Arterial Spin Labeling and Dynamic Susceptibility Contrast-enhanced MR Imaging for evaluation of arteriovenous shunting and tumor hypoxia in glioblastoma

Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer

To develop and validate an enhanced CT-based radiomics nomogram for evaluating preoperative metastasis risk of epithelial ovarian cancer (EOC). One hundred and nine patients with histologically confirmed EOC were retrospectively enrolled. The volume of interest (VOI) was delineated in preoperative enhanced CT images, and 851 radiomics features were extracted. The radiomics features were selected by the least absolute shrinkage and selection operator (LASSO), and the rad-score was calculated using the formula of the radiomics label. A clinical model, radiomics model, and combined model were constructed using the logistic regression classification algorithm. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance of the models. Seventy-five patients (68.8%) were histologically confirmed to have metastasis. Eleven optimal radiomics features were retained by the LASSO algorithm to develop the radiomic model. The combined model for evaluating metastasis of EOC achieved area under the curve (AUC) values of 0.929 (95% CI 0.8593-0.9996) in the training cohort and 0.909 (95% CI 0.7921-1.0000) in the test cohort. To facilitate clinical use, a radiomic nomogram was built by combining the clinical characteristics with rad-score. The DCA indicated that the nomogram had the most significant net benefit when the threshold probability exceeded 15%, surpassing the benefits of both the treat-all and treat-none strategies. Compared with clinical model and radiomics model, the radiomics nomogram has the best diagnostic performance in evaluating EOC metastasis. The nomogram is a useful and convenient tool for clinical doctors to develop personalized treatment plans for EOC patients.

Arterial Spin Labeling Perfusion Imaging

Noninvasive imaging of tissue perfusion is a valuable tool for both research and clinical applications. Arterial spin labeling (ASL) is a contrast-free perfusion imaging method that enables measuring and quantifying tissue blood flow using MR imaging. ASL uses radiofrequency and magnetic field gradient pulses to label arterial blood water, which then serves as an endogenous tracer. This review highlights the basic mechanism of ASL perfusion imaging, labeling strategies, and quantification. ASL has been widely used during the past 30 years for the study of normal brain function as well as in multiple neurovascular, neuro-oncological and degenerative pathologic conditions.

Clinical Significance of Early Venous Filling Detected via Preoperative Angiography in Glioblastoma

Preoperative angiography in glioblastoma (GBM) often shows arteriovenous shunts and early venous filling (EVF). Here, we investigated the clinical implications of EVF in GBM as a prognostic and vascular mimicry biomarker. In this retrospective multicenter study, we consecutively enrolled patients who underwent angiography with a GBM diagnosis between 1 April 2013 and 31 March 2021. The primary and secondary endpoints were the differences in overall survival (OS) and progression-free survival (PFS), respectively, between cases with and without EVF. Of the 133 initially enrolled patients, 91 newly diagnosed with GBM underwent preoperative angiography and became the study population. The 6-year OS and PFS were significantly worse in the EVF than in the non-EVF group. Moreover, 20 GBM cases (10 with EVF and 10 without EVF) were randomly selected and evaluated for histological vascular mimicry. Except for two cases that were difficult to evaluate, the EVF group had a significantly higher frequency of vascular mimicry than the non-EVF group (0/8 vs. 5/10, p = 0.04). EVF on preoperative angiography is a robust prognostic biomarker for GBM and may help detect cases with a high frequency of histological vascular mimicry.

Biomaterial-based in vitro 3D modeling of glioblastoma multiforme

Adult-onset brain cancers, such as glioblastomas, are particularly lethal. People with glioblastoma multiforme (GBM) do not anticipate living for more than 15 months if there is no cure. The results of conventional treatments over the past 20 years have been underwhelming. Tumor aggressiveness, location, and lack of systemic therapies that can penetrate the blood-brain barrier are all contributing factors. For GBM treatments that appear promising in preclinical studies, there is a considerable rate of failure in phase I and II clinical trials. Unfortunately, access becomes impossible due to the intricate architecture of tumors. In vitro, bioengineered cancer models are currently being used by researchers to study disease development, test novel therapies, and advance specialized medications. Many different techniques for creating in vitro systems have arisen over the past few decades due to developments in cellular and tissue engineering. Later-stage research may yield better results if in vitro models that resemble brain tissue and the blood-brain barrier are used. With the use of 3D preclinical models made available by biomaterials, researchers have discovered that it is possible to overcome these limitations. Innovative in vitro models for the treatment of GBM are possible using biomaterials and novel drug carriers. This review discusses the benefits and drawbacks of 3D in vitro glioblastoma modeling systems.

