Amide Proton Transfer Imaging Allows Detection of Glioma Grades and Tumor Proliferation: Comparison with Ki-67 Expression and Proton MR Spectroscopy Imaging

Utilizing the amide proton transfer technique to characterize diffuse gliomas based on the WHO 2021 classification of CNS tumors

PURPOSE

Diffuse gliomas present a significant challenge for healthcare systems globally. While brain MRI plays a vital role in diagnosis, prognosis, and treatment monitoring, accurately characterizing gliomas using conventional MRI techniques alone is challenging. In this study, we explored the potential of utilizing the amide proton transfer (APT) technique to predict tumor grade and type based on the WHO 2021 Classification of CNS Tumors.

METHODS

Forty-two adult patients with histopathologically confirmed brain gliomas were included in the study. They underwent 3T MRI imaging, which involved APT sequence. Multinomial and binary logistic regression models were employed to classify patients into clinically relevant groups based on MRI findings and demographic variables.

RESULTS

We found that the best model for tumor grade classification included patient age along with APT values. The highest sensitivity (88%) was observed for Grade 4 tumors, while Grade 3 tumors showed the highest specificity (79%). For tumor type classification, our model incorporated four predictors: APT values, patient's age, necrosis, and the presence of hemorrhage. The glioblastoma group had the highest sensitivity and specificity (87%), whereas balanced accuracy was the lowest for astrocytomas (0.73).

CONCLUSION

The APT technique shows great potential for noninvasive evaluation of diffuse gliomas. The changes in the classification of gliomas as per the WHO 2021 version of the CNS Tumor Classification did not affect its usefulness in predicting tumor grade or type.

Amide proton transfer weighted imaging in pediatric neuro-oncology: initial experience

Amide proton transfer weighted (APTw) imaging enables in vivo assessment of tissue-bound mobile proteins and peptides through the detection of chemical exchange saturation transfer. Promising applications of APTw imaging have been shown in adult brain tumors. As pediatric brain tumors differ from their adult counterparts, we investigate the radiological appearance of pediatric brain tumors on APTw imaging. APTw imaging was conducted at 3 T. APTw maps were calculated using magnetization transfer ratio asymmetry at 3.5 ppm. First, the repeatability of APTw imaging was assessed in a phantom and in five healthy volunteers by calculating the within-subject coefficient of variation (wCV). APTw images of pediatric brain tumor patients were analyzed retrospectively. APTw levels were compared between solid tumor tissue and normal-appearing white matter (NAWM) and between pediatric high-grade glioma (pHGG) and pediatric low-grade glioma (pLGG) using t-tests. APTw maps were repeatable in supratentorial and infratentorial brain regions (wCV ranged from 11% to 39%), except those from the pontine region (wCV between 39% and 50%). APTw images of 23 children with brain tumor were analyzed (mean age 12 years ± 5, 12 male). Significantly higher APTw values are present in tumor compared with NAWM for both pHGG and pLGG (p < 0.05). APTw values were higher in pLGG subtype pilocytic astrocytoma compared with other pLGG subtypes (p < 0.05). Non-invasive characterization of pediatric brain tumor biology with APTw imaging could aid the radiologist in clinical decision-making.

Amide proton transfer-weighted imaging of pediatric brainstem glioma and its predicted value for H3 K27 alteration

BACKGROUND

Non-invasive determination of H3 K27 alteration of pediatric brainstem glioma (pedBSG) remains a clinical challenge.

PURPOSE

To predict H3 K27-altered pedBSG using amide proton transfer-weighted (APTw) imaging.

MATERIAL AND METHODS

This retrospective study included patients with pedBSG who underwent APTw imaging and had the H3 K27 alteration status determined by immunohistochemical staining. The presence or absence of foci of markedly increased APTw signal in the lesion was visually assessed. Quantitative APTw histogram parameters within the entire solid portion of tumors were extracted and compared between H3 K27-altered and wild-type groups using Student's t-test. The ability of APTw for differential diagnosis was evaluated using logistic regression.

RESULTS

Sixty pedBSG patients included 48 patients with H3 K27-altered tumor (aged 2-48 years) and 12 patients with wild-type tumor (aged 3-53 years). Visual assessment showed that the foci of markedly increased APTw signal intensity were more common in the H3 K27-altered group than in wild-type group (60% vs. 16%, P = 0.007). Histogram parameters of APTw signal intensity in the H3 K27-altered group were significantly higher than those in the wild-type group (median, 2.74% vs. 2.22%, P = 0.02). The maximum (area under the receiver operating characteristic curve [AUC] = 0.72, P = 0.01) showed the highest diagnostic performance among histogram analysis. A combination of age, median and maximum APTw signal intensity could predict H3 K27 alteration with a sensitivity of 81%, specificity of 75% and AUC of 0.80.

CONCLUSION

APTw imaging may serve as an imaging biomarker for H3 K27 alteration of pedBSGs.

Biogenic Imaging Contrast Agents

Imaging contrast agents are widely investigated in preclinical and clinical studies, among which biogenic imaging contrast agents (BICAs) are developing rapidly and playing an increasingly important role in biomedical research ranging from subcellular level to individual level. The unique properties of BICAs, including expression by cells as reporters and specific genetic modification, facilitate various in vitro and in vivo studies, such as quantification of gene expression, observation of protein interactions, visualization of cellular proliferation, monitoring of metabolism, and detection of dysfunctions. Furthermore, in human body, BICAs are remarkably helpful for disease diagnosis when the dysregulation of these agents occurs and can be detected through imaging techniques. There are various BICAs matched with a set of imaging techniques, including fluorescent proteins for fluorescence imaging, gas vesicles for ultrasound imaging, and ferritin for magnetic resonance imaging. In addition, bimodal and multimodal imaging can be realized through combining the functions of different BICAs, which helps overcome the limitations of monomodal imaging. In this review, the focus is on the properties, mechanisms, applications, and future directions of BICAs.

Prediction of Recurrent Cervical Cancer in 2-Year Follow-Up After Treatment Based on Quantitative and Qualitative Magnetic Resonance Imaging Parameters: A Preliminary Study

PURPOSE

This study investigated predictors of cervical cancer (CC) recurrence from native T1 mapping, conventional imaging, and clinicopathologic metrics.

PATIENTS AND METHODS

In total, 144 patients with histopathologically confirmed CC (90 with and 54 without surgical treatment) were enrolled in this prospective study. Native T1 relaxation time, conventional imaging, and clinicopathologic characteristics were acquired. The association of quantitative and qualitative parameters with post-treatment tumor recurrence was assessed using univariate and multivariate Cox proportional hazard regression analyses. Independent risk factors were combined into a model and individual prognostic index equation for predicting recurrence risk. The receiver operating characteristic (ROC) curve determined the optimal cutoff point.

RESULTS

In total, 12 of 90 (13.3%) surgically treated patients experienced tumor recurrence. Native T1 values (X1) [hazard ratio (HR) 1.008; 95% confidence interval (CI) 1.001-1.016], maximum tumor diameter (X2) (HR 1.065; 95% CI 1.020-1.113), and parametrial invasion (X3) (HR 3.930; 95% CI 1.013-15.251) were independent tumor recurrence risk factors. The individual prognostic index (PI) of the established recurrence risk model was PI = 0.008X1 + 0.063X2 + 1.369X3. The area under the ROC curve (AUC) of the Cox regression model was 0.923. A total of 20 of 54 (37.0%) non-surgical patients experienced tumor recurrence. Native T1 values (X1) (HR 1.012; 95% CI 1.007-1.016) and lymph node metastasis (X2) (HR 4.064; 95% CI 1.378-11.990) were independent tumor recurrence risk factors. The corresponding PI was calculated as follows: PI = 0.011X1 + 1.402X2; the Cox regression model AUC was 0.921.

CONCLUSIONS

Native T1 values combined with conventional imaging and clinicopathologic variables could facilitate the pretreatment prediction of CC recurrence.

Risk factors for the recurrence of cervical cancer using MR-based T1 mapping: A pilot study

Objectives

This study aimed to identify risk factors for recurrence in patients with cervical cancer (CC) through quantitative T1 mapping.

Methods

A cohort of 107 patients histopathologically diagnosed with CC at our institution between May 2018 and April 2021 was categorized into surgical and non-surgical groups. Patients in each group were further divided into recurrence and non-recurrence subgroups depending on whether they showed recurrence or metastasis within 3 years of treatment. The longitudinal relaxation time (native T1) and apparent diffusion coefficient (ADC) value of the tumor were calculated. The differences between native T1 and ADC values of the recurrence and non-recurrence subgroups were analyzed, and receiver operating characteristic (ROC) curves were drawn for parameters with statistical differences. Logistic regression was performed for analysis of significant factors affecting CC recurrence. Recurrence-free survival rates were estimated by Kaplan–Meier analysis and compared using the log-rank test.

