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Diagnosis of spinal lesions using perfusion parameters measured by DCE-MRI and metabolism parameters measured by PET/CT.

ABSTRACT: PURPOSE:To investigate the correlation of parameters measured by dynamic-contrast-enhanced MRI (DCE-MRI) and 18F-FDG PET/CT in spinal tumors, and their role in differential diagnosis. METHODS:A total of 49 patients with pathologically confirmed spinal tumors, including 38 malignant, six benign and five borderline tumors, were analyzed. The MRI and PET/CT were done within 3 days, before biopsy. On MRI, the ROI was manually placed on area showing the strongest enhancement to measure pharmacokinetic parameters Ktrans and kep. On PET, the maximum standardized uptake value SUVmax was measured. The parameters in different histological groups were compared. ROC was performed to differentiate between the two largest subtypes, metastases and plasmacytomas. Spearman rank correlation was performed to compare DCE-MRI and PET/CT parameters. RESULTS:The Ktrans, kep and SUVmax were not statistically different among malignant, benign and borderline groups (P?=?0.95, 0.50, 0.11). There was no significant correlation between Ktrans and SUVmax (r?=?-?0.20, P?=?0.18), or between kep and SUVmax (r?=?-?0.16, P?=?0.28). The kep was significantly higher in plasmacytoma than in metastasis (0.78?±?0.17 vs. 0.61?±?0.18, P?=?0.02); in contrast, the SUVmax was significantly lower in plasmacytoma than in metastasis (5.58?±?2.16 vs. 9.37?±?4.26, P?=?0.03). In differential diagnosis, the AUC of kep and SUVmax was 0.79 and 0.78, respectively. CONCLUSIONS:The vascular parameters measured by DCE-MRI and glucose metabolism measured by PET/CT from the most aggressive tumor area did not show a significant correlation. The results suggest they provide complementary information reflecting different aspects of the tumor, which may aid in diagnosis of spinal lesions. These slides can be retrieved under Electronic Supplementary Material.

SUBMITTER: Zhang J

PROVIDER: S-EPMC7709817 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Diagnosis of spinal lesions using perfusion parameters measured by DCE-MRI and metabolism parameters measured by PET/CT.

Zhang Jiahui J Chen Yongye Y Zhang Yanyan Y Zhang Enlong E Yu Hon J HJ Yuan Huishu H Zhang Yang Y Su Min-Ying MY Lang Ning N Lang Ning N

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society 20191121 5

<h4>Purpose</h4>To investigate the correlation of parameters measured by dynamic-contrast-enhanced MRI (DCE-MRI) and <sup>18</sup>F-FDG PET/CT in spinal tumors, and their role in differential diagnosis.<h4>Methods</h4>A total of 49 patients with pathologically confirmed spinal tumors, including 38 malignant, six benign and five borderline tumors, were analyzed. The MRI and PET/CT were done within 3 days, before biopsy. On MRI, the ROI was manually placed on area showing the strongest enhancement ...[more]

PMID: 31754820

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Project description:PurposeTumor hypoxia is a major cause of radiation resistance, often present in various solid tumors. Dynamic [18 F]-fluoromisonidazole (FMISO) PET imaging is able to reliably assess tumor hypoxia. Comprehensive characterization of tumor microenvironment through FMISO-PET and dynamic contrast enhanced (DCE) MR multimodality imaging might be a valuable alternative to the dynamic FMISO-PET acquisition. The aim of this work was to explore the correlation between the FMISO-PET and DCE-MRI kinetic parameters.MethodsThis study was done on head and neck cancer patients (N = 6), who were imaged dynamically with FMISO-PET and DCE-MRI on the same day. Images were registered and analyzed for kinetics on a voxel basis. FMISO-PET images were analyzed with the two-tissue compartment three rate-constant model. Additionally, tumor-to-muscle ratio (TMR) maps were evaluated. DCE-MRI was analyzed with the extended Tofts model. Voxel-wise Pearson's coefficients were calculated for each patient to assess pairwise parameter correlations.ResultsMedian correlations between FMISO uptake parameters and DCE-MRI kinetic parameters varied across the parameter pairs in the range from -0.05 to 0.71. The highest median correlation of r = 0.71 was observed for the pair Vb -vp , while the K1 -Ktrans median correlation was r = 0.45. Median correlation coefficients for the K1 -vp and the Ki -Ktrans pairs were r = 0.42 and r = 0.32, respectively. Correlations between FMISO uptake rate parameter Ki and DCE-MRI kinetic parameters varied substantially across the patients, whereas correlations between the FMISO and DCE-MRI vascular parameters were consistently high. Median TMR-K1 and TMR-Ktrans correlations were r = 0.52 and r = 0.46, respectively, but varied substantially across the patients.ConclusionsBased on this clinical evidence, we can conclude that the vascular fraction parameters obtained through DCE-MRI kinetic analysis or FMISO kinetic analysis measure the same biological property, while other kinetic parameters are unrelated. These results might be useful in the design of future clinical trials involving FMISO-PET/DCE-MR multimodality imaging for the assessment of tumor microenvironment.

Project description:ObjectivesPulmonary perfusion abnormalities are prevalent in patients with chronic obstructive pulmonary disease (COPD), are potentially reversible, and may be associated with emphysema development. Therefore, we aimed to evaluate the clinical meaningfulness of perfusion defects in percent (QDP) using DCE-MRI.MethodsWe investigated a subset of baseline DCE-MRIs, paired inspiratory/expiratory CTs, and pulmonary function testing (PFT) of 83 subjects (age = 65.7 ± 9.0 years, patients-at-risk, and all GOLD groups) from one center of the "COSYCONET" COPD cohort. QDP was computed from DCE-MRI using an in-house developed quantification pipeline, including four different approaches: Otsu's method, k-means clustering, texture analysis, and 80th percentile threshold. QDP was compared with visual MRI perfusion scoring, CT parametric response mapping (PRM) indices of emphysema (PRMEmph) and functional small airway disease (PRMfSAD), and FEV1/FVC from PFT.ResultsAll QDP approaches showed high correlations with the MRI perfusion score (r = 0.67 to 0.72, p < 0.001), with the highest association based on Otsu's method (r = 0.72, p < 0.001). QDP correlated significantly with all PRM indices (p < 0.001), with the strongest correlations with PRMEmph (r = 0.70 to 0.75, p < 0.001). QDP was distinctly higher than PRMEmph (mean difference = 35.85 to 40.40) and PRMfSAD (mean difference = 15.12 to 19.68), but in close agreement when combining both PRM indices (mean difference = 1.47 to 6.03) for all QDP approaches. QDP correlated moderately with FEV1/FVC (r = - 0.54 to - 0.41, p < 0.001).ConclusionQDP is associated with established markers of disease severity and the extent corresponds to the CT-derived combined extent of PRMEmph and PRMfSAD. We propose to use QDP based on Otsu's method for future clinical studies in COPD.Key points• QDP quantified from DCE-MRI is associated with visual MRI perfusion score, CT PRM indices, and PFT. • The extent of QDP from DCE-MRI corresponds to the combined extent of PRMEmph and PRMfSAD from CT. • Assessing pulmonary perfusion abnormalities using DCE-MRI with QDP improved the correlations with CT PRM indices and PFT compared to the quantification of pulmonary blood flow and volume.

Project description:PURPOSE:The aim of this study was to develop a portable perfusion phantom and validate its utility in quantitative dynamic contrast-enhanced magnetic resonance imaging of the abdomen. METHODS:A portable perfusion phantom yielding a reