18F-FDG PET/CT radiomics model from non-small cell lung cancer for preoperative prediction of lymph node metastasis based on overall data and the subset of occult lymph nodes.

OBJECTIVE

Lymph node (LN) staging in lung cancer is crucial for treatment decisions. To develop and validate a positron emission tomography/computed tomography (PET/CT) radiomics model for preoperative estimation of LN metastasis in non-small cell lung cancer (NSCLC).

SUBJECTS AND

METHODS

A retrospective analysis of 252 NSCLC patients with 548 pathologically confirmed LN, including 227 occult LN, was performed. Clinical and PET/CT features were collected. Eight machine learning models were used for feature selection and radiomics signature (R-signature) construction. Models were developed for both the overall and occult LN groups. Model performance was evaluated using area under the curve (AUC), calibration, and decision curve analysis.

RESULTS

The random forest-enhanced logistic regression (RFELR) model, based on 20 features, showed the best performance in predicting LN metastasis in both groups. The combined model demonstrated the highest predictive efficacy, with AUC of 0.94 (overall LN) and 0.89 (occult LN) in the training cohort, and 0.95 (overall LN) and 0.78 (occult LN) in the validation cohort. The combined model outperformed clinical, CT, and PET models (P<0.05) in both cohorts. Decision curve analysis showed a greater net benefit across a wider range of threshold probabilities for LN metastasis prediction.

CONCLUSION

The combined model, integrating clinical, conventional PET/CT, and radiomics features, significantly enhances LN metastasis diagnosis. It shows promise in predicting occult LN metastasis and offers valuable support for personalized therapeutic decisions in NSCLC patients.