Results

Thirteen and 10 patients in the surgical and non-surgical groups, respectively, showed recurrence after treatment. There were significant differences in native T1 values between the recurrence and non-recurrence subgroups in the surgical and non-surgical groups (P&lt;0.05); however, there was no difference in ADC values (P&gt;0.05). The areas under the ROC curve of native T1 values for discriminating recurrence of CC after surgical and non-surgical treatment were 0.742 and 0.780, respectively. Logistic regression analysis indicated that native T1 values were risk factors for tumor recurrence in the surgical and non-surgical groups (P=0.004 and 0.040, respectively). Compared with cut-offs, recurrence-free survival curves of patients with higher native T1 values of the two groups were significantly different from those with lower ones (P=0.000 and 0.016, respectively).

Conclusion

Quantitative T1 mapping could help identify CC patients with a high risk of recurrence, supplementing information on tumor prognosis other than clinicopathological features and providing the basis for individualized treatment and follow-up schemes.

Correlation Between Apparent Diffusion Coefficient and the Ki-67 Proliferation Index in Grading Pediatric Glioma

OBJECTIVE

This study aimed to investigate the correlation between apparent diffusion coefficient (ADC) and the Ki-67 proliferation index with the pathologic grades of pediatric glioma and to compare their diagnostic performance in differentiating grades of pediatric glioma.

PATIENTS AND METHODS

Magnetic resonance imaging examinations and histopathologies of 121 surgically treated pediatric gliomas (87 low-grade gliomas [LGGs; grades 1 and 2] and 34 high-grade gliomas [HGGs; grades 3 and 4]) were retrospectively reviewed. The mean tumor ADC (ADCmean), minimum tumor ADC (ADCmin), tumor/normal brain ADC ratio (ADC ratio), and value of the Ki-67 proliferation index of LGGs and HGGs were compared. Correlation coefficients were calculated for ADC parameters and Ki-67 values. The receiver operating characteristic curve was used to determine the diagnostic value of ADCmean, ADCmin, ADC ratio, and Ki-67 proliferation index for differentiating LGGs and HGGs.

RESULTS

The ADC values were significantly negatively correlated with glioma grade, and the Ki-67 proliferation index had a significant positive correlation with glioma grade. A significant negative correlation was observed between ADCmean and Ki-67 proliferation index, between ADCmin and Ki-67 proliferation index, and between ADC ratio and Ki-67 proliferation index. The receiver operating characteristic analysis demonstrated moderate to good accuracy for ADCmean in discriminating LGGs from HGGs (area under the curve [AUC], 0.875; sensitivity, 79.3%; specificity, 82.4%; accuracy, 80.2%; positive predictive value [PPV], 92.0%; and negative predictive value [NPV], 60.9% [cutoff value, 1.187] [×10-3 mm2/s]). Minimum tumor ADC showed very good to excellent accuracy with AUC of 0.946, sensitivity of 86.2%, specificity of 94.1%, accuracy of 88.4%, PPV of 97.4%, and NPV of 72.7% (cutoff value, 0.970) (×10-3 mm2/s). The ADC ratio showed moderate to good accuracy with AUC of 0.854, sensitivity of 72.4%, specificity of 88.2%, accuracy of 76.9%, PPV of 94.0%, and NPV of 55.6% (cutoff value, 1.426). For the parameter of the Ki-67 proliferation index, in discriminating LGGs from HGGs, very good to excellent diagnostic accuracy was observed (AUC, 0.962; sensitivity, 94.1%; specificity, 89.7%; accuracy, 90.9%; PPV, 97.5%; and NPV, 78.0% [cutoff value, 7]).

CONCLUSIONS

Apparent diffusion coefficient parameters and the Ki-67 proliferation index were significantly correlated with histological grade in pediatric gliomas. Apparent diffusion coefficient was closely correlated with the proliferative potential of pediatric gliomas. In addition, ADCmin showed superior performance compared with ADCmean and ADC ratio in differentiating pediatric glioma grade, with a close diagnostic efficacy to the Ki-67 proliferation index.

Comparison and combination of amide proton transfer magnetic resonance imaging and the apparent diffusion coefficient in differentiating the grades of prostate cancer

Background

More effective risk stratification of prostate cancer (PCa) than that possible with current methods can reduce undertreatment and guard against overtreatment. The aim of this study is to validate the differences and combined effects of amide proton transfer (APT) imaging and apparent diffusion coefficient (ADC) in discriminating the PCa grade group (GG) ≤2 from GG ≥3 PCa.

Methods

This is an ongoing prospective study conducted in the radiology department of Nanxishan Hospital of Guangxi Zhuang Autonomous Region. Patients pathologically diagnosed with PCa were enrolled consecutively according to the eligibility criteria. A total of 180 patients (age range, 42-92 years) were included in this study. Using histopathology as the reference standard, we placed 71 cases in GG ≤2 (mean age 67.03±8.696 years) and 109 cases in GG ≥3 (age 69.60±9.638 years). Magnetic resonance imaging (MRI) parameters, including APT and ADC values, were analyzed using an independent samples t-test and binary logistic regression analysis stratified with GG. Receiver operating characteristic curve was used to analyze the diagnostic performance for different parameters distinguishing GG ≤2 and GG ≥3.

Results

APT [odds ratio (OR) for the transitional zone (TZ) PCa: 3.20, 95% CI: 1.14-8.98, P=0.02; OR for the peripheral zone (PZ) PCa: 86.32, 95% CI: 13.24-562.88, P=0.003] and ADC values (OR for TZ PCa: 89.79; 95% CI: 2.85-2,827.99, P=0.01; OR for PZ PCa: 39.92; 95% CI: 3.22-494.18, P=0.004) were independent predictors that differentiated the GG of patients. The sensitivity and specificity of the APT values were 61.1% and 81.0%, respectively, while the sensitivity and specificity of the ADC values were 83.3% and 61.9%, respectively. The optimal cutoff value of APT was 3.35% and which of ADC was 1.25×10^-3 mm²/s in TZ origin PCa. At the optimal cutoff values of 3.31% (APT) and 0.79×10^-3 mm²/s (ADC) in PZ PCa, the sensitivity and specificity of the APT values were 74.0% and 83.6%, respectively, while the sensitivity and specificity of the ADC values were 94.0% and 53.4%, respectively. The area under the curve of the combination of APT and ADC was significantly higher than either of APT or ADC alone in Delong test (TZ: P=0.002 and P=0.020; PZ: P=0.033 and P<0.001).

Conclusions

APT and ADC have complementary effects on the sensitivity and specificity for identifying different PCa GGs. A combination model of APT and ADC could improve the diagnostic efficacy of PCa differentiation.

Whole-tumor amide proton transfer-weighted imaging histogram analysis to predict pathological extramural venous invasion in rectal adenocarcinoma: a preliminary study

OBJECTIVES

To evaluate amide proton transfer-weighted (APTw)-derived whole-tumor histogram analysis parameters in predicting pathological extramural venous invasion (pEMVI) positive status of rectal adenocarcinoma (RA).

METHODS

Preoperative MR including APTw imaging of 125 patients with RA (mean 61.4 ± 11.6 years) were retrospectively analyzed. Two radiologists reviewed each case's EMVI status based on the MR-based modified 5-point scale system with conventional MR images. The APTw histogram parameters of primary tumors were obtained automatically using whole-tumor volume histogram analysis. The independent risk factors markedly correlated with pEMVI-positive status were assessed using univariate and multivariate logistic regression analyses. Diagnosis performance was assessed by receiver operating characteristic curve (ROC) analysis. The AUCs were compared using the Delong method.

RESULTS

Univariate analysis demonstrated that MR-tumor (T) stage, MR-lymph node (N) stage, APTw-10%, APTw-90%, interquartile range, APTw-minimum, APTw-maximum, APTw-mean, APTw-median, entropy, kurtosis, mean absolute deviation (MAD), and robust MAD were significantly related to pEMVI-positive status (all p < 0.05). Multivariate analysis demonstrated that MR-T stage (OR = 4.864, p = 0.018), MR-N stage (OR = 4.967, p = 0.029), interquartile range (OR = 0.892, p = 0.037), APT-minimum (OR = 1.046, p = 0.031), entropy (OR = 11.604, p = 0.006), and kurtosis (OR = 1.505, p = 0.007) were the independent risk factors enabling prediction of pEMVI-positive status. The AUCs for diagnostic ability of conventional MRI assessment, the APTw histogram model, and the combined model (including APTw histogram and clinical variables) were 0.785, 0.853, and 0.918, respectively. The combined model outperformed the APTw histogram model (p = 0.013) and the conventional MRI assessment (p = 0.006).

CONCLUSIONS

Whole-tumor histogram analysis of APTw images combined with clinical factors showed better diagnosis efficiency in predicting EMVI involvement in RA.

