Independent Validation of a Machine Learning Classifier for Predicting Mediastinal Lymph Node Metastases in Non-Small Cell Lung Cancer Using Routinely Obtainable [¹⁸F]FDG-PET/CT Parameters.

ABSTRACT: In non-small cell lung cancer (NSCLC), [¹⁸F]FDG-PET/CT is limited in pretherapeutic lymph node (LN) staging by false-positives. We previously demonstrated that a machine learning (ML) classifier using routine [¹⁸F]FDG-PET/CT and clinical variables can improve diagnostic accuracy compared to visual assessment. The present study aimed at independent validation. Cohort 1 (Charité) included 87 NSCLC patients (surgical and non-surgical), prospectively enrolled at our institution. Cohort 2 (TCIA) comprised 124 patients with primary surgery from the multi-institution NSCLC Radiogenomics dataset. Our ML classifier for differentiating N0/1 vs. N2/3 status was applied without modification. As comparator, the combined standard PET/CT criterion of "mediastinal LN uptake > mediast

SUBMITTER: Wdowiak A 

PROVIDER: S-EPMC12732160 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature