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Objective Using radiomics to compute quantitative imaging features may reveal information beyond standard magnetic resonance imaging (MRI) metrics. We aim to investigate the test-retest repeatability of ¹⁹F MRI radiomic features in phantoms containing two perfluorocarbons and to validate these findings in a pilot in vivo mouse tumor model. Materials and methods Two phantoms containing perfluoropolyether (PFPE) or perfluoro-15-crown-5 ether (PFCE) were repeatedly scanned (intrasession and intersession) using a 7-T system equipped with a dual-tuned ¹H/¹⁹F volume coil. Radiomic features were extracted and assessed for stability using the concordance correlation coefficient (CCC) ≥ 0.85 and normalized dynamic range ≥ 0.90. A separate in vivo test-retest experiment was conducted in tumor-bearing mice injected with a PFPE nanoemulsion. Results A total of 194 scans and 772 segments were evaluated across the PFPE phantom, PFCE phantom, and in vivo experiments. In both phantoms, radiomic features displayed high intrasession repeatability (median CCC up to 0.886) but decreased intersession repeatability (median CCC down to 0.683). Intensity features were consistently more repeatable ( p  < 0.003) than shape or texture features. We found that 23.1% (466/2,013) of features were repeatable across phantoms. In vivo pilot scans showed that 86.1% (401/466) of these phantom-stable features, or ~20.0% overall, remained repeatable under physiological conditions. Conclusion Several ¹⁹F MRI-derived features exhibited excellent short-term repeatability, and a considerable proportion proved robust to intersession variability. These robust features may reliably capture ¹⁹F signals under both phantom and physiological conditions, paving the way for more quantitative imaging analysis in this modality and encouraging general reproducibility of data. Relevance statement Key Points We analyzed 194 ¹⁹F MRI scans and 772 segments obtained in phantoms at 7 T. Cross-agent stability identified 466 radiomic features meeting concordance correlation coefficient ≥ 0.85 and normalized dynamic range ≥ 0.90. Of these phantom-stable features, 401 of 466 remained stable in vivo in a tumor mouse model. Intensity features were most repeatable, while shape features were least stable across sessions. Median concordance correlation coefficient dropped from 0.886 intrasession to 0.683 intersession. Graphical Abstract

Extracellular vesicles (EVs) secreted by prostate cancer (PCa) cells contain specific biomarkers and can be isolated from urine. Collection of urine is not invasive, and therefore urinary EVs represent a liquid biopsy for diagnostic and prognostic testing for PCa. In this study, we optimised urinary EV isolation using a method based on heat shock proteins and compared it to gold-standard ultracentrifugation. The urinary EV isolation protocol using the Vn96-peptide is easier, time convenient (≈1.5 h) and no special equipment is needed, in contrast to ultracentrifugation protocol (>3.5 h), making this protocol clinically feasible. We compared the isolated vesicles of both ultracentrifugation and Vn96-peptide by proteome profiling using mass spectrometry-based proteomics (n = 4 per method). We reached a depth of >3000 proteins, with 2400 proteins that were commonly detected in urinary EVs from different donors. We show a large overlap (>85%) between proteins identified in EVs isolated by ultracentrifugation and Vn96-peptide. Addition of the detergent NP40 to Vn96-peptide EV isolations reduced levels of background proteins and highly increased the levels of the EV-markers TSG101 and PDCD6IP, indicative of an increased EV yield. Thus, the Vn96-peptide-based EV isolation procedure is clinically feasibly and allows large-scale protein profiling of urinary EV biomarkers.

Objective Radiogenomics promises noninvasive tumor profiling; however, the extent to which imaging morphology reflects tumor lineage versus host-organ milieu remains unclear. This study aimed to quantify the relative influence of tumor type and anatomical environment on contrast-enhanced computed tomography (CT) radiomic phenotypes. Materials and methods A discovery cohort of 1,598 patients (10,485 lesions) and an external validation cohort of 2,440 patients (6,597 lesions) underwent portal-venous-phase CT. After manual segmentation, lesion-level radiomic features were standardized and embedded using t -distributed stochastic neighbor embedding. Bayesian-optimized agglomerative clustering defined morphology-based groups. Concordance with the primary tumor site (lineage) and anatomical environment was quantified using bootstrapped adjusted Rand indices (ARI); the silhouette score assessed clustering quality. Feature-class (shape, intensity, texture) and mask-erosion experiments probed mechanistic drivers. Results Six morphological clusters were identified in the discovery set (silhouette = 0.44). Morphology aligned more strongly with environment (mean ARI = 0.37) but poorly with lineage (mean ARI = 0.04; p  < 0.010); this pattern held externally. In solid organ metastases, environment dominance was even stronger (mean ARI = 0.60 versus 0.05; p  < 0.010). Intensity and texture drove the morphological association with anatomical environment (ARI = 0.64–0.56) more than shape (ARI = 0.06). When the periphery of the tumor was eroded, the same patterns were observed, implicating the tumor core. Conclusion Across organs and tumor types, tumor morphological phenotype on CT imaging is largely driven by a host tissue-related environmental “imprint” rather than the primary tumor site. Relevance statement Context-aware modeling is essential for reliable radiomic biomarkers and could motivate a two-step AI pipeline that first identifies the organ habitat and refines lineage-specific predictions. Key Points In a large, multicenter cohort, tumors exhibited distinct morphological clustering. These clusters did not align with primary tumor sites (ARI = 0.04). Stronger associations emerged between morphological clusters and the local anatomical environment (ARI = 0.37). Stratification by lesion type revealed even stronger associations between local anatomical context and solid organ metastases (ARI = 0.60). Graphical Abstract