KEY POINTS

• Rectal adenocarcinomas with pEMVI-positive status are typically associated with higher APTw-SI values. • APTw-minimum, interquartile range, entropy, kurtosis, MR-T stage, and MR-N stage are the independent risk factors for EMVI involvement. • The best prediction for EMVI involvement was obtained with a combined model of APTw histogram and clinical variables (area under the curve, 0.918).

Correlation of Diffusion Tensor Imaging Parameters with the Pathological Grade of Brain Glioma and Expression of Vascular Endothelial Growth Factor and Ki-67

Background: Most brain gliomas are high-grade and likely to spread locally. Consequently, these patients commonly have a poor prognosis. Accurate identification of the malignancy grade of brain glioma before treatment is of great clinical significance. Objectives: This study aimed to explore the correlation of diffusion tensor imaging (DTI) parameters, fractional anisotropy (FA), and apparent diffusion coefficient (ADC) with the pathological grade of brain glioma and expression of vascular endothelial growth factor (VEGF) and Ki-67. Patients and Methods: A total of 116 patients were selected for this study from January 2018 to December 2019. All the participants underwent magnetic resonance imaging (MRI) and DTI before surgery, and the FA and ADC values were measured for the regions of interest. Surgically resected tumor specimens were collected for immunohistochemical assay. Finally, the FA and ADC values and positive expression rates of VEGF and Ki-67 were compared. Results: A significantly higher FA, besides the positive expression of VEGF and Ki-67, was reported in the high-grade group, whereas a lower ADC was found in this group compared to the low-grade group (P < 0.05). Areas of normal white matter and peritumoral edema had higher FA values, whereas lower ADCs were measured in these areas compared to the cerebrospinal fluid (P < 0.05). The FA of tumor parenchymal area was positively correlated with the World Health Organization (WHO) WHO class of tumors (r = 0.588, P = 0.028), and the expression of VEGF and Ki-67 was positively correlated with the WHO grade (r = 0.843, P = 0.002 and r = 0.743, P = 0.006, respectively). The FA of tumor parenchymal area was positively correlated with the expression of VEGF and Ki-67 (r = 0.654, P = 0.008 and r = 0.567, P = 0.012, respectively). However, the ADC of tumor parenchymal area was not significantly correlated with the WHO grade, VEGF expression, or Ki-67 expression (r = 0.143, P = 0.156, r = 0.232, P = 0.116, and r = 0.054, P = 0.179, respectively). Conclusion: The FA value, as a DTI parameter, is valuable for assessing the malignancy grade of tumor cells and can provide a proper reference for formulating treatment regimens for brain gliomas.

Smart Visualization of Medical Images as a Tool in the Function of Education in Neuroradiology

The smart visualization of medical images (SVMI) model is based on multi-detector computed tomography (MDCT) data sets and can provide a clearer view of changes in the brain, such as tumors (expansive changes), bleeding, and ischemia on native imaging (i.e., a non-contrast MDCT scan). The new SVMI method provides a more precise representation of the brain image by hiding pixels that are not carrying information and rescaling and coloring the range of pixels essential for detecting and visualizing the disease. In addition, SVMI can be used to avoid the additional exposure of patients to ionizing radiation, which can lead to the occurrence of allergic reactions due to the contrast media administration. Results of the SVMI model were compared with the final diagnosis of the disease after additional diagnostics and confirmation by neuroradiologists, who are highly trained physicians with many years of experience. The application of the realized and presented SVMI model can optimize the engagement of material, medical, and human resources and has the potential for general application in medical training, education, and clinical research.

Differentiation of Intracerebral Tumor Entities with Quantitative Contrast Attenuation and Iodine Mapping in Dual-Layer Computed Tomography

Purpose: To investigate if quantitative contrast enhancement and iodine mapping of common brain tumor (BT) entities may correctly differentiate between tumor etiologies in standardized stereotactic CT protocols. Material and Methods: A retrospective monocentric study of 139 consecutive standardized dual-layer dual-energy CT (dlDECT) scans conducted prior to the stereotactic needle biopsy of untreated primary brain tumor lesions. Attenuation of contrast-enhancing BT was derived from polyenergetic images as well as spectral iodine density maps (IDM) and their contrast-to-noise-ratios (CNR) were determined using ROI measures in contrast-enhancing BT and healthy contralateral white matter. The measures were correlated to histopathology regarding tumor entity, isocitrate dehydrogenase (IDH) and MGMT mutation status. Results: The cohort included 52 female and 76 male patients, mean age of 59.4 (±17.1) years. Brain lymphomas showed the highest attenuation (IDM CNR 3.28 ± 1,23), significantly higher than glioblastoma (2.37 ± 1.55, p < 0.005) and metastases (1.95 ± 1.14, p < 0.02), while the differences between glioblastomas and metastases were not significant. These strongly enhancing lesions differed from oligodendroglioma and astrocytoma (Grade II and III) that showed IDM CNR in the range of 1.22−1.27 (±0.45−0.82). Conventional attenuation measurements in DLCT data performed equally or slightly superior to iodine density measurements. Conclusion: Quantitative attenuation and iodine density measurements of contrast-enhancing brain tumors are feasible imaging biomarkers for the discrimination of cerebral tumor lesions but not specifically for single tumor entities. CNR based on simple HU measurements performed equally or slightly superior to iodine quantification.

Application of 7T MRS to High-Grade Gliomas

MRS, including single-voxel spectroscopy and MR spectroscopic imaging, captures metabolites in high-grade gliomas. Emerging evidence indicates that 7T MRS may be more sensitive to aberrant metabolic activity than lower-field strength MRS. However, the literature on the use of 7T MRS to visualize high-grade gliomas has not been summarized. We aimed to identify metabolic information provided by 7T MRS, optimal spectroscopic sequences, and areas for improvement in and new applications for 7T MRS. Literature was found on PubMed using "high-grade glioma," "malignant glioma," "glioblastoma," "anaplastic astrocytoma," "7T," "MR spectroscopy," and "MR spectroscopic imaging." 7T MRS offers higher SNR, modestly improved spatial resolution, and better resolution of overlapping resonances. 7T MRS also yields reduced Cramér-Rao lower bound values. These features help to quantify D-2-hydroxyglutarate in isocitrate dehydrogenase 1 and 2 gliomas and to isolate variable glutamate, increased glutamine, and increased glycine with higher sensitivity and specificity. 7T MRS may better characterize tumor infiltration and treatment effect in high-grade gliomas, though further study is necessary. 7T MRS will benefit from increased sample size; reductions in field inhomogeneity, specific absorption rate, and acquisition time; and advanced editing techniques. These findings suggest that 7T MRS may advance understanding of high-grade glioma metabolism, with reduced Cramér-Rao lower bound values and better measurement of smaller metabolite signals. Nevertheless, 7T is not widely used clinically, and technical improvements are necessary. 7T MRS isolates metabolites that may be valuable therapeutic targets in high-grade gliomas, potentially resulting in wider ranging neuro-oncologic applications.

Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors

Amide proton transfer-weighted (APTw) MR imaging shows promise as a biomarker of brain tumor status. Currently used APTw MRI pulse sequences and protocols vary substantially among different institutes, and there are no agreed-on standards in the imaging community. Therefore, the results acquired from different research centers are difficult to compare, which hampers uniform clinical application and interpretation. This paper reviews current clinical APTw imaging approaches and provides a rationale for optimized APTw brain tumor imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods. We expect that these consensus recommendations will become the first broadly accepted guidelines for APTw imaging of brain tumors on 3 T MRI systems from different vendors. This will allow more medical centers to use the same or comparable APTw MRI techniques for the detection, characterization, and monitoring of brain tumors, enabling multi-center trials in larger patient cohorts and, ultimately, routine clinical use.

Molecular Imaging of Brain Tumors and Drug Delivery Using CEST MRI: Promises and Challenges

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) detects molecules in their natural forms in a sensitive and non-invasive manner. This makes it a robust approach to assess brain tumors and related molecular alterations using endogenous molecules, such as proteins/peptides, and drugs approved for clinical use. In this review, we will discuss the promises of CEST MRI in the identification of tumors, tumor grading, detecting molecular alterations related to isocitrate dehydrogenase (IDH) and O-6-methylguanine-DNA methyltransferase (MGMT), assessment of treatment effects, and using multiple contrasts of CEST to develop theranostic approaches for cancer treatments. Promising applications include (i) using the CEST contrast of amide protons of proteins/peptides to detect brain tumors, such as glioblastoma multiforme (GBM) and low-grade gliomas; (ii) using multiple CEST contrasts for tumor stratification, and (iii) evaluation of the efficacy of drug delivery without the need of metallic or radioactive labels. These promising applications have raised enthusiasm, however, the use of CEST MRI is not trivial. CEST contrast depends on the pulse sequences, saturation parameters, methods used to analyze the CEST spectrum (i.e., Z-spectrum), and, importantly, how to interpret changes in CEST contrast and related molecular alterations in the brain. Emerging pulse sequence designs and data analysis approaches, including those assisted with deep learning, have enhanced the capability of CEST MRI in detecting molecules in brain tumors. CEST has become a specific marker for tumor grading and has the potential for prognosis and theranostics in brain tumors. With increasing understanding of the technical aspects and associated molecular alterations detected by CEST MRI, this young field is expected to have wide clinical applications in the near future.

