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The following is a summary of “Evaluating peritumoral and intratumoral radiomics signatures for predicting lymph node metastasis in surgically resectable non-small cell lung cancer,” published in the October 2024 issue of Oncology by Xu et al.   

Lymph node metastasis in non-small cell lung cancer (NSCLC) is crucial for determining prognosis and treatment options. Accurate preoperative assessment of lymph node involvement can significantly impact clinical decision-making for patients with NSCLC.  

Researchers conducted a retrospective study evaluating the effectiveness of radiomic signatures in predicting lymph node metastasis in patients with NSCLC.  

They enrolled 247 patients with resectable NSCLC who underwent preoperative chest CT scans to identify lung nodules, followed by lobectomies and lymph node sampling or dissection. Intratumoral and peritumoral radiomic features were extracted from CT images and used as covariates to predict lymph node metastasis status. Model performance was compared using receiver operating characteristic (ROC) curves, DeLong tests, calibration curves, and decision curve analysis (DCA).  

The results showed that the intra-tumoral-peri-tumoral model (0.953, 95% CI 0.9272-0.9792) outperformed the intratumoral radiomics model (0.898, 95% CI 0.8553-0.9402) and the clinical model (0.818, 95% CI 0.7653-0.8709) in predicting lymph node metastasis. The DeLong test demonstrated that the performance of the intratumoral and peritumoral radiomics models was superior to the intratumoral or clinical feature models (P<0.001).  

They concluded that a radiomics-based model incorporating both peritumoral and intratumoral features from CT images can accurately predict lymph node metastasis in NSCLC, outperforming traditional methods.  

Source: frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1427743/full