to the location of value from the 2nd to the 6th axis, we can get the vertically corresponding points on the first axis. By summing up each points, we get a total point, and the vertically corresponding predicted value on the last axis shows the predicted possibility of N2 metastasis.

Figure 3

Calibration curve of the logistic regression model. The red line indicated a perfect prediction of observed possibilities. The black line represented the entire development group (n = 924).

Figure 4

The receiver operating characteristic curve for the development and validation groups. (a) The ROC curve for the development group. The AUC was 0.931 (95% CI: 0.906-0.955). (b) The ROC curve for the validation group. The AUC was 0.906 (95% CI: 0.857-0.956).

Figure 5

Comparison of our model and other published models using data from the same validation group. (a) Comparison with Zang et al. (2017) in cT1NxM0 patients. The AUC was 0.879 validated by our data (95% CI: 0.821-0.937). DeLong test for comparing two ROC curves: p = 0.405. (b) Comparison of our model with Zhang et al. (2012) in cT1N0M0 patients. The AUC was 0.712 validated by our data (95% CI: 0.602-0.822). DeLong test for comparing two ROC curves: p = 0.002.