Machine Learning-Driven Radiomics for an Early-Stage Predictive Model of Nodal Upstaging in Thoracic Oncology.
Authors
Affiliations (7)
Affiliations (7)
- Cardiothoracic Department, Thoracic Surgery Unit, Spedali Civili, 25123 Brescia, Italy.
- Nuclear Medicine Department, Università degli Studi di Brescia, 25121 Brescia, Italy.
- Nuclear Medicine Department, ASST Spedali Civili of Brescia, 25123 Brescia, Italy.
- Units of Biostatistics and Biomathematics and Bioinformatics, Department of Molecular and Translational Medicine, University of Brescia, 25121 Brescia, Italy.
- Department of Thoracic Surgery, Università degli Studi di Padova, 35122 Padova, Italy.
- Department of Thoracic Surgery, Università degli Studi di Modena e Reggio Emilia, 41121 Modena, Italy.
- Medical Oncology, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health Medical, ASST-Spedali Civili, University of Brescia, 25121 Brescia, Italy.
Abstract
<b>Objectives:</b> Precise lymph node staging remains a cornerstone in the management of early-stage and locally advanced non-small cell lung cancer (NSCLC), directly influencing surgical planning and multimodal therapy. Despite the widespread use of 2-[18F]FDG PET/CT, occult nodal metastases frequently lead to unexpected upstaging after surgery, potentially affecting prognosis and therapeutic strategies. This study aimed to investigate whether radiomic features derived from preoperative PET/CT scans can predict nodal involvement in patients with early-stage lung cancer. <b>Methods:</b> A retrospective analysis was conducted on 124 patients with cT1N0 NSCLC who underwent 2-[18F]FDG PET/CT scans as part of the preoperative workup, followed by anatomical lung resection and systematic mediastinal lymph node dissection. Radiomic features were extracted from PET predictive of pathological nodal upstaging. <b>Results:</b> During the study period, 67 patients who underwent anatomical lung resection for early-stage lung cancer demonstrated unexpected nodal metastasis; a continuous series of 57 patients with the same clinical TMN was enrolled as a control group. Several radiomic parameters were significantly associated with nodal upstaging. According to variable importance (VIMP) analysis, metabolic tumor volume (MTV), total lesion glycolysis (TLG), run-length non-uniformity (RLNU), and gray-level non-uniformity (GLNU) emerged as the strongest predictors of lymph node involvement. <b>Conclusions:</b> Although the clinical utility of these findings remains to be validated, radiomic analysis of 2-[18F]FDG PET/CT imaging offers non-invasive biomarkers that may enhance the preoperative prediction of nodal involvement in early-stage NSCLC. Integrating radiomics into clinical workflows could improve surgical decision-making, refine patient selection, and reduce the incidence of unforeseen nodal upstaging.