¹⁸F-FDG PET/CT-based deep learning models and a clinical-metabolic nomogram for predicting high-grade patterns in...

To develop and validate deep learning (DL) and traditional clinical-metabolic (CM) models based on 18 F-FDG PET/CT images for noninvasively predicting high-grade patterns (HGPs) of invasive lung adenocarcinoma (LUAD).

A total of 303 patients with invasive LUAD were enrolled in this retrospective study; these patients were randomly divided into training, validation and test sets at a ratio of 7:1:2. DL models were trained and optimized on PET, CT and PET/CT fusion images, respectively. CM model was built from clinical and PET/CT metabolic parameters via backwards stepwise logistic regression and visualized via a nomogram. The prediction performance of the models was evaluated mainly by the area under the curve (AUC). We also compared the AUCs of different models for the test set.

CM model was established upon clinical stage (OR: 7.30; 95% CI: 2.46-26.37), cytokeratin 19 fragment 21 - 1 (CYFRA 21-1, OR: 1.18; 95% CI: 0.96-1.57), mean standardized uptake value (SUVmean, OR: 1.31; 95% CI: 1.17-1.49), total lesion glycolysis (TLG, OR: 0.994; 95% CI: 0.990-1.000) and size (OR: 1.37; 95% CI: 0.95-2.02). Both the DL and CM models exhibited good prediction efficacy in the three cohorts, with AUCs ranging from 0.817 to 0.977. For the test set, the highest AUC was yielded by the CT-DL model (0.895), followed by the PET/CT-DL model (0.882), CM model (0.879) and PET-DL model (0.817), but no significant difference was revealed between any two models.

Deep learning and clinical-metabolic models based on the ¹⁸F-FDG PET/CT model could effectively identify LUAD patients with HGP. These models could aid in treatment planning and precision medicine.

Not applicable.