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Brain metastases (BM), along with primary central nervous system lymphomas and glioblastomas, represent the majority of malignant brain tumors encountered in clinical neuro-oncology, driving a need for advanced imaging techniques and post-processing methods to improve their characterization and treatment monitoring. In particular, stereotactic radiosurgery (SRS), a cornerstone treatment for BM, delivers high-dose, focused radiation (>20 Gy) to target lesions with minimal impact on surrounding tissues. Despite its efficacy, radiation-induced effects such as early radiation effects (ERE) and adverse radiation effects (ARE) complicate diagnosis and management, with ARE occurring in up to 30% of patients, often presenting as ring-enhancing T2/FLAIR hyperintensities. To address these challenges, we aimed to compare standard radiomics-based machine learning approaches with pretrained generative models for assessing ERE in BM lesions. A cohort of 21 patients for a total of 35 lesions (17 treatment-naïve and 18 post-SRS +/- combination therapy) who underwent multiparametric ¹⁸F-FPIA PET/MRI was analyzed. The study investigated: 1) Multiparametric analysis of PET and MRI diffusion/perfusion parameters (ADC, K^trans, CBF, K₁, v_t); 2) MRI-based radiomics; 3) static PET radiomics; 4) Dynomics; 5) a combination of PET and MRI radiomics; and 6) low-level embeddings from a pretrained generative diffusion model applied to full T1, static PET, and their combination. Using manually contoured lesion masks for analyses 1-5 and lesion-free embeddings for analysis 6, multiple classifiers (SVM, XGBoost, Linear regressor) were applied after feature standardization and principal component analysis (retaining 90% variance). Fivefold cross-validation demonstrated comparable performances across radiomic approaches (Accuracy: 71.95±0.05%, AUC: 0.72±0.05%), while the pretrained generative model achieved significantly higher performance (Accuracy: 83.82±0.01%, AUC: 0.83±0.01%) without requiring lesion segmentation in assessing ERE in BM lesions.Clinical Relevance-This study shows the potential of generative models to streamline and enhance the assessment of early radiation effects in parenchymal metastatic lesions without need of lesion segmentation.