A nomogram for predicting the risk of lymph node metastasis in T1-2 non-small-cell lung cancer based on PET/CT and clinical characteristics

Abstract

Background: Accurately predicting the risk level for a lymph node metastasis is critical in the treatment of non-small cell lung cancer (NSCLC). This study aimed to construct a novel nomogram to identify patients with a risk of lymph node metastasis in T1-2 NSCLC based on positron emission tomography/computed tomography (PET/CT) and clinical characteristics.

Methods: From January 2011 to November 2017, the records of 318 consecutive patients who had undergone PET/CT examination within 30 days before surgical resection for clinical T1-2 NSCLC were retrospectively reviewed. A nomogram to predict the risk of lymph node metastasis was constructed. The model was confirmed using bootstrap resampling, and an independent validation cohort contained 156 patients from June 2017 to February 2020 at another institution.

Results: Six factors [age, tumor location, histology, the lymph node maximum standardized uptake value (SUVmax), the tumor SUVmax and the carcinoembryonic antigen (CEA) value] were identified and entered into the nomogram. The nomogram developed based on the analysis showed robust discrimination, with an area under the receiver operating characteristic curve of 0.858 in the primary cohort and 0.749 in the validation cohort. The calibration curve for the probability of lymph node metastasis showed excellent concordance between the predicted and actual results. Decision curve analysis suggested that the nomogram was clinically useful.

Conclusions: We set up and validated a novel and effective nomogram that can predict the risk of lymph node metastasis for individual patients with T1-2 NSCLC. This model may help clinicians to make treatment recommendations for individuals.

Keywords: Non-small cell lung cancer (NSCLC); lymph node status; model; nomogram; positron emission tomography/computed tomography (PET/CT).

Figure 1

Nomogram for predicting the risk of lymph nodes metastasis in non-small-cell lung cancer. The value of each variable was given a score on the point scale axis. A total score could be easily calculated by adding every single score and, by projecting the total score to the lower total point scale, we were able to estimate the probability of lymph nodes metastasis.

Figure 2

The calibration curves for the nomogram. (A) Primary cohort; (B) validation cohort. The x-axis represents the nomogram predicted probability, and the y-axis represents the actual probability of lymph nodes metastasis. A perfect prediction would correspond to the diagonal blue solid line. The red dashed line represents the performance of the nomogram, of which a closer fit to the diagonal line represents a better prediction.

Figure 3

Decision curve analysis for the nomogram. The y-axis measures the net benefit. The red line represents the nomogram. The blue line represents the assumption that all patients have lymph nodes metastasis. The green line represents the assumption that no patients have lymph nodes metastasis. The net benefit was calculated by subtracting the proportion of all patients who are false positive from the proportion, which is a true positive.