Leveraging machine learning with dynamic 18F-FDG PET/CT: integrating metabolic and flow features for lung cancer differential diagnosis.

ABSTRACT:

Background

Dynamic 18F-fluorodeoxyglucose (18F-FDG) PET/CT imaging has been shown to provide additional information for diagnosing lung cancer. The aim of this study was to investigate whether metabolic and flow features directly extracted from time activity curves (TACs) help differentiate between benign and malignant conditions of lung lesions.

Methods

TACs at the primary lesion were extracted from each dynamic 18F-FDG PET/CT scan. The TAC signal was then decomposed into metabolism and blood flow components through kinetic modeling. Dynamic features including area under the curve (AUC), time-to-peak, and slopes were then extracted from each component. The extracted features from 187 patients (mean age, 60.41 ± 11.01 years; 117 males) were used to train a classification mod

SUBMITTER: Yao Z 

PROVIDER: S-EPMC12316803 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature