Background: Nonsmall cell lung cancer (NSCLC) remains a significant global health burden, necessitating advancements in diagnostic and prognostic strategies. Liquid biopsy and radiomics offer promising avenues for enhancing preoperative assessment and treatment planning in NSCLC. Methods: This prospective study enrolled 60 NSCLC patients who underwent both computed tomography (CT)-guided biopsy and liquid biopsy. Radiomic features were extracted from CT images, and circulating tumor DNA (ctDNA) was sequenced to identify genetic mutations. Machine learning algorithms were employed to assess the association between radiomic features and gene mutations. Results: Among 57 patients with available data, associations between radiomic features and gene pairs mutation obtained from liquid biopsy exhibited moderate accuracy (approximately 0.60), with texture features demonstrating higher importance. However, when predicting the combined mutation status of gene pairs (e.g., EGFR and ROS1), the classification task involved three classes and yielded substantially lower accuracy (approximately 0.30), likely due to class imbalance and increased complexity. Discussion: Our findings demonstrate a moderate association between radiomic features and single gene mutations detected through liquid biopsy in NSCLC patients, with classification accuracies reaching approximately 0.60. In contrast, classification performance significantly declined (to ~0.30) when gene mutation pairs were used as targets, likely due to increased complexity and class imbalance. Notably, second-order texture features showed the highest importance in the models. These preliminary results suggest that radiomics may capture aspects of tumor biology reflected in liquid biopsy, warranting further validation in larger, well-balanced cohorts. Conclusion: The integration of liquid biopsy and radiomics holds promise for enhancing preoperative assessment and personalized treatment strategies in NSCLC. Further research on larger cohorts is warranted to validate the findings and translate them into clinical practice. Trial Registration: University of Campania Trial Board UC20201112-24997.
Radiomic Analysis and Liquid Biopsy in Preoperative CT of NSCLC: An Explorative Experience
Belfiore, Maria Paola;Sansone, Mario;Ciani, Giovanni;Genco, Carlotta;Montella, Marco;Monti, Riccardo;Cappabianca, Salvatore;Reginelli, Alfonso
2025
Abstract
Background: Nonsmall cell lung cancer (NSCLC) remains a significant global health burden, necessitating advancements in diagnostic and prognostic strategies. Liquid biopsy and radiomics offer promising avenues for enhancing preoperative assessment and treatment planning in NSCLC. Methods: This prospective study enrolled 60 NSCLC patients who underwent both computed tomography (CT)-guided biopsy and liquid biopsy. Radiomic features were extracted from CT images, and circulating tumor DNA (ctDNA) was sequenced to identify genetic mutations. Machine learning algorithms were employed to assess the association between radiomic features and gene mutations. Results: Among 57 patients with available data, associations between radiomic features and gene pairs mutation obtained from liquid biopsy exhibited moderate accuracy (approximately 0.60), with texture features demonstrating higher importance. However, when predicting the combined mutation status of gene pairs (e.g., EGFR and ROS1), the classification task involved three classes and yielded substantially lower accuracy (approximately 0.30), likely due to class imbalance and increased complexity. Discussion: Our findings demonstrate a moderate association between radiomic features and single gene mutations detected through liquid biopsy in NSCLC patients, with classification accuracies reaching approximately 0.60. In contrast, classification performance significantly declined (to ~0.30) when gene mutation pairs were used as targets, likely due to increased complexity and class imbalance. Notably, second-order texture features showed the highest importance in the models. These preliminary results suggest that radiomics may capture aspects of tumor biology reflected in liquid biopsy, warranting further validation in larger, well-balanced cohorts. Conclusion: The integration of liquid biopsy and radiomics holds promise for enhancing preoperative assessment and personalized treatment strategies in NSCLC. Further research on larger cohorts is warranted to validate the findings and translate them into clinical practice. Trial Registration: University of Campania Trial Board UC20201112-24997.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
