CT radiomics-histopathological correlation for mediastinal lymph node staging in non-small cell lung cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) staging relies on accurate assessment of mediastinal lymph nodes. This study investigates the utility of radiomic features derived from contrast-enhanced thoracic CT scans in predicting malignancy in clinically positive (cN+) mediastinal lymph nodes. METHODS: A retrospective cohort of 110 NSCLC patients with cN + who underwent surgical resection was analyzed. 3D segmentations of up to three lymph nodes per patient were performed. Radiomic features, encompassing heterogeneity measures, were extracted. A radiomics model was constructed using a random forest and XGboost algorithm. To ensure robustness and minimize bias, 100 iterations of data splitting were conducted to create distinct training and test sets for reproducibility and statistical reliability. RESULTS: The radiomics model achieved an area under the curve (AUC) of 0.703 for Forest model and 063 for XGboost model. Three recurrent features in the radiomic signatures, "RootMeanSquared", "Grey Level Co-Occurrence Matrix Imc2", and "Grey Level Run Length Matrix RunEntropy", highlighted the importance of nodal heterogeneity features in the model. CONCLUSION: Radiomic features extracted from contrast-enhanced thoracic CT scans could predict malignancy in cN+ mediastinal lymph nodes of NSCLC patients (AUC = 0.703). Three features of heterogeneity were recurrent in radiomic signature, emphasizing the potential importance of incorporating nodal heterogeneity criteria in addition to size assessment.