A novel nomogram based on PET/CT to predict lymph nodal metastasis for lung adenocarcinoma with normal size lymph node.

Background: A precise assessment of lymph nodal status is essential for guiding an individualized treatment plan in lung adenocarcinoma patients. A novel nomogram using easily accessible indicators was developed and validated in this study to predict CT-negative lymph nodal metastasis.

Methods: Between September 2020 and December 2023, data from 132 consecutive patients diagnosed with lung adenocarcinoma who underwent lung resection with systemic lymph node dissection or sampling were retrospectively reviewed. Risk factors associated with lymph nodal metastasis were identified using univariable and multivariable logistic regression analyses. Subsequently, a nomogram was developed on basis of these identified parameters. The performance and validity of the nomogram were evaluated using the area under the receiver operating characteristic (ROC) curve, calibration curve, and bootstrap resampling techniques.

Results: Four predictors (primary tumor location, primary tumor SUVmax value, N1 lymph node SUVmax, and N2 lymph node SUVmax) were identified and incorporated into the nomogram. The nomogram exhibited notable discrimination, evidenced by an area under the ROC curve of 0.825 (95% CI: 0.749-0.886, P < 0.001). Excellent concordance between the predicted and observed probabilities of lymph nodal involvement was demonstrated by the calibration curve. Furthermore, decision curve analysis indicated a net benefit associated with the use of our nomogram.

Conclusion: The nomogram demonstrated efficacy and practicality in predicting CT-negative lymph node metastasis for lung adenocarcinoma patients. It holds potential to offer valuable treatment guidance for clinicians.