Diagnostic Accuracy of 18F-FDG-PET/CT and MRI in Predicting the Tumor Response in Locally Advanced Cervical Carcinoma Treated by Chemoradiotherapy: A Meta-AnalysisRead the full article
Contrast Media & Molecular Imaging is an exciting journal in the area of contrast agents and molecular imaging, covering all areas of imaging technologies with a special emphasis on MRI and PET.
Chief Editor, Professor Zimmer, focuses on the development and use of PET radiotracers for new applications of PET/MRI imaging in neuroscience and pharmacology.
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Zinc Preconditioning Provides Cytoprotection following Iodinated Contrast Media Exposure in In Vitro Models
Introduction & Objectives. Contrast media (CM) causes renal injury through both direct cellular injury (cytotoxicity) and regional vascular changes (renal hypoxia) mediated by reactive oxygen species (ROS). Zinc may be able to provide protection against CM-induced cytotoxicity due to its indirect antioxidant properties and subsequent effect on ROS. We aimed to determine the protective role of zinc preconditioning against contrast-induced renal injury in vitro. Methods. Normal human proximal renal kidney cells (HK-2) were preconditioned with either increasing doses of ZnCl2 or control. Following this preconditioning, cells were exposed to increasing concentrations of Iohexol 300 mg I2/ml for four hours. Key outcome measures included cell survival (MTT colorimetric assay) and ROS generation (H2DCFDA fluorescence assay). Results. Contrast media induced a dose-dependent reduction in survival of HK-2 cells. Compared to control, contrast media at 150, 225, and 300 mg I2/ml resulted in 69.5% (SD 8.8%), 37.3% (SD 4.8%), and 4.8% (SD 6.6%) cell survival, respectively (). Preconditioning with 37.5 μM and 50 μM ZnCl2 increased cell survival by 173% (SD 27.8%) () and 219% (SD 32.2%) (), respectively, compared to control preconditioning. Zinc preconditioning resulted in a reduction of ROS generation. Zinc pre-conditioning with 37.5 μM μM ZnCl2 reduced ROS generation by 46% () compared to control pre-conditioning. Conclusions. Zinc preconditioning reduces oxidative stress following exposure to radiographic contrast media which in turn results in increased survival of renal cells. Translation of this in vitro finding in animal models will lay the foundation for future use of zinc preconditioning against contrast induced nephropathy.
Predictive Ki-67 Proliferation Index of Cervical Squamous Cell Carcinoma Based on IVIM-DWI Combined with Texture Features
Purpose. This study aims to determine whether IVIM-DWI combined with texture features based on preoperative IVIM-DWI could be used to predict the Ki-67 PI, which is a widely used cell proliferation biomarker in CSCC. Methods. A total of 70 patients were included. Among these patients, 16 patients were divided into the Ki-67 PI <50% group and 54 patients were divided into the Ki-67 PI ≥50% group based on the retrospective surgical evaluation. All patients were examined using a 3.0T MRI unit with one standard protocol, including an IVIM-DWI sequence with 10 b values (0–1,500 sec/mm2). The maximum level of CSCC with a b value of 800 sec/mm2 was selected. The parameters (diffusion coefficient (D), microvascular volume fraction (f), and pseudodiffusion coefficient (D)) were calculated with the ADW 4.6 workstation, and the texture features based on IVIM-DWI were measured using GE AK quantitative texture analysis software. The texture features included the first order, GLCM, GLSZM, GLRLM, and wavelet transform features. The differences in IVIM-DWI parameters and texture features between the two groups were compared, and the ROC curve was performed for parameters with group differences, and in combination. Results. The D value in the Ki-67 PI ≥50% group was lower than that in the Ki-67 PI <50% group (). A total of 1,050 texture features were obtained using AK software. Through univariate logistic regression, mPMR feature selection, and multivariate logistic regression, three texture features were obtained: wavelet_HHL_GLRLM_ LRHGLE, lbp_3D_k_ firstorder_IR, and wavelet_HLH_GLCM_IMC1. The AUC of the prediction model based on the three texture features was 0.816, and the combined D value and three texture features was 0.834. Conclusions. Texture analysis on IVIM-DWI and its parameters was helpful for predicting Ki-67 PI and may provide a noninvasive method to investigate important imaging biomarkers for CSCC.