CEST MRI provides amide/amine surrogate biomarkers for treatment-naïve glioma sub-typing

PURPOSE

Accurate glioma classification affects patient management and is challenging on non- or low-enhancing gliomas. This study investigated the clinical value of different chemical exchange saturation transfer (CEST) metrics for glioma classification and assessed the diagnostic effect of the presence of abundant fluid in glioma subpopulations.

METHODS

Forty-five treatment-naïve glioma patients with known isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status received CEST MRI (B_1rms = 2μT, T_sat = 3.5 s) at 3 T. Magnetization transfer ratio asymmetry and CEST metrics (amides: offset range 3-4 ppm, amines: 1.5-2.5 ppm, amide/amine ratio) were calculated with two models: 'asymmetry-based' (AB) and 'fluid-suppressed' (FS). The presence of T2/FLAIR mismatch was noted.

RESULTS

IDH-wild type had higher amide/amine ratio than IDH-mutant_1p/19q^codel (p < 0.022). Amide/amine ratio and amine levels differentiated IDH-wild type from IDH-mutant (p < 0.0045) and from IDH-mutant_1p/19q^ret (p < 0.021). IDH-mutant_1p/19q^ret had higher amides and amines than IDH-mutant_1p/19q^codel (p < 0.035). IDH-mutant_1p/19q^ret with AB/FS mismatch had higher amines than IDH-mutant_1p/19q^ret without AB/FS mismatch ( < 0.016). In IDH-mutant_1p/19q^ret, the presence of AB/FS mismatch was closely related to the presence of T2/FLAIR mismatch (p = 0.014).

CONCLUSIONS

CEST-derived biomarkers for amides, amines, and their ratio can help with histomolecular staging in gliomas without intense contrast enhancement. T2/FLAIR mismatch is reflected in the presence of AB/FS CEST mismatch. The AB/FS CEST mismatch identifies glioma subgroups that may have prognostic and clinical relevance.

Contribution of blood to nuclear Overhauser effect at -1.6 ppm

PURPOSE

A relayed nuclear Overhauser enhancement (rNOE) saturation transfer effect at around -1.6 ppm from water, termed NOE(-1.6), was previously reported in rat and human brain, and some publications suggest that it may be related to blood. Here, we studied whether the NOE(-1.6) arises from blood through in vivo and ex vivo experiments.

METHODS

To evaluate the contribution from in vivo blood to NOE(-1.6), intravascular signals in rat brain were suppressed by two approaches: (1) signal acquisition with a diffusion-weighting of b = 400 s/mm2 ; (2) intravascular injection of 5 mg/kg monocrystalline iron oxide nanoparticle (MION). Ex vivo blood sample was also prepared. The signals were acquired using a chemical exchange saturation transfer (CEST) pulse sequence. Multiple-pool Lorentzian fitting of CEST Z-spectra was performed to quantify the NOE(-1.6) signal.

RESULTS

There are no significant variations in the fitted in vivo NOE(-1.6) signals when measured with or without diffusion-weighting, but significant signal decease does occur after injection of MION. The NOE(-1.6) signal from ex vivo blood is weaker than that from in vivo tissues.

CONCLUSION

Considering the relatively small volume of blood in brain, the in vivo experiments with diffusion weighting and the ex vivo experiments both suggest that the NOE(-1.6) is not mainly from blood. The mechanism for the in vivo experiments with MION are less clear. MION not only suppresses MR signals from intravascular space, but changes the susceptibility in the perivascular space. This result suggests that although the NOE(-1.6) is not mainly from blood, it may be vasculature dependent.

Fast and equilibrium CEST imaging of brain tumor patients at 3T

Chemical exchange saturation transfer (CEST) MRI, versatile for detecting endogenous mobile proteins and tissue pH, has proved valuable in tumor imaging. However, CEST MRI scans are often performed under non-equilibrium conditions, which confound tissue characterization. This study proposed a quasi-steady-state (QUASS) CEST MRI algorithm to standardize fast and accurate tumor imaging at 3 T. The CEST signal evolution was modeled by longitudinal relaxation rate during relaxation delay (Td) and spinlock relaxation during RF saturation time (Ts), from which the QUASS CEST effect is derived. Numerical simulation and human MR imaging experiments (7 healthy volunteers and 19 tumor patients) were conducted at 3 T to compare the CEST measurements obtained under two representative experimental conditions. In addition, amide proton transfer (APT), combined magnetization transfer (MT) and nuclear overhauser enhancement (NOE) effects, and direct water saturation were isolated using a 3-pool Lorentzian fitting in white matter and gray matter of healthy volunteers and for patients in the contralateral normal-appearing white matter and tumor regions. Finally, the student's t-test was performed between conventional and QUASS CEST measurements. The routine APT and combined MT & NOE measures significantly varied with Ts and Td (P < .001) and were significantly smaller than the corresponding QUASS indices (P < .001). In contrast, the results from the QUASS reconstruction showed little dependence on the scan protocol (P > .05), indicating the accuracy and robustness of QUASS CEST MRI for tumor imaging. To summarize, the QUASS CEST reconstruction algorithm enables fast and accurate tumor CEST imaging at 3 T, promising to expedite and standardize clinical CEST MRI.

Imaging Advances for Central Nervous System Tumors

This article reviews recent advances in the use of standard and advanced imaging techniques for diagnosis and treatment of central nervous system (CNS) tumors, including glioma and brain metastasis. Following the recent transition from a histology-based approach in classifying CNS tumors to one that integrates histology with the molecular information of tumor, the approaches for imaging CNS tumors have also been adapted to this new framework. Some challenges related to the diagnosis and treatment of CNS tumors, such as differentiating tumor from treatment-related imaging changes, require further progress to implement advanced imaging for clinical use.

Metabolic Magnetic Resonance Imaging in Neuroimaging: Magnetic Resonance Spectroscopy, Sodium Magnetic Resonance Imaging and Chemical Exchange Saturation Transfer

Magnetic resonance (MR) is a powerful and versatile technique that offers much more beyond conventional anatomic imaging and has the potential of probing in vivo metabolism. Although MR spectroscopy (MRS) predates clinical MR imaging (MRI), its clinical application has been limited by technical and practical challenges. Other MR techniques actively being developed for in vivo metabolic imaging include sodium concentration imaging and chemical exchange saturation transfer. This article will review some of the practical aspects of MRS in neuroimaging, introduce sodium MRI and chemical exchange saturation transfer MRI, and highlight some of their emerging clinical applications.

Multi-parametric Z-spectral MRI may have a good performance for glioma stratification in clinical patients

OBJECTIVES

To comprehensively and noninvasively risk-stratify glioma grade, isocitrate dehydrogenase (IDH) genotype, and 1p/19q codeletion status using multi-contrast Z-spectral magnetic resonance imaging (MRI).

METHODS

One hundred and thirteen patients with glioma were retrospectively included. Multiple contrasts contributing to Z-spectra, including direct saturation of water (DSW), semi-solid magnetization transfer contrast (MTC), amide proton transfer (APT) effect, aliphatic nuclear Overhauser effect, and the 2-ppm chemical exchange saturation transfer peak (CEST@2ppm), were fitted with five individual Lorentzian functions. Z-spectral contrasts were compared according to the three most important risk stratifications: tumor grade, IDH genotype, and 1p/19q codeletion status. We further investigated the differentiation of 1p/19q codeletion status within IDH mutant gliomas. The stratification performance of individual Z-spectral contrasts and their combination was quantified using receiver operating characteristic (ROC) analyses.

RESULTS

DSW was significantly different within grade, IDH genotypes, and 1p/19q codeletion status. APT was significantly different with grade and IDH mutation, but not with 1p/19q subtypes. CEST@2ppm was only significantly different with 1p/19q codeletion subtypes. DSW and CEST@2ppm were the two Z-spectral contrasts able to differentiate 1p/19q codeletion subtypes within IDH mutant gliomas. For differentiating glioma grades using ROC analyses, DSW achieved the largest AUC. For differentiating IDH genotypes, DSW and APT achieved comparable AUCs. DSW was the best metric for differentiating 1p/19q codeletion status within all patients and within the IDH mutant patients. Combining all Z-spectral contrasts improved sensitivity and specificity for all risk stratifications.

CONCLUSIONS

Multi-parametric Z-spectral MRI serves as a useful, comprehensive, and noninvasive imaging technique for glioma stratification in clinical patients.