Toward quantification of hypoxia using fluorinated EuII/III-containing ratiometric probes

Hypoxia is a prognostic biomarker of rapidly growing cancers, where the extent of hypoxia is an indication of tumor progression and prognosis; therefore, hypoxia is also used for staging while performing chemo- and radiotherapeutics for cancer. Contrast-enhanced MRI using EuII-based contrast agents is a noninvasive method that can be used to map hypoxic tumors, but quantification of hypoxia using these agents is challenging due to the dependence of signal on the concentration of both oxygen and EuII. Here, we report a ratiometric method to eliminate concentration dependence of contrast enhancement of hypoxia using fluorinated EuII/III-containing probes. We studied three different EuII/III couples of complexes containing 4, 12, or 24 fluorine atoms to balance fluorine signal-to-noise ratio with aqueous solubility. The ratio between the longitudinal relaxation time (T1) and 19F signal of solutions containing different ratios of EuII- and EuIII-containing complexes was plotted against the percentage of EuII-containing complexes in solution. We denote the slope of the resulting curves as hypoxia indices because they can be used to quantify signal enhancement from Eu, that is related to oxygen concentration, without knowledge of the absolute concentration of Eu. This mapping of hypoxia was demonstrated in vivo in an orthotopic syngeneic tumor model. Our studies significantly contribute toward improving the ability to radiographically map and quantify hypoxia in real time, which is critical to the study of cancer and a wide range of diseases.

Prediction of short-term treatment outcome of nasopharyngeal carcinoma based on voxel incoherent motion imaging and arterial spin labeling quantitative parameters

Purpose

To evaluate the early response of chemoradiotherapy (CRT) in nasopharyngeal carcinoma (NPC) based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and three-dimensional pseudo-continuous arterial spin labeling (3D pCASL).

Materials and methods

Forty patients diagnosed with NPC were recruited and divided into complete remission (CR) and partial remission (PR) group after CRT. All patients underwent IVIM and ASL and the related parameters was obtained. These parameters include pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), average blood flow ( BF_avg), minimum blood flow (BF_min), and maximum blood flow (BF_max). Student's t test was used to compare the difference in ASL and IVIM derived parameters between CR and PR. The Areas under curve (AUC) of the receiver operating characteristic (ROC) was used to analyze the diagnostic performance of each parameter of ASL and IVIM to the treatment outcome.

Results

the D value of IVIM in CR group was lower than that of the PR group ( P = 0.014),. Among the parameters of ASL, the BF_avg and BF_max of the CR group were higher than those of the PR group(p = 0.004,0.013), but the BF_min had no statistical significance in the two groups（P = 0.54）. AUC of D, BF_avg, and BF_max is about 0.731, 0.753, and 0.724, respectively, all of their combined AUC diagnosis was 0.812.

Conclusion

The early response of NPC after CRT can predict by IVIM's diffusion parameters and ASL-related blood flow parameters.

Noncontrast Pediatric Brain Perfusion: Arterial Spin Labeling and Intravoxel Incoherent Motion

Noncontrast magnetic resonance imaging techniques for measuring brain perfusion include arterial spin labeling (ASL) and intravoxel incoherent motion (IVIM). These techniques provide noninvasive and repeatable assessment of cerebral blood flow or cerebral blood volume without the need for intravenous contrast. This article discusses the technical aspects of ASL and IVIM with a focus on normal physiologic variations, technical parameters, and artifacts. Multiple pediatric clinical applications are presented, including tumors, stroke, vasculopathy, vascular malformations, epilepsy, migraine, trauma, and inflammation.

Quantification of tumor microenvironment acidity in glioblastoma using principal component analysis of dynamic susceptibility contrast enhanced MR imaging

Glioblastoma (GBM) has high metabolic demands, which can lead to acidification of the tumor microenvironment. We hypothesize that a machine learning model built on temporal principal component analysis (PCA) of dynamic susceptibility contrast-enhanced (DSC) perfusion MRI can be used to estimate tumor acidity in GBM, as estimated by pH-sensitive amine chemical exchange saturation transfer echo-planar imaging (CEST-EPI). We analyzed 78 MRI scans in 32 treatment naïve and post-treatment GBM patients. All patients were imaged with DSC-MRI, and pH-weighting that was quantified from CEST-EPI estimation of the magnetization transfer ratio asymmetry (MTRasym) at 3 ppm. Enhancing tumor (ET), non-enhancing core (NC), and peritumoral T2 hyperintensity (namely, edema, ED) were used to extract principal components (PCs) and to build support vector machines regression (SVR) models to predict MTRasym values using PCs. Our predicted map correlated with MTRasym values with Spearman's r equal to 0.66, 0.47, 0.67, 0.71, in NC, ET, ED, and overall, respectively (p < 0.006). The results of this study demonstrates that PCA analysis of DSC imaging data can provide information about tumor pH in GBM patients, with the strongest association within the peritumoral regions.

Emerging Techniques in Imaging of Glioma Microenvironment

Magnetic resonance imaging (MRI) has been the cornerstone of imaging of brain tumors in the past 4 decades. Conventional MRI remains the workhorse for neuro-oncologic imaging, not only for basic information such as location, extent, and navigation but also able to provide information regarding proliferation and infiltration, angiogenesis, hemorrhage, and more. More sophisticated MRI sequences have extended the ability to assess and quantify these features; for example, permeability and perfusion acquisitions can assess blood-brain barrier disruption and angiogenesis, diffusion techniques can assess cellularity and infiltration, and spectroscopy can address metabolism. Techniques such as fMRI and diffusion fiber tracking can be helpful in diagnostic planning for resection and radiation therapy, and more sophisticated iterations of these techniques can extend our understanding of neurocognitive effects of these tumors and associated treatment responses and effects. More recently, MRI has been used to go beyond such morphological, physiological, and functional characteristics to assess the tumor microenvironment. The current review highlights multiple recent and emerging approaches in MRI to characterize the tumor microenvironment.