We have previously demonstrated that endothelial targeting of gold nanoparticles followed by external beam irradiation can cause specific tumor vascular disruption in mouse models of cancer. The induced vascular damage may lead to changes in tumor physiology, including tumor hypoxia, thereby compromising future therapeutic interventions. In this study, we investigate the dynamic changes in tumor hypoxia mediated by targeted gold nanoparticles and clinical radiation therapy (RT). By using noninvasive whole-body fluorescence imaging, tumor hypoxia was measured at baseline, on day 2 and day 13, post-tumor vascular disruption. A 2.5-fold increase (P<0.05) in tumor hypoxia was measured two days after combined therapy, resolving by day 13. In addition, the combination of vascular-targeted gold nanoparticles and radiation therapy resulted in a significant (P<0.05) suppression of tumor growth. This is the first study to demonstrate the tumor hypoxic physiological response and recovery after delivery of vascular-targeted gold nanoparticles followed by clinical radiation therapy in a human non-small cell lung cancer athymic Foxn1^nu mouse model.

Objectives: The potential use of electron density (ED) and effective atomic number (Zeff) derived from dual-energy computed tomography (DECT) as novel quantitative imaging biomarkers for differentiating malignant brain tumors was investigated. Methods: Data pertaining to 136 patients with a pathological diagnosis of brain metastasis (BM), glioblastoma, and primary central nervous system lymphoma (PCNSL) were retrospectively reviewed. The 10th percentile, mean and 90th percentile values of conventional 120-kVp CT value (CTconv), ED, Zeff, and relative apparent diffusion coefficient derived from diffusion-weighted magnetic resonance imaging (rADC: ADC of lesion divided by ADC of normal-appearing white matter) within the contrast-enhanced tumor region were compared across the three groups. Furthermore, machine learning (ML)-based diagnostic models were developed to maximize diagnostic performance for each tumor classification using the indices of DECT parameters and rADC. Machine learning models were developed using the AutoGluon-Tabular framework with rigorous patient-level data splitting into training (60%), validation (20%), and independent test sets (20%). Results: The 10th percentile of Zeff was significantly higher in glioblastomas than in BMs (p = 0.02), and it was the only index with a significant difference between BMs and glioblastomas. In the comparisons including PCNSLs, all indices of CTconv, Zeff, and rADC exhibited significant differences (p < 0.001–0.02). DECT-based ML models exhibited high area under the receiver operating characteristic curves (AUC) for all pairwise differentiations (BMs vs. Glioblastomas: AUC = 0.83; BMs vs. PCNSLs: AUC = 0.91; Glioblastomas vs. PCNSLs: AUC = 0.82). Combined models of DECT and rADC demonstrated excellent diagnostic performance between BMs and PCNSLs (AUC = 1) and between Glioblastomas and PCNSLs (AUC = 0.93). Conclusion: This study suggested the potential of DECT-derived ED and Zeff as novel quantitative imaging biomarkers for differentiating malignant brain tumors.

Background: The optimal protocol for clinical liver ³¹ P‐magnetic resonance spectroscopy (MRS) remains unclear. Single‐voxel ³¹ P‐MRS using image‐selected in vivo spectroscopy (ISIS) employs respiratory‐triggering (RT) or free‐breathing (FB) acquisition. RT provides robust data but prolongs scan duration; FB allows faster acquisition but may suffer from low signal‐to‐noise ratio (SNR). Yet, direct comparison using a regulatory‐approved coil in a clinical setting has not been reported. Purpose: To compare ³¹ P‐MRS data stability and robustness between RT and FB. Study Type: Prospective. Population: 24 volunteers (19 male/5 female). Field Strength/Sequence: 3 T MRI; single‐voxel ³¹ P‐MRS using ISIS (free induction decay‐based) with approved ³¹ P coil. Methods: ³¹ P‐MRS was performed using RT and FB techniques (128 and 192 signal averages, respectively; expected scan duration ~13 min each). Spectra were analyzed using jMRUI. SNR and peak areas for PME, Pi, PDE, α‐ATP, and NADPH, normalized using γ‐ATP, were compared. PME/PDE and NADPH/(PME + PDE) ratios were also compared. Statistical Test: Paired t ‐tests and Bland–Altman analysis were used. A p value < 0.05 was considered significant. Results: Scan duration was significantly longer for RT (15 min 44 s) than FB (12 min 56 s). No significant differences were observed for SNR ( p = 0.570), NADPH/(PME + PDE) ( p = 0.931), PME/γ‐ATP ( p = 0.556), Pi/γ‐ATP ( p = 0.931), α‐ATP/γ‐ATP ( p = 0.332), or NADPH/γ‐ATP ( p = 0.394). Significant differences were noted for PDE/γ‐ATP (RT 1.68 vs. FB 1.35, p = 0.003) and PME/PDE (RT 0.434 vs. FB 0.489, p = 0.046). Bland–Altman analysis showed near‐zero fixed biases and no proportional bias, with limits of agreement from −0.53 to 0.62 (PME), −0.30 to 0.30 (Pi), −0.41 to 1.07 (PDE), −0.45 to 0.43 (α‐ATP), and −0.80 to 0.90 (NADPH). Data Conclusion: ³¹ P‐MRS of the liver showed equivalent stability and robustness for RT and FB. FB yielded comparable data within a shorter, predictable scan duration. Evidence Level: 2. Technical Efficacy: 1.