A multi-model fusion approach incorporating conventional radiological and machine learning features across age spectrum for periorbital fat status prediction.

Objectives: To develop an ensemble learning model fusing conventional radiomics (CR) and machine learning (ML) features to assess periorbital fat status across the entire age spectrum. Methods: Retrospective analysis was conducted on preoperative cranial and facial MRI data of meningioma patients. Patients were categorized into youth, middle-aged, and senior groups and allocated to training and test sets through stratified random sampling. CR and ML features of fat in three periorbital regions were extracted to develop an ensemble learning model, with its clinical application value subsequently evaluated. Results: 237 patients were enrolled: 165 in the training set and 72 in the test set. The training set comprised 19 youth cases (28.5 ± 5.0, 7 male), 41 middle-aged cases (42.9 ± 4.7, 9 male), and 105 senior cases (60.0 ± 6.5, 26 male). The test set included 8 youth cases (28.6 ± 5.6, 4 male), 18 middle-aged cases (43.9 ± 4.1, 6 male), and 46 senior cases (58.8 ± 6.7, 10 male). The ensemble learning model outperformed the CR model, the ML model, and the CR-ML fusion model on the test set, achieving an AUC-macro of 0.833 (95% CI: 0.737-0.902), an F1-score of 0.614, an accuracy (Acc) of 0.597, and a positive predictive value (PPV) of 0.690. Ensemble learning models demonstrated optimal comprehensive capabilities in multi-classification tasks, enhancing generalization and robustness. Conclusion: Our ensemble learning model achieved non-invasive and reliable assessment of periorbital fat status across the entire age spectrum, enriching the evaluation methodology for rejuvenation surgery.