Speaker
Description
Intracranial metastatic disease (IMD) remains a major cause of cancer-related morbidity and mortality, yet the metabolic heterogeneity of brain metastases is poorly characterized in vivo. Conventional imaging typically treats lesions as metabolically homogeneous entities, potentially obscuring biologically meaningful tumor subregions. Here, we present a multimodal imaging framework integrating dynamic 18F-fluoropivalate (FPIA) positron emission tomography (PET) with multiparametric magnetic resonance imaging (mpMRI) to identify metabolic oligoclones within brain metastases and investigate their biological and clinical relevance.
Twenty-one patients (22 imaging sessions) with brain metastases from lung, breast, melanoma, and colorectal primaries underwent integrated dynamic FPIA PET–MRI either at baseline (treatment-naïve, n = 12) or 4–8 weeks after stereotactic radiosurgery (SRS, n = 10). Voxel-wise PET time–activity curves (TACs) were analyzed using unsupervised time-series k-means clustering, allowing identification of distinct metabolic subpopulations based on tracer kinetic behavior rather than static uptake metrics. PET-derived clusters were spatially mapped to tumor volumes and characterized using diffusion (ADC), perfusion (CBF, CBV), and permeability parameters (Ktrans, vp, ve) derived from dynamic contrast enhanced (DCE) and dynamic susceptibility constrast (DSC) MRI, together with PET kinetic parameters (K1, k2, k3, Ki).
In treatment-naïve lesions, clustering revealed three reproducible metabolic phenotypes: an intermediate kinetic cluster, a fast uptake/clearance cluster, and a slow-trapping cluster characterized by sustained tracer retention and positive Patlak Ki values. These oligoclones were spatially intermingled throughout the tumor volume, demonstrating microscale metabolic heterogeneity. Cluster prevalence was associated with overall survival: a higher proportion of the intermediate kinetic phenotype correlated with longer survival, whereas enrichment of the slow-trapping phenotype was associated with poorer outcomes.
Integration with multiparametric MRI revealed that these metabolic clusters correspond to distinct vascular phenotypes. Regions corresponding to the favorable phenotype exhibited higher tracer delivery, vascular permeability, and plasma volume together with shorter capillary transit times, whereas the unfavorable phenotype showed reduced delivery and permeability with prolonged transit times. Across clusters, capillary transit time demonstrated a strong inverse correlation with vascular permeability, indicating that inefficient microvascular flow is associated with reduced permeability exchange.
A logistic regression model combining cluster prevalence and vascular imaging parameters predicted poor survival with an AUC of 0.86 (leave-one-out cross-validation; permutation p = 0.038), highlighting the prognostic relevance of the identified phenotypes.
These findings demonstrate that dynamic FPIA PET combined with multiparametric MRI enables noninvasive characterization of metabolic–vascular niches within brain metastases. This approach provides a framework for imaging-based phenotyping of tumor heterogeneity and may support biologically informed treatment strategies and adaptive radiotherapy in intracranial metastatic disease.
| Track | PSMR |
|---|---|
| Presentation type | Oral |