Prediction of Histologic Subtype and FNCLCC Grade by SUVmax Measured on 18F-FDG PET/CT in Patients with Retroperitoneal Liposarcoma
This study aimed to evaluate the usefulness of maximum standardized uptake value (SUVmax) on 18F-fluorodeoxyglucose positron emission tomography with computed tomography (18F-FDG PET/CT) in differentiating the subtypes and tumor grades of retroperitoneal liposarcoma (RPLS). The data of RPLS patients who underwent surgical resection from November 2013 to December 2019 at the sarcoma center of our institute were reviewed. The demographics, clinical features, and SUVmax of 84 patients who underwent preoperative 18F-FDG PET/CT scans were analyzed. Of these, 19 patients (22.6%) were with well-differentiated liposarcoma (WDLPS), 60 patients (71.4%) were with dedifferentiated liposarcoma (DDLPS), and 5 patients (6.0%) were with pleomorphic liposarcoma (PMLPS). The median SUVmax of WDLPS, DDLPS, and PMLPS groups was 2.8 (IQR: 1.9–3.2), 6.2 (IQR: 4.1–11.3), and 4.5 (IQR: 4.0–7.4). The ROC curve suggested 3.8 as an approximate cutoff value of SUVmax for distinguishing WDLPS and non-WDLPS (sensitivity = 0.769; specificity = 0.895). The median SUVmax for FNCLCC Grades 1, 2, and 3 of RPLS was 2.5 (IQR: 1.9–3.2), 4.5 (IQR: 3.2–6.7), and 9.0 (IQR: 6.0–13.3). The ROC curves suggest that SUVmax of ≤3.8 and >5.3 can be used for predicting FNCLCC Grades 1 and 3, respectively. The result showed that 18F-FDG PET/CT exhibited high sensitivity and specificity for identifying the subtypes and FNCLCC grades of RPLS. Additionally, 18F-FDG PET/CT might be a useful complementary imaging modality for guiding suitable biopsy location of RPLS.
Amphiphilic Polymer-Modified Uniform CuFeSe2 Nanoparticles for CT/MR Dual-Modal Imaging
Recently, magnetic photothermal nanomaterials have attracted much attention in the diagnosis and treatment of cancer. In this study, we developed the ultrasmall magnetic CuFeSe2 nanoparticles for CT/MR dual-modal imaging. By controlling the reaction time and condition, CuFeSe2 nanoparticles were synthesized by a simple directly aqueous method. After modification with copolymer methoxy polyethylene glycol-polycaprolactone (MPEG-PCL), the obtained MPEG-PCL@CuFeSe2 nanoparticles showed excellent water solubility, colloidal stability, and biocompatibility. In addition, they also exhibited superparamagnetism and X-ray’s characteristics. For these properties, they will become ideal nanomaterials for CT/MR dual-modal imaging.
Iron (III)-Quercetin Complex: Synthesis, Physicochemical Characterization, and MRI Cell Tracking toward Potential Applications in Regenerative Medicine
In cell therapy, contrast agents T1 and T2 are both needed for the labeling and tracking of transplanted stem cells over extended periods of time through magnetic resonance imaging (MRI). Importantly, the metal-quercetin complex via coordination chemistry has been studied extensively for biomedical applications, such as anticancer therapies and imaging probes. Herein, we report on the synthesis, characterization, and labeling of the iron (III)-quercetin complex, “IronQ,” in circulating proangiogenic cells (CACs) and also explore tracking via the use of a clinical 1.5 Tesla (T) MRI scanner. Moreover, IronQ had a paramagnetic T1 positive contrast agent property with a saturation magnetization of 0.155 emu/g at 1.0 T and longitudinal relaxivity (r1) values of 2.29 and 3.70 mM−1s−1 at 1.5 T for water and human plasma, respectively. Surprisingly, IronQ was able to promote CAC growth in conventional cell culture systems without the addition of specific growth factors. Increasing dosages of IronQ from 0 to 200 μg/mL led to higher CAC uptake, and maximum labeling time was achieved in 10 days. The accumulated IronQ in CACs was measured by two methodologies, an inductively coupled plasma optical emission spectrometry (ICP-EOS) and T1-weighted MRI. In our research, we confirmed that IronQ has excellent dual functions with the use of an imaging probe for MRI. IronQ can also act as a stimulating agent by favoring circulating proangiogenic cell differentiation. Optimistically, IronQ is considered beneficial for alternative labeling and in the tracking of circulation proangiogenic cells and/or other stem cells in applications of cell therapy through noninvasive magnetic resonance imaging in both preclinical and clinical settings.
Diagnostic Accuracy of Machine Learning-Based Radiomics in Grading Gliomas: Systematic Review and Meta-Analysis
Purpose. This study aimed to estimate the diagnostic accuracy of machine learning- (ML-) based radiomics in differentiating high-grade gliomas (HGG) from low-grade gliomas (LGG) and to identify potential covariates that could affect the diagnostic accuracy of ML-based radiomic analysis in classifying gliomas. Method. A primary literature search of the PubMed database was conducted to find all related literatures in English between January 1, 2009, and May 1, 2020, with combining synonyms for “machine learning,” “glioma,” and “radiomics.” Five retrospective designed original articles including LGG and HGG subjects were chosen. Pooled sensitivity, specificity, their 95% confidence interval, area under curve (AUC), and hierarchical summary receiver-operating characteristic (HSROC) models were obtained. Result. The pooled sensitivity when diagnosing HGG was higher (96% (95% CI: 0.93, 0.98)) than the specificity when diagnosing LGG (90% (95% CI 0.85, 0.93)). Heterogeneity was observed in both sensitivity and specificity. Metaregression confirmed the heterogeneity in sample sizes (), imaging sequence types (), and data sources (), but not for the inclusion of the testing set (), feature extraction number (), and selection of feature number (). The results of subgroup analysis indicate that sample sizes of more than 100 and feature selection numbers less than the total sample size positively affected the diagnostic performance in differentiating HGG from LGG. Conclusion. This study demonstrates the excellent diagnostic performance of ML-based radiomics in differentiating HGG from LGG.