Radiomics nomogram integrating intratumoural and peritumoural features to predict lymph node metastasis and prognosis in clinical stage IA non-small cell lung cancer: a two-centre study.

Aim: To investigate the value of a radiomics nomogram integrating intratumoural and peritumoural features in predicting lymph node metastasis and overall survival (OS) in patients with clinical stage IA non-small-cell lung cancer (NSCLC).

Materials And Methods: This study retrospectively enrolled 199 patients (training cohort: 71 patients from Affiliated Tumour Hospital of Nantong University; internal validation cohort: 46 patients from Affiliated Tumour Hospital of Nantong University; external validation cohort: 82 patients from the public database). CT radiomics models were constructed based on four volumes of interest: gross tumour volume (GTV), gross and 3 mm peritumoural volume (GPTV), gross and 6 mm peritumoural volume (GPTV), and gross and 9 mm peritumoural volume (GPTV). The optimal radiomics signature was further combined with independent clinical predictors to develop a nomogram. Univariable and multivariable Cox regression analysis were applied to determine the relationship between factors and OS.

Results: GPTV radiomics yielded better performance than GTV, GPTV, and, GPTV radiomics in the training (area under the curve [AUC], 0.81), internal validation (AUC, 0.79), and external validation cohorts (AUC, 0.71), respectively. The nomogram integrating GPTV radiomics and spiculation improved predictive ability, with AUCs of 0.85, 0.80, and 0.74 in three cohorts, respectively. Pathological lymph node metastasis, nomogram-predicted lymph node metastasis, and pleural indentation were independent risk predictors of OS (p<0.05).

Conclusions: The nomogram integrating GPTV radiomics features and independent clinical predictors performed well in predicting lymph node metastasis and prognosis in patients with clinical stage IA NSCLC.