Machine Learning-Driven Radiomics for an Early-Stage Predictive Model of Nodal Upstaging in Thoracic Oncology.

Objectives

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.

Methods

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.

Results

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.

Conclusions

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.