Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study

Objective To investigate the prognostic value of 18 F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treat...

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Bibliographic Details
Published in:Annals of nuclear medicine 2024-08, Vol.38 (8), p.647-658
Main Authors: Lue, Kun-Han, Chen, Yu-Hung, Chu, Sung-Chao, Lin, Chih-Bin, Wang, Tso-Fu, Liu, Shu-Hsin
Format: Article
Language:English
Subjects:
Adenocarcinoma
Cancer
Deep learning
Epidermal growth factor receptors
Growth factors
Imaging
Independent variables
Kinases
Lung cancer
Lungs
Medicine
Medicine & Public Health
Nuclear Medicine
Original Article
Patients
Positron emission
Positron emission tomography
Prediction models
Predictions
Radiology
Scanners
Survival
Tumors
Tyrosine
Tyrosine kinase inhibitors
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Summary:Objective To investigate the prognostic value of 18 F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment. Methods We retrospectively analyzed the pre-treatment 18 F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog ( n  = 166) and digital ( n  = 51) PET cohorts. 18 F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively. Results In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUV max , metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p  
ISSN:0914-7187
1864-6433
1864-6433
DOI:10.1007/s12149-024-01936-2