KEY POINTS

• Multiple contrasts contributing to Z-spectra were separately fitted with Lorentzian functions. • Z-spectral contrasts were compared within the three most important and common tumor risk stratifications for gliomas: tumor grade, IDH genotype, and 1p/19q codeletion status. • The stratification performance of individual Z-spectral contrasts and their combination was quantified using receiver operating characteristic analyses, which found Z-spectral MRI to be a useful and comprehensive imaging biomarker for glioma stratification.

Predicting cancer malignancy and proliferation in glioma patients: intra-subject inter-metabolite correlation analyses using MRI and MRSI contrast scans

Background

The non-invasive characterization of glioma metabolites would greatly assist the management of glioma patients in the clinical setting. This study investigated the applicability of intra-subject inter-metabolite correlation analyses for differentiating glioma malignancy and proliferation.

Methods

A total of 17 negative controls (NCs), 39 low-grade gliomas (LGGs) patients, and 25 high-grade gliomas (HGGs) subjects were included in this retrospective study. Amide proton transfer (APT) and magnetization transfer contrast (MTC) imaging contrasts, as well as total choline/total creatine (tCho/tCr) and total N-acetylaspartate/total creatine (tNAA/tCr) ratios quantified from magnetic resonance spectroscopic imaging (MRSI) were co-registered voxel-wise and used to produce three intra-subject inter-metabolite correlation coefficients (IMCCs), namely, R_{APT vs . MTC}, R_{APT vs . tCho/tCr}, and R_{MTC vs . tNAA/tCr}. The correlation between the IMCCs and tumor grade and Ki-67 labeling index (LI) for tumor proliferation were explored. The differences in the IMCCs between the three groups were compared with one-way analysis of variance (ANOVA). Finally, regression analysis was used to build a combined model with multiple IMCCs to improve the diagnostic performance for tumor grades based on receiver operator characteristic curves.

Results

Compared with the NCs, gliomas showed stronger inter-metabolic correlations. R_{APT vs . MTC} was significantly different among the three groups (NC vs. LGGs vs. HGGs: -0.18±0.38 vs. -0.40±0.34 vs. -0.70±0.29, P<0.0001). No significant differences were detected in R_{MTC vs . tNAA/tCr} among the three groups. R_{APT vs . MTC} and R_{APT vs . tCho/tCr} correlated significantly with tumor grade (R=-0.41, P=0.001 and R=0.448, P<0.001, respectively). However, only R_{APT vs . MTC} was mildly correlated with Ki-67 (R=-0.33, P=0.02). R_{APT vs . MTC} and R_{APT vs . tCho/tCr} achieved areas under the curve (AUCs) of 0.754 and 0.71, respectively, for differentiating NCs from gliomas; and 0.77 and 0.78, respectively, for differentiating LGGs from HGGs. The combined multi-IMCCs model improved the correlation with the Ki-67 LI (R=0.46, P=0.0008) and the tumor-grade stratification with AUC increased to 0.85 (sensitivity: 80.0%, specificity: 79.5%).

Conclusions

This study demonstrated that glioma patients showed stronger inter-metabolite correlations than control subjects, and the IMCCs were significantly correlated with glioma grade and proliferation. The multi-IMCCs combined model further improved the performance of clinical diagnosis.

Galectin-9 and PSMB8 overexpression predict unfavorable prognosis in patients with AML

Acute myeloid leukemia (AML) is a deadly heterogeneous hematologic malignancy. Despite the well-characterized genetic characteristics and new promising targeted therapies for AML, the clinical outcome remains suboptimal. Galectin-9 (Gal-9) is a good potential target due to its immunosuppressive capacity in inflammatory processes. In our study, we firstly performed a wide range of integrated bioinformatical approach to assess the importance of Gal-9 by analyzing the expression, potential function and prognostic impact in AML. The results indicated that Gal-9 is overexpressed in AML cells, especially when relapse after hematopoietic stem cell transplantation (HSCT) and predicts poor prognosis. Co-expression analysis showed Gal-9 has a strong positive correlation with proteasome subunit beta type-8 (PSMB8), which was also highly expressed in AML with poor prognosis, implying a synergy in cell survival, cell signaling and the development of AML. In summary, we have confirmed the overexpression of Gal-9 and its partner PSMB8 in AML and validated their importance as prognostic factors. We propose that Gal-9 and PSMB8 could be a promising molecular target for treatment of AML and may provide more combined treatment options, especially in patients with relapse after HSCT.

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

Amide proton transfer imaging for differentiation of tuberculomas from high-grade gliomas: Preliminary experience

PURPOSE

Tuberculomas can occasionally masquerade as high-grade gliomas (HGG). Evidence from magnetisation transfer (MT) imaging suggests that there is lower protein content in the tuberculoma microenvironment. Building on the principles of chemical exchange saturation transfer and MT, amide proton transfer (APT) imaging generates tissue contrast as a function of the mobile amide protons in tissue's native peptides and intracellular proteins. This study aimed to further the understanding of tuberculomas using APT and to compare it with HGG.

METHOD

Twenty-two patients (n = 8 tuberculoma; n = 14 HGG) were included in the study. APT was a 3D turbo spin-echo Dixon sequence with inbuilt B₀ correction. A two-second, 2 μT saturation pulse alternating over transmit channels was applied at ±3.5 ppm around water resonance. The APT-weighted image (APTw) was computed as the MT ratio asymmetry (MTR_asym) at 3.5 ppm. Mean MTR_asym values in regions of interest (areas = 9 mm²; positioned in component with homogeneous enhancement/least apparent diffusion coefficient) were used for the analysis.

RESULTS

MTR_asym values of tuberculomas (n = 14; 8 cases) ranged from 1.34% to 3.11% (M = 2.32 ± 0.50). HGG (n = 17;14 cases) showed MTR_asym ranging from 2.40% to 5.70% (M = 4.32 ± 0.84). The inter-group difference in MTR_asym was statistically significant (p < 0.001). APTw images in tuberculomas were notable for high MTR_asym values in the perilesional oedematous-appearing parenchyma (compared to contralateral white matter; p < 0.001).

CONCLUSION

Tuberculomas demonstrate lower MTR_asym ratios compared to HGG, reflective of a relative paucity of mobile amide protons in the ambient microenvironment. Elevated MTR_asym values in perilesional parenchyma in tuberculomas are a unique observation that may be a clue to the inflammatory milieu.

Dual-layer detector spectral CT-a new supplementary method for preoperative evaluation of glioma

PURPOSE

To investigate the value of the iodine concentration (IC) measured by dual-layer detector spectral CT (DLDSCT) in evaluating the factors related to the treatment scheme and survival prognosis of patients with glioma.

METHODS

From 2018 to 2019, we prospectively collected the data of 99 patients with glioma. The degree of CT enhancement and the IC of low grade gliomas (LGGs, II), high grade gliomas (HGGs, III and IV), grade II and III gliomas, were compared. The predictive performance of the degree of CT enhancement and IC was examined via receiver operating characteristic (ROC) analysis. The correlations between IC and Ki-67 labeling index, isocitrate dehydrogenase (IDH) mutation, chromosome 1p/19q deletion status of the tumor were examined.

RESULTS

Both IC and the degree of CT enhancement of patients with HGG were significantly higher than those of patients with LGG (p < 0.001; χ² =41.707, p <  0.001); IC had large area under the ROC curve for diagnostic HGG (0.931; 95 % CI: 0.882-0.979; p <  0.001). The IC in the grade III gliomas was significantly higher than that in grade II gliomas (p < 0.001); IC had a large area under the ROC curve for diagnostic grade III gliomas (0.865; 95 % CI: 0.779-0.952; p <  0.001). There was a significant positive correlation between IC and Ki-67 LI (r = 0.679; p < 0.001).

CONCLUSIONS

The DLDSCT technology can be used as a supplementary method to provide more information for preoperative grading of the gliomas and the prognosis assessment of the patients.

The role of APT imaging in gliomas grading: A systematic review and meta-analysis

PURPOSE

Gliomas are diagnosed and staged by conventional MRI. Although non-conventional sequences such as perfusion-weighted MRI may differentiate low-grade from high-grade gliomas, they are not reliable enough yet. The latter is of paramount importance for patient management. In this regard, we aim to evaluate the role of Amide Proton Transfer (APT) imaging in grading gliomas as a non-invasive tool to provide reliable differentiation across tumour grades.

METHODS

A systematic search of PubMed, Medline and Embase was conducted to identify relevant publications between 01/01/2008 and 15/09/2020. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess studies' quality. A random-effects model standardized mean difference meta-analysis was performed to assess APT's ability to differentiate low-grade gliomas (LGGs) from high-grade gliomas (HGGs), WHO 2-4 grades, wild-type from mutated isocitrate dehydrogenase (IDH) gliomas, methylated from unmethylated O6-methylguanine-DNA methyltransferase (MGMT) gliomas. Area under the curve (AUC) of the Receiver Operating Characteristic (ROC) meta-analysis was employed to assess the diagnostic performance of APT.

