Ultra-early changes in vascular parameters from dynamic contrast enhanced MRI of breast cancer xenografts following systemic therapy with doxorubicin and liver X receptor agonist

Dynamic Contrast-Enhanced MRI in the Abdomen of Mice with High Temporal and Spatial Resolution Using Stack-of-Stars Sampling and KWIC Reconstruction

Application of quantitative dynamic contrast-enhanced (DCE) MRI in mouse models of abdominal cancer is challenging due to the effects of RF inhomogeneity, image corruption from rapid respiratory motion and the need for high spatial and temporal resolutions. Here we demonstrate a DCE protocol optimized for such applications. The method consists of three acquisitions: (1) actual flip-angle B₁ mapping, (2) variable flip-angle T₁ mapping and (3) acquisition of the DCE series using a motion-robust radial strategy with k-space weighted image contrast (KWIC) reconstruction. All three acquisitions employ spoiled radial imaging with stack-of-stars sampling (SoS) and golden-angle increments between the views. This scheme is shown to minimize artifacts due to respiratory motion while simultaneously facilitating view-sharing image reconstruction for the dynamic series. The method is demonstrated in a genetically engineered mouse model of pancreatic ductal adenocarcinoma and yielded mean perfusion parameters of K^trans = 0.23 ± 0.14 min⁻¹ and v_e = 0.31 ± 0.17 (n = 22) over a wide range of tumor sizes. The SoS-sampled DCE method is shown to produce artifact-free images with good SNR leading to robust estimation of DCE parameters.

Multimodal Imaging Response after the Singular or Combination Treatments of Vascular Endothelial Growth Factor Inhibitor and Immune Checkpoint Inhibitor

This study aims to dynamically assess tumor changes after variable treatments with vascular endothelial growth factor (VEGF) inhibitor and/or immune checkpoint inhibitor (ICI) using multimodal imaging of MRI and ¹⁸F-FDG PET/CT in a hepatocellular carcinoma (HCC) mice model. Based on different treatments, 24 mice were randomly divided into four groups: control (isotype-matched IgG antibody 10 mg/kg), VEGF inhibitor (sorafenib 50 mg/kg), ICI (anti-PD-L1 antibody 10 mg/kg), and combination groups (sorafenib 50 mg/kg + anti-PD-L1 antibody 10 mg/kg). Quantitative imaging assessments, including volume transfer constant (K^trans), apparent diffusion coefficient (ADC), lactate/choline ratio, and the maximum standardized ¹⁸F-FDG uptake value ratio of tumor to muscle (SUV_tumor/SUV_muscle ratio), were acquired at different time points (before treatment and 7, 14, and 21 days after treatment). Quantitative data were presented as the mean ± standard errors and two-way repeated-measure ANOVA tests were performed for intergroup and intertime point comparisons. After 21 days from the initiation of therapies, combination group showed the lowest tumor volume and weight, followed by ICI, VEGF inhibitor, and control group, with no significance between the VEGF inhibitor and control groups. In addition, K^trans values significantly decreased, and the lactate/choline ratio and SUV_tumor/SUV_muscle ratio were significantly elevated in the VEGF inhibitor group. ADC significantly increased in the ICI and combination groups, with no significant differences in ADC observed between the control and VEGF inhibitor groups, which showed a similar dynamic change to the tumor volume. Furthermore, K^trans, lactate/choline ratio, and ADC were significantly correlated with CD31⁺ area, hypoxyprobe⁺ area, and apoptosis, respectively. Our results suggest that the singular treatment and combination of the VEGF inhibitor and ICI treatments for HCC present different multimodal imaging changes in accordance with the specific histopathological features. These findings might facilitate the formulation of better treatment response criteria; besides, we find ADC is probably an indicator easily to obtain for treatment response evaluation.

