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Amide proton transfer imaging to discriminate between low- and high-grade gliomas: added value to apparent diffusion coefficient and relative cerebral blood volume.

ABSTRACT: OBJECTIVES:To evaluate the added value of amide proton transfer (APT) imaging to the apparent diffusion coefficient (ADC) from diffusion tensor imaging (DTI) and the relative cerebral blood volume (rCBV) from perfusion magnetic resonance imaging (MRI) for discriminating between high- and low-grade gliomas. METHODS:Forty-six consecutive adult patients with diffuse gliomas who underwent preoperative APT imaging, DTI and perfusion MRI were enrolled. APT signals were compared according to the World Health Organization grade. The diagnostic ability and added value of the APT signal to the ADC and rCBV for discriminating between low- and high-grade gliomas were evaluated using receiver operating characteristic (ROC) analyses and integrated discrimination improvement. RESULTS:The APT signal increased as the glioma grade increased. The discrimination abilities of the APT, ADC and rCBV values were not significantly different. Using both the APT signal and ADC significantly improved discrimination vs. the ADC alone (area under the ROC curve [AUC], 0.888?vs.?0.910; P?=?0.007), whereas using both the APT signal and rCBV did not improve discrimination vs. the rCBV alone (AUC, 0.927?vs.?0.923; P?=?0.222). CONCLUSIONS:APT imaging may be a useful imaging biomarker that adds value to the ADC for discriminating between low- and high-grade gliomas. KEY POINTS:• Higher APT values were correlated with higher glioma grades. • Adding the APT signal to the ADC improved glioma grading. • Adding the APT signal to rCBV did not improve glioma grading. • APT is a useful adjunct to the ADC for glioma grading.

SUBMITTER: Choi YS

PROVIDER: S-EPMC5746027 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Amide proton transfer imaging to discriminate between low- and high-grade gliomas: added value to apparent diffusion coefficient and relative cerebral blood volume.

Choi Yoon Seong YS Ahn Sung Soo SS Lee Seung-Koo SK Chang Jong Hee JH Kang Seok-Gu SG Kim Se Hoon SH Zhou Jinyuan J

European radiology 20170123 8

<h4>Objectives</h4>To evaluate the added value of amide proton transfer (APT) imaging to the apparent diffusion coefficient (ADC) from diffusion tensor imaging (DTI) and the relative cerebral blood volume (rCBV) from perfusion magnetic resonance imaging (MRI) for discriminating between high- and low-grade gliomas.<h4>Methods</h4>Forty-six consecutive adult patients with diffuse gliomas who underwent preoperative APT imaging, DTI and perfusion MRI were enrolled. APT signals were compared accordin ...[more]

PMID: 28116517

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Project description:PurposeGlioblastoma and anaplastic astrocytoma represent the most commonly encountered high-grade-glioma (HGG) in adults. Although both neoplasms are very distinct entities in context of epidemiology, clinical course and prognosis, their appearance in conventional magnetic resonance imaging (MRI) is very similar. In search for additional information aiding the distinction of potentially confusable neoplasms, histogram analysis of apparent diffusion coefficient (ADC) maps recently proved to be auxiliary in a number of entities. Therefore, our present exploratory retrospective study investigated whether ADC histogram profile parameters differ significantly between anaplastic astrocytoma and glioblastoma, reflect the proliferation index Ki-67, or are associated with the prognostic relevant MGMT (methylguanine-DNA methyl-transferase) promotor methylation status.MethodsPre-surgical ADC volumes of 56 HGG patients were analyzed by histogram-profiling. Association between extracted histogram parameters and neuropathology including WHO-grade, Ki-67 expression and MGMT promotor methylation status was investigated due to comparative and correlative statistics.ResultsGrade IV gliomas were more heterogeneous than grade III tumors. More specifically, ADCmin and the lowest percentile ADCp10 were significantly lower, whereas ADCmax, ADC standard deviation and Skewness were significantly higher in the glioblastoma group. ADCmin, ADCmax, ADC standard deviation, Kurtosis and Entropy of ADC histogram were significantly correlated with Ki-67 expression. No significant difference could be revealed by comparison of ADC histogram parameters between MGMT promotor methylated and unmethylated HGG.ConclusionsADC histogram parameters differ significantly between glioblastoma and anaplastic astrocytoma and show distinct associations with the proliferative activity in both HGG. Our results suggest ADC histogram profiling as promising biomarker for differentiation of both, however, further studies with prospective multicenter design are wanted to confirm and further elaborate this hypothesis.

Project description:BackgroundMore 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.MethodsThis 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.ResultsAPT [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).ConclusionsAPT 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.

Project description:BackgroundSuspicious breast lesions [Breast Imaging Reporting and Data System (BI-RADS) category 4 or 5] detected only by magnetic resonance imaging (MRI) and invisible on other initial imaging modalities (MRI-only lesions) are usually small and poorly characterized in previous literature, thus making diagnosis and management difficult. This study aimed to investigate the clinical significance of quantitative apparent diffusion coefficient (ADC) metrics derived from conventional diffusion-weighted imaging (DWI) on evaluating MRI-only lesions.MethodsA total of 90 suspicious MRI-only lesions were evaluated, including 51 malignant and 39 benign lesions. Morphological and kinetic characteristics of all lesions (termed BI-RADS parameters) were described according to the BI-RADS lexicon on dynamic contrast-enhanced (DCE) imaging. Minimum, maximum, and mean ADC values (ADCmin, ADCmax, ADCmean) were obtained by measuring the ADC map of DWI. ADCheterogeneity was then obtained by the following formula: ADCheterogeneity = (ADCmax - ADCmin)/ADCmean. Diagnostic performance of these parameters was assessed and compared using the receiver operating characteristic (ROC) curve.ResultsOf the 90 MRI-only lesions, there were 45 masses and 45 non-mass lesions. Among BI-RADS parameters, only two different kinetic patterns were significantly different between benign and malignant groups (P=0.005 and P<0.001, respectively). The area under the ROC curve (AUC) of combined significant ADC parameters (ADCmin, ADCmean, and ADCmax, all P≤0.001) was significantly higher than that of the two different kinetic patterns (P=0.006 for both). For MRI-only masses, only ADCmean and ADCmax, among all BI-RADS and ADC parameters, had diagnostic value (combined AUC =0.833). For non-mass lesions, size, distribution, ADCmin, and ADCmean were significantly different between benign and malignant groups (P=0.004, P<0.001, P=0.001, and P<0.001, respectively). In addition, ADCmean had the highest diagnostic performance among all ADC parameters, regardless of mass or non-mass (AUC =0.825 and 0.812, respectively). ADCheterogeneity showed no significant differences, no matter in mass or non-mass groups (P=0.62 and 0.43, respectively).ConclusionsIn differentiating MRI-only suspicious lesions, quantitative ADC metrics generally performed better than BI-RADS parameters, and ADCmean is still the best ADC parameter to distinguish MRI-only lesions.

Project description:Background1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI.MethodsThis retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant.ResultsThe radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist.ConclusionsOur results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.

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Dataset Information

Amide proton transfer imaging to discriminate between low- and high-grade gliomas: added value to apparent diffusion coefficient and relative cerebral blood volume.

Publications

Amide proton transfer imaging to discriminate between low- and high-grade gliomas: added value to apparent diffusion coefficient and relative cerebral blood volume.

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