RESULTS

23 manuscripts met the inclusion criteria and reported the use of APT to differentiate glioma grades with histopathology as reference standard. APT-weighted signal intensity can differentiate LGGs from HGGs with an estimated size effect of (-1.61 standard deviations (SDs), p < 0.0001), grade 2 from grade 3 (-1.83 SDs, p = 0.005), grade 2 from grade 4 (-2.34 SDs, p < 0.0001) and IDH wild-type from IDH mutated (0.94 SDs, p = 0.003) gliomas. The combined AUC of 0.84 highlights the good diagnostic performance of APT-weighted imaging in differentiating LGGs from HGGs.

CONCLUSIONS

APT imaging is an exciting prospect in differentiating LGGs from HGGs and with potential to predict the histopathological grade. However, more studies are required to optimize and improve its reliability.

Comparative Analysis of Amide Proton Transfer MRI and Diffusion-Weighted Imaging in Assessing p53 and Ki-67 Expression of Rectal Adenocarcinoma

BACKGROUND

The evaluation of prognostic factors in rectal carcinoma patients has important clinical significance. P53 status and the Ki-67 index have served as prognostic factors in rectal carcinoma. Amide proton transfer (APT) imaging has shown great potential in tumor diagnosis. However, few studies reported the value of APT imaging in evaluating p53 and Ki-67 status of rectal carcinoma.

PURPOSE

To investigate the feasibility of amide proton transfer MRI in assessing p53 and Ki-67 expression of rectal adenocarcinoma, and compare it with conventional diffusion-weighted imaging (DWI).

STUDY TYPE

Retrospective.

POPULATION

Forty-three patients with rectal adenocarcinoma (age: 34-85 years).

FIELD STRENGTH/SEQUENCE

3T/APT imaging using a 3D turbo spin echo (TSE)-Dixon pulse sequence with chemical shift-selective fat suppression, 2D DWI, and 2D T₂ -weighted TSE.

ASSESSMENT

Mean tumor APT signal intensity (SI_mean ) and apparent diffusion coefficient (ADC_mean ) were measured. Traditional tumor pathological analysis included WHO grades, pT (pathologic tumor) stages, and pN (pathologic node) stages. Expression levels of p53 and Ki-67 were determined by immunohistochemical assay.

STATISTICAL TESTS

One-way analysis of variance (ANOVA); Student's t-test; Spearman's correlation coefficient; receiver operating characteristic (ROC) curve analysis.

RESULTS

High-grade tumors, more advanced stage tumors, and tumors with lymph node involvement had higher APT SI_mean values: high grade (n = 15) vs. low-grade (n = 28), P < 0.001; pT2 (n = 10) vs. pT3 (n = 20) vs. pT4 (N = 13), P = 0.021; pN0 (n = 24) vs. pN1-2 (n = 19), P = 0.019. ADC_mean differences were found in tumors with different pT stage: pT2 (n = 10) vs. pT3 (n = 20) vs. pT4 (N = 13), P = 0.013, but not in tumors with different histologic grade: high grade (n = 15) vs. low-grade (n = 28), P = 0.3536; or pN stage: pN0 (n = 24) vs. pN1-2 (n = 19), P = 0.624. Tumor with p53 positive status had higher APT SI_mean than tumor with negative p53 status (2.363 ± 0.457 vs. 2.0150 ± 0.3552, P = 0.014). There was no difference in ADC_mean with p53 status (1.058 ± 0.1163 10^-3  mm² /s vs. 1.055 ± 0.128 10^-3  mm² /s, P = 0.935). APT SI_mean and ADC_mean were significantly different in tumors with low and high Ki-67 status (1.7882 ± 0.11386 vs. 2.3975 ± 0.41586, P < 0.001; 1.1741 ± 0.093 10^-3  mm² /s vs. 1.0157 ± 0.10459 10^-3  mm² /s, P < 0.001, respectively). APT SI_mean exhibited a positive correlation with p53 labeling index and Ki-67 labeling index (r = 0.3741, P = 0.0135; r = 0.7048; P < 0.001, respectively). ADC_mean showed no correlation with p53 labeling index, but a negative correlation with Ki-67 labeling index (r = -0.5543, P < 0.0001). ROC curves demonstrated that APT SI_mean had significantly higher diagnostic ability for differentiation of high Ki-67 expression of rectal adenocarcinoma than ADC_mean (81.2% vs. 78.12%, 90.91% vs. 63.64; P < 0.001 vs. P = 0.017), while no difference was found in predicting p53 status (92.86% vs. 71.4%, 53.33% vs. 66.7%; P < 0.001 vs. P = 0.0471).

DATA CONCLUSION

APT SI_mean was related to p53 and Ki-67 expression levels in rectal adenocarcinoma. APT imaging may serve as a noninvasive biomarker for assessing genetic prognostic factors of rectal adenocarcinoma.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Comparing amide proton transfer imaging with dynamic susceptibility contrast-enhanced perfusion in predicting histological grades of gliomas: a meta-analysis

Background

As a subtype of chemical exchange saturation transfer imaging without contrast agent administration, amide proton transfer (APT) imaging has demonstrated the potential for differentiating the histologic grades of gliomas. Dynamic susceptibility contrast-enhanced perfusion, a perfusion-weighted imaging technique, is a well-established technique in grading gliomas.

Purpose

To compare the ability of amide proton transfer and dynamic susceptibility contrast-enhanced imaging for predicting the grades of gliomas.

Material and Methods

A comprehensive literature search was performed independently by two observers to identify articles about the diagnostic performance of amide proton transfer and dynamic susceptibility contrast-enhanced perfusion in predicting the grade of gliomas. Summary estimates of diagnostic accuracy were obtained by using a random-effects model.

Results

Of 179 studies identified, 23 studies were included the analysis. Eight studies evaluated amide proton transfer and 16 studies evaluated dynamic susceptibility contrast-enhanced perfusion with the parameter rCBV. The pooled sensitivities and specificities of each study’s best performing parameter were 88% (95% confidence interval [CI] 74–95) and 89% (95% CI 78–95) for amide proton transfer, and 95% (95% CI 87–98), 88% (95% CI 81–93) for perfusion-weighted imaging–dynamic susceptibility contrast-enhanced perfusion, respectively. The pooled sensitivities and specificities for grading gliomas using the two most commonly evaluated parameters, were 92% (95% CI 80–97) and 90% (95% CI 75–96) for APTmax, and 97% (95% CI 91–99) and 87% (95% CI 80–92) for rCBVmax, respectively.

Conclusion

Considering the similar performance of APT and dynamic susceptibility contrast-enhanced (DSC) in predicting glioma grade, the former method appears preferable since it needs no contrast agent.

Endogenous Chemical Exchange Saturation Transfer MRI for the Diagnosis and Therapy Response Assessment of Brain Tumors: A Systematic Review

Purpose

To generate a narrative synthesis of published data on the use of endogenous chemical exchange saturation transfer (CEST) MRI in brain tumors.

Materials and Methods

A systematic database search (PubMed, Ovid Embase, Cochrane Library) was used to collate eligible studies. Two researchers independently screened publications according to predefined exclusion and inclusion criteria, followed by comprehensive data extraction. All included studies were subjected to a bias risk assessment using the Quality Assessment of Diagnostic Accuracy Studies tool.

Results

The electronic database search identified 430 studies, of which 36 fulfilled the inclusion criteria. The final selection of included studies was categorized into five groups as follows: grading gliomas, 19 studies (area under the receiver operating characteristic curve [AUC], 0.500-1.000); predicting molecular subtypes of gliomas, five studies (AUC, 0.610-0.920); distinction of different brain tumor types, seven studies (AUC, 0.707-0.905); therapy response assessment, three studies (AUC not given); and differentiating recurrence from treatment-related changes, five studies (AUC, 0.880-0.980). A high bias risk was observed in a substantial proportion of studies.

Conclusion

Endogenous CEST MRI offers valuable, potentially unique information in brain tumors, but its diagnostic accuracy remains incompletely known. Further research is required to assess the method's role in support of molecular genetic diagnosis, to investigate its use in the posttreatment phase, and to compare techniques with a view to standardization.Keywords: Brain/Brain Stem, MR-Imaging, Neuro-OncologySupplemental material is available for this article.© RSNA, 2020.

Quantitative Assessment of Tumor Cell Proliferation in Brain Gliomas with Dynamic Contrast-Enhanced MRI

RATIONALE AND OBJECTIVES

This study aimed to investigate whether volume transfer constant (K^trans) and volume of extravascular extracellular space per unit volume of tissue (V_e) derived from dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) could quantitatively assess the tumor proliferation index (Ki-67) of gliomas noninvasively.