Artificial Intelligence Algorithm in Classification and Recognition of Primary Hepatic Carcinoma Images under Magnetic Resonance Imaging

This study aimed to discuss the application value of the bias field correction algorithm in magnetic resonance imaging (MRI) images of patients with primary hepatic carcinoma (PHC). In total, 52 patients with PHC were selected as the experimental group and divided into three subgroups: mild (15 cases), moderate (19 cases), and severe (18 cases) according to pathological grading. Another 52 patients with hepatic nodules in the same period were included in the control group. All the patients underwent dynamic contrast-enhanced (DCE) MRI examination, and the image qualities of MRI before and after bias field correction were compared. The DCE-MRI perfusion parameters were measured, including the transport constant Ktrans, reverse rate constant Kep, extravascular extracellular volume fraction (Ve), plasma volume (Vp), microvascular density (MVD), hepatic artery perfusion index (HPI), mean transit time of contrast agent (MTT), time to peak (TTP), blood volume (BV), hepatic arterial perfusion (HAP), full perfusion (FP), and portal venous perfusion (PVP). It was found that the sensitivity (93.63%), specificity (71.62%), positive predictive value (95.63%), negative predictive value (71.62%), and accuracy (90.01%) of MRI examination processed by the bias field correction algorithm were all significantly greater than those before processing (P < 0.05). The Ktrans, Kep, Ve, Vp, and MVD of patients in the experimental group were significantly larger than those of the control group, and severe group> moderate group> mild group (P < 0.05). HPI, MTT, TTP, BV, and HAP of patients in the experimental group were also significantly greater than those of the control group, which was shown as severe group > moderate group > mild group (P < 0.05). FP and PVP of the experimental group were significantly lower than those of the control group, and severe group < moderate group < mild group (P < 0.05). It was suggested that in MRI images of patients with PHC, the bias field correction algorithm could significantly improve the diagnosis rate. Each perfusion parameter was related to the pathological grading, which could be used to evaluate the prognosis of patients.

The potential of advanced MR techniques for precision radiotherapy of glioblastoma

As microscopic tumour infiltration of glioblastomas is not visible on conventional magnetic resonance (MR) imaging, an isotropic expansion of 1-2 cm around the visible tumour is applied to define the clinical target volume for radiotherapy. An opportunity to visualize microscopic infiltration arises with advanced MR imaging. In this review, various advanced MR biomarkers are explored that could improve target volume delineation for radiotherapy of glioblastomas. Various physiological processes in glioblastomas can be visualized with different advanced MR techniques. Combining maps of oxygen metabolism (CMRO₂), relative cerebral blood volume (rCBV), vessel size imaging (VSI), and apparent diffusion coefficient (ADC) or amide proton transfer (APT) can provide early information on tumour infiltration and high-risk regions of future recurrence. Oxygen consumption is increased 6 months prior to tumour progression being visible on conventional MR imaging. However, presence of the Warburg effect, marking a switch from an infiltrative to a proliferative phenotype, could result in CMRO₂ to appear unaltered in high-risk regions. Including information on biomarkers representing angiogenesis (rCBV and VSI) and hypercellularity (ADC) or protein concentration (APT) can omit misinterpretation due to the Warburg effect. Future research should evaluate these biomarkers in radiotherapy planning to explore the potential of advanced MR techniques to personalize target volume delineation with the aim to improve local tumour control and/or reduce radiation-induced toxicity.

Preclinical Applications of Multi-Platform Imaging in Animal Models of Cancer

In animal models of cancer, oncologic imaging has evolved from a simple assessment of tumor location and size to sophisticated multimodality exploration of molecular, physiologic, genetic, immunologic, and biochemical events at microscopic to macroscopic levels, performed noninvasively and sometimes in real time. Here, we briefly review animal imaging technology and molecular imaging probes together with selected applications from recent literature. Fast and sensitive optical imaging is primarily used to track luciferase-expressing tumor cells, image molecular targets with fluorescence probes, and to report on metabolic and physiologic phenotypes using smart switchable luminescent probes. MicroPET/single-photon emission CT have proven to be two of the most translational modalities for molecular and metabolic imaging of cancers: immuno-PET is a promising and rapidly evolving area of imaging research. Sophisticated MRI techniques provide high-resolution images of small metastases, tumor inflammation, perfusion, oxygenation, and acidity. Disseminated tumors to the bone and lung are easily detected by microCT, while ultrasound provides real-time visualization of tumor vasculature and perfusion. Recently available photoacoustic imaging provides real-time evaluation of vascular patency, oxygenation, and nanoparticle distributions. New hybrid instruments, such as PET-MRI, promise more convenient combination of the capabilities of each modality, enabling enhanced research efficacy and throughput.