Hepatocellular carcinoma (HCC) develops predominantly in the inflammatory environment of a cirrhotic liver caused by hepatitis, toxin exposure, or chronic liver disease. A targeted therapeutic approach is required to enable cancer killing without causing toxicity and liver failure. Poly(beta-amino-ester) (PBAE) nanoparticles (NPs) were used to deliver a completely CpG-free plasmid harboring mutant herpes simplex virus type 1 sr39 thymidine kinase (sr39) DNA to human HCC cells. Transfection with sr39 enables cancer cell killing with the prodrug ganciclovir and accumulation of 9-(4-¹⁸F-fluoro-3-hydroxymethylbutyl)guanine (¹⁸F-FHBG) for in vivo imaging. Targeting was achieved using a CpG-free human alpha fetoprotein (AFP) promoter (CpGf-AFP-sr39). Expression was restricted to AFP-producing HCC cells, enabling selective transfection of orthotopic HCC xenografts. CpGf-AFP-sr39 NP treatment resulted in 62% reduced tumor size, and therapeutic gene expression was detectable by positron emission tomography (PET). This systemic nanomedicine achieved tumor-specific delivery, therapy, and imaging, representing a promising platform for targeted treatment of HCC.

Despite initial promise, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-based approaches to cancer treatment have yet to yield a clinically approved therapy, due to delivery challenges, a lack of potency, and drug resistance. To address these challenges, we have developed poly(beta-amino ester) (PBAE) nanoparticles (NPs), as well as an engineered cDNA sequence encoding a secretable TRAIL (sTRAIL) protein, to enable reprogramming of liver cancer cells to locally secrete TRAIL protein. We show that sTRAIL initiates apoptosis in transfected cells and has a bystander effect to non-transfected cells. To address TRAIL resistance, NP treatment is combined with histone deacetylase inhibitors, resulting in >80% TRAIL-mediated cell death in target cancer cells and significantly slowed xenograft tumor growth. This anti-cancer effect is specific to liver cancer cells, with up to 40-fold higher cell death in HepG2 cancer cells over human hepatocytes. By combining cancer-specific TRAIL NPs with small-molecule-sensitizing drugs, this strategy addresses multiple challenges associated with TRAIL therapy and offers a new potential approach for cancer treatment.

Enhanced myometrial vascularity is a pregnancy-related hypervascularity that may persist with retained products of conception and is distinct from arteriovenous malformation in cause, imaging findings, and management.

BACKGROUND AND PURPOSE: To evaluate the radiomics-based model performance for differentiation between glioblastoma (GB) and brain metastases (BM) using magnetization prepared rapid gradient echo (MPRAGE) and volumetric interpolated breath-hold examination (VIBE) T1-contrast enhanced sequences. MATERIALS AND METHODS: T1-CE MPRAGE and VIBE sequences acquired in 108 patients (31 GBs and 77 BM) during the same MRI session were retrospectively evaluated. Post standardized image pre-processing and segmentation, radiomics features were extracted from necrotic and enhancing tumor components. Pearson correlation analysis of radiomics features from tumor subcomponents was also performed. A total of 90 machine learning (ML) pipelines were evaluated using a five-fold cross validation. Performance was measured by mean AUC-ROC, Log-loss and Brier scores. RESULTS: A feature-wise comparison showed that the radiomic features between sequences were strongly correlated, with the highest correlation for shape-based features. The mean AUC across the top-ten pipelines ranged between 0.851-0.890 with T1-CE MPRAGE and between 0.869-0.907 with T1-CE VIBE sequence. Top performing models for the MPRAGE sequence commonly used support vector machines, while those for VIBE sequence used either support vector machines or random forest. Common feature reduction methods for top-performing models included linear combination filter and least absolute shrinkage and selection operator (LASSO) for both sequences. For the same ML-feature reduction pipeline, model performances were comparable (AUC-ROC difference range: [-0.078, 0.046]). CONCLUSIONS: Radiomic features derived from T1-CE MPRAGE and VIBE sequences are strongly correlated and may have similar overall classification performance for differentiating GB from BM. ABBREVIATIONS: BM: Brain metastases, GB: glioblastoma, T1-CE: T1 contrast enhanced sequence, MPRAGE: magnetization prepared rapid gradient echo, ML: machine learning, RF: random forest, VIBE: volumetric interpolated breath-hold examination.

Introduction: Peptide Receptor Radionuclide Therapy (PRRT) with [¹⁷⁷Lu]Lu-DOTA-TATE is effective in treating advanced Neuroendocrine Tumors (NETs), yet predicting individual response in this treatment remains a challenge due to inter-lesion heterogeneity. There is a lack of standardized, effective methods for using multi-lesion radiomics to predict progression and Time to Progression (TTP) in PRRT-treated patients. This study evaluated how aggregating radiomic features from multiple PET-identified lesions can be used to predict disease progression (event [progression and death] vs. event-free) and TTP. Methods: Eighty-one NETs patients with multiple lesions underwent pre-treatment PET/CT imaging. Lesions were segmented and ranked by minimum Standard Uptake Value (SUV_min) (both descending and ascending), SUV_mean, SUV_max, and volume (descending). From each sorting, the top one, three, and five lesions were selected. For the selected lesions, radiomic features were extracted (using the Pyradiomics library) and lesion aggregation was performed using stacked vs. statistical methods. Eight classification models along with three feature selection methods were used to predict progression, and five survival models and three feature selection methods were used to predict TTP under a nested cross-validation framework. Results: The overall appraisal showed that sorting lesions based on SUV_min (descending) yields better classification performance in progression prediction. This is in addition to the fact that aggregating features extracted from all the lesions, as well as the top five lesions sorted by SUV_mean, lead to the highest overall performance in TTP prediction. The individual appraisal in progression prediction models trained on the single top lesion sorted by SUV_min (descending) showed the highest recall and specificity despite data imbalance. The best-performing model was the Logistic Regression (LR) classifier with Recursive Feature Elimination (RFE) (recall: 0.75, specificity: 0.77). In TTP prediction, the highest concordance index was obtained using a Random Survival Forest (RSF) trained on statistically aggregated features from the top five lesions ranked by SUV_mean, selected via Univariate C-Index (UCI) (C-index = 0.68). Across both tasks, features from the Gray Level Size Zone Matrix (GLSZM) family were consistently among the most predictive, highlighting the importance of spatial heterogeneity in treatment response. Conclusions: This study demonstrates that informed lesion selection and tailored aggregation strategies significantly impact the predictive performance of radiomics-based models for progression and TTP prediction in PRRT-treated NET patients. These approaches can potentially enhance model accuracy and better capture tumor heterogeneity, supporting more personalized and practical PRRT implementation.