MATERIALS AND METHODS

The preoperative DCE MRI data of 69 patients with pathologically confirmed glioma (28, 8, and 33 cases in grades Ⅱ, Ⅲ, and Ⅳ) were retrospectively reviewed. The maximal K^trans and V_e were measured in the tumor body. The immunohistochemistry was used to detect the expression of Ki-67 proteins in glioma specimens. The Mann-Whitney U test was applied to analyze the differences in K^trans, V_e, and Ki-67 index across histologically defined glioma grades. Spearman correlation was performed between K^trans, V_e, and Ki-67 index. The receiver operating characteristic curve analysis was used to determine the cutoff values of K^trans and V_e in distinguishing different Ki-67 index expression levels.

RESULTS

K^trans, V_e, and Ki-67 index of grade Ⅱ (0.027 min^-1, 0.065, 4.04%) were significantly lower than those of grade Ⅲ (0.093 min^-1, 0.297, 25.13%) and Ⅳ (0.100 min^-1, 0.299, 25.37%). Both K^trans and V_e significantly correlated with the Ki-67 index in all tumors and high-grade gliomas (HGGs, grade Ⅲ and Ⅳ). The receiver operating characteristic curve analysis revealed that the cutoff values for K^trans (0.079 min^-1) and V_e (0.249) provided the best combination of sensitivity and specificity to distinguish the gliomas with high Ki-67 index from those with low Ki-67 index.

CONCLUSION

The DCE MRI-derived parameters were valuable in assessing the tumor cell proliferation in HGG noninvasively.

Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR)

Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions.

Amide proton transfer imaging might predict survival and IDH mutation status in high-grade glioma

OBJECTIVES

To assess the utility of amide proton transfer (APT) imaging as an imaging biomarker to predict prognosis and molecular marker status in high-grade glioma (HGG, WHO grade III/IV).

METHODS

We included 71 patients with pathologically diagnosed HGG who underwent preoperative MRI with APT imaging. Overall survival (OS) and progression-free survival (PFS) according to APT signal, clinical factors, MGMT methylation status, and IDH mutation status were analyzed. Multivariate Cox regression models with and without APT signal data were constructed. Model performance was compared using the integrated AUC (iAUC). Associations between APT signals and molecular markers were assessed using the Mann-Whitney test.

RESULTS

High APT signal was a significant predictor for poor OS (HR = 3.21, 95% CI = 1.62-6.34) and PFS (HR = 2.22, 95% CI = 1.33-3.72) on univariate analysis. On multivariate analysis, high APT signals were an independent predictor of poor OS and PFS when clinical factors alone (OS: HR = 2.89; PFS: HR = 2.13), or in combination with molecular markers (OS: HR = 2.85; PFS: HR = 2.00), were included as covariates. The incremental prognostic value of APT signals was significant for OS and PFS. IDH-wild type was significantly associated with high APT signals (p = 0.001) when compared to IDH-mutant; however, there was no difference based on MGMT methylation status (p = 0.208).

CONCLUSION

High APT signal was a significant predictor of poor prognosis in HGG. APT data showed significant incremental prognostic value over clinical prognostic factors and molecular markers and may also predict IDH mutation status.

KEY POINTS

• Amide proton transfer (APT) imaging is a promising prognostic marker of high-grade glioma. • APT signals were significantly higher in IDH-wild type compared to IDH-mutant high-grade glioma. • APT imaging may be valuable for preoperative screening and treatment guidance.

Amide Proton Transfer Contrast Distribution in Different Brain Regions in Young Healthy Subjects

Objectives

To define normal signal intensity values of amide proton transfer-weighted (APTw) magnetic resonance (MR) imaging in different brain regions.

Materials and Methods

Twenty healthy subjects (9 females, mean age 29 years, range 19 - 37 years) underwent MR imaging at 3 Tesla. 3D APTw (RF saturation B_1,rms = 2 μT, duration 2 s, 100% duty cycle) and 2D T2-weighted turbo spin echo (TSE) images were acquired. Postprocessing (image fusion, ROI measurements of APTw intensity values in 22 different brain regions) was performed and controlled by two independent neuroradiologists. Values were measured separately for each brain hemisphere. A subject was scanned both in prone and supine position to investigate differences between hemispheres. A mixed model on a 5% significance level was used to assess the effect of gender, brain region and side on APTw intensity values.

Results

Mean APTw intensity values in the hippocampus and amygdala varied between 1.13 and 1.57%, in the deep subcortical nuclei (putamen, globus pallidus, head of caudate nucleus, thalamus, red nucleus, substantia nigra) between 0.73 and 1.84%, in the frontal, occipital and parietal cortex between 0.56 and 1.03%; in the insular cortex between 1.11 and 1.15%, in the temporal cortex between 1.22 and 1.37%, in the frontal, occipital and parietal white matter between 0.32 and 0.54% and in the temporal white matter between 0.83 and 0.89%. APTw intensity values were significantly impacted both by brain region (p < 0.001) and by side (p < 0.001), whereby overall values on the left side were higher than on the right side (1.13 vs. 0.9%). Gender did not significantly impact APTw intensity values (p = 0.24). APTw intensity values between the left and the right side were partially reversed after changing the position of one subject from supine to prone.

Conclusion

We determined normal baseline APTw intensity values in different anatomical localizations in healthy subjects. APTw intensity values differed both between anatomical regions and between left and right brain hemisphere.

Can amide proton transfer-weighted imaging differentiate tumor grade and predict Ki-67 proliferation status of meningioma?

OBJECTIVES

To determine the utility of the amide proton transfer-weighted MR imaging in differentiating the WHO grade and predict proliferative activity of meningioma.

METHODS

Fifty-three patients with WHO grade I meningiomas and 26 patients with WHO grade II meningiomas underwent conventional and APT-weighted sequences on a 3.0 Tesla MR before clinical intervention. The APT-weighted (APTw) parameters in the solid tumor region were obtained and compared between two grades using the t test; the receiver operating characteristic (ROC) curve was used to assess the best parameter for predicting the grade of meningiomas. Pearson's correlation coefficient was calculated between the APTw_max and Ki-67 labeling index in meningiomas.

RESULTS

The APTw_max and APTw_mean values were not significantly different between WHO grade I and grade II meningiomas (p = 0.103 and p = 0.318). The APTw_min value was higher and the APTw_max-min value was lower in WHO grade II meningiomas than in WHO grade I tumors (p = 0.027 and p = 0.019). But the APTw_min was higher and the APTw_max-min was lower in microcystic meningiomas than in WHO grade II meningiomas (p = 0.001 and p = 0.006). The APTw_min combined with APTw_max-min showed the best diagnostic performance in predicting the grade of meningiomas with an AUC of 0.772. The APTw_max value was positively correlated with Ki-67 labeling index (r = 0.817, p < 0.001) in meningiomas; the regression equation for the Ki-67 labeling index (%) (Y) and APTw_max (%) (X) was Y = 4.9 × X - 12.4 (R² = 0.667, p < 0.001).

CONCLUSION

As a noninvasive imaging method, the ability of APTw-MR imaging in differentiating the grade of meningiomas is limited, but the technology can be used to predict the proliferative activity of meningioma.

KEY POINTS

• The APTw _min value was higher and the APTw _max-min value was lower in WHO grade II meningioma than in grade I tumors. • The APTw _min value was higher and the APTw _max-min value was lower in microcystic meningiomas than in WHO grade II meningiomas. • The APTw _max value was positively correlated with meningioma proliferation index.

An evidence-based approach to evaluate the accuracy of amide proton transfer-weighted MRI in characterization of gliomas

BACKGROUD

To perform a meta-analysis to evaluate the diagnostic accuracy of the amide proton transfer (APT) technique in differentiating high-grade gliomas (HGGs) from low grade gliomas (LGGs).

METHODS

Medical literature databases were searched for studies that evaluated the diagnostic accuracy of APT in patients suspected of brain tumor who underwent APT MRI and surgery. Only English language studies and published before September 2018 were considered to be included in this project. Homogeneity was assessed by the inconsistency index. Mean difference (MD) at 95% confidence interval (CI) of all parameters derived from APT was calculated. Publication bias was explored by Egger's funnel plot.

RESULTS

Six eligible studies were included in the meta-analysis, comprising 144 HGGs and 122 LGGs. The APT-related parameter signal intensity (SI) was significantly higher in the HGG than the LGG (WMD = 0.86 (0.61-1.1), P < .0001); A significant difference was also found between grade II and grade III (WMD = 0.6 (0.4-0.8), P < .0001), and between grade II and grade IV (WMD = 1.07 (0.65-1.49), P < .0001).

CONCLUSIONS

APT imaging may be a useful imaging biomarker for discriminating between LGGs and HGGs. However, large randomized control trials (RCT) were necessary to evaluate its clinical value.

The diagnostic efficacy of amide proton transfer imaging in grading gliomas and predicting tumor proliferation

Glioma is the most common primary intracranial tumor. Molecular neuropathology was introduced into the new 2016 WHO classification of brain tumor. Among the molecular biomarkers, Ki-67 antigen is the most important one, which reflects the proliferation rate and invasive ability of tumor cells. The amide proton transfer imaging, as a novel functional MR technique, can detect the free protein and polypeptide noninvasively, which might be a novel imaging method for predicting the WHO grading of glioma. In our study, the asymmetric magnetization transfer ratio (MTRasym) of high-grade gliomas (4.5%±2.3%) was significantly higher than of low-grade gliomas (2.9±1.1%), and the high-grade gliomas also showed higher expression of Ki-67 (38.9±21.0%) than did low-grade gliomas (4.3±2.8%). The MTRasym was positively correlated with Ki-67 values (r=0.25, P<0.001). The area under the receiver operating characteristic curve of MTRasym was 0.719. Furthermore, the diagnosis efficiency of MTRasym is better than that of the apparent diffusion coefficient (area under the receiver operating characteristic curve=0.682). This prospective study demonstrates that amide proton transfer may help in grading gliomas and has great potency in predicting tumor cell proliferation.

Amide proton transfer-weighted MRI in distinguishing high- and low-grade gliomas: a systematic review and meta-analysis

PURPOSE

Grading of brain gliomas is of clinical importance, and noninvasive molecular imaging may help differentiate low- and high-grade gliomas. We aimed to evaluate the diagnostic performance of amide proton transfer-weighted (APTw) MRI for differentiating low- and high-grade gliomas on 3-T scanners.

METHODS

A systematic literature search of Ovid-MEDLINE and EMBASE was performed up to March 28, 2018. Original articles evaluating the diagnostic performance of APTw MRI for differentiating low- and high-grade gliomas were selected. The pooled sensitivity and specificity were calculated using a bivariate random-effects model. A coupled forest plot and a hierarchical summary receiver operating characteristic curve were obtained. Heterogeneity was investigated using Higgins inconsistency index (I²) test. Meta-regression was performed.

RESULTS

Ten original articles with a total of 353 patients were included. High-grade gliomas showed significantly higher APT signal intensity than low-grade gliomas. The pooled sensitivity and specificity for the diagnostic performance of APTw MRI for differentiating low-grade and high-grade gliomas were 88% (95% CI, 77-94%) and 91% (95% CI, 82-96%), respectively. Higgins I² statistic demonstrated heterogeneity in the sensitivity (I² = 68.17%), whereas no heterogeneity was noted in the specificity (I² = 44.84%). In meta-regression, RF saturation power was associated with study heterogeneity. Correlation coefficients between APT signal intensity and Ki-67 cellular proliferation index ranged from 0.430 to 0.597, indicating moderate correlation. All studies showed excellent interobserver agreement.

CONCLUSIONS

Although heterogeneous protocols were used, APTw MRI demonstrated excellent diagnostic performance for differentiating low- and high-grade gliomas. APTw MRI could be a reliable technique for glioma grading in clinical practice.

APT-weighted MRI: Techniques, current neuro applications, and challenging issues

Amide proton transfer-weighted (APTw) imaging is a molecular MRI technique that generates image contrast based predominantly on the amide protons in mobile cellular proteins and peptides that are endogenous in tissue. This technique, the most studied type of chemical exchange saturation transfer imaging, has been used successfully for imaging of protein content and pH, the latter being possible due to the strong dependence of the amide proton exchange rate on pH. In this article we briefly review the basic principles and recent technical advances of APTw imaging, which is showing promise clinically, especially for characterizing brain tumors and distinguishing recurrent tumor from treatment effects. Early applications of this approach to stroke, Alzheimer's disease, Parkinson's disease, multiple sclerosis, and traumatic brain injury are also illustrated. Finally, we outline the technical challenges for clinical APT-based imaging and discuss several controversies regarding the origin of APTw imaging signals in vivo. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019;50:347-364.

RRM2 is a potential prognostic biomarker with functional significance in glioma

Glioma is one of the most common brain tumors, suggesting the importance of investigating the molecular mechanism of gliomas. We studied the roles of Ribonucleotide Reductase Regulatory Subunit M2 (RRM2) in glioma. Expressions of RRM2 are higher in glioma tissues evidenced by TCGA data, western blot and immunohistochemistry. RRM2 is negatively correlated with glioma patient's survival. RNA-seq showed that genes involved in apoptosis, proliferation, cell adhesion and negative regulation of signaling were up-regulated upon RNAi-mediated knock-down of RRM2. Cell phenotypes specific for stably knocking down RRM2 were determined using stable transfection in vitro. In an in vivo model, knock-down of RRM2 inhibited tumor growth and caused suppression of AKT and ERK1/2 signalings. Interfering RRM2 also down-regulated the expression of cyclin A, cyclin B1, cyclin D1, Vimentin, and N-cadherin, and elevated E-cadherin expression. Moreover, overexpression of RRM2 failed to increase the expression of cyclin B1, cyclin D1, and N-cadherin when phosphorylation of AKT and ERK1/2 was suppressed by LY294002 or PD98059. These findings indicated that RRM2 is a positive regulator of glioma progression which contributes to the migration and proliferation of glioma cells through ERK1/2 and AKT signalings and might be a novel prognostic indicator for glioma patients.

Predicting O6-Methylguanine-DNA Methyltransferase Protein Expression in Primary Low- and High-Grade Gliomas Using Certain Qualitative Characteristics of Amide Proton Transfer-Weighted Magnetic Resonance Imaging

OBJECTIVE

To demonstrate that certain qualitative amide proton transfer-weighted (APTw) characteristics can provide practical imaging clues for predicting O6-methylguanine-DNA methyltransferase (MGMT) protein expression in primary low- and high-grade gliomas, preoperatively and noninvasively.

METHODS

Pathologically confirmed low- and high-grade gliomas with APT data and immunohistochemical (IHC) reports were recruited in this study. The MGMT protein expression status was classified by postsurgery specimen immunostaining. Subjects were divided into two groups, MGMT-positive and MGMT-negative group, according to the immunoreactivity of MGMT protein expression documented in IHC reports. APTw images scanned at 3T magnetic resonance preoperatively were retrospectively analyzed. Two neuroradiologists were trained to evaluate presence of certain APTw features. Kappa value was calculated to show the consistency between the 2 observers. The Mann-Whitney U test was used to evaluate relationships between the 2 groups on APTw features. Negative predictive value and positive predictive value was used to evaluate the ability of APTw characteristics in predicting MGMT protein expression. Receiver operating characteristic curve was used to evaluate the diagnostic performance of APTw characters. Two-tailed P < 0.05 was considered as statistically significant.

RESULTS

Forty-two subjects were recruited in this study. Among them 38 specimens presented positive MGMT immunostaining (MGMT-positive group), 4 specimens were negative MGMT immunostaing (MGMT-negative group). There were, respectively, 37 and 5 APTw images appeared positive and negative APTw features. Differences between tumors of positive and negative MGMT expression on qualitative APTw features were significant (P = 0.020). The consistency coefficient of the 2 observers was 0.876 (kappa = 0.876). Three of five llgliomas with negative APTw features showed MGMT-negative immunostaining, leading to a negative predictive value of 60%, and 36 of 37 cases presenting positive APTw characteristics were tumors of MGMT-positive expression, generating a positive predictive value of 97.3%. The area under curve was 0.849.

CONCLUSIONS

APTw characteristics could be promising imaging markers by which to predict IHC MGMT expression in primary low- and high-grade gliomas preoperatively and noninvasively.

PSMB8 regulates glioma cell migration, proliferation, and apoptosis through modulating ERK1/2 and PI3K/AKT signaling pathways

Glioma has been considered as one of the most aggressive and popular brain tumors of patients. It is essential to explore the mechanism of glioma. In this study, we established PSMB8 as a therapeutic target for glioma treatment. Expression of PSMB8 as well as Ki-67 was higher in glioma tissues demonstrated by western blot and immunohistochemistry. Then, the role of PSMB8 in migration and proliferation of glioma cells was investigated by conducting wound-healing, trans-well assay, cell counting kit (CCK)-8, flow cytometry assay and colony formation analysis. The data showed that interfering PSMB8 may inhibit the migration and proliferation of glioma cells by reducing expression of cyclin A, cyclin B1, cyclin D1, Vimentin, and N-cadherin, and by increasing expression of E-cadherin. Additionally, interfering PSMB8 may induce apoptosis of glioma cells by upregulating caspase-3 expression. Furthermore, these in vitro findings were validated in vivo and the ERK1/2 and PI3k/AKT signaling pathways were involved in PSMB8-triggered migration and proliferation of glioma cells. In an in vivo model, downregulation of PSMB8 suppressed tumor growth. In conclusion, PSMB8 is closely associated with migration, proliferation, and apoptosis of glioma cells, and might be considered as a novel prognostic indicator in patients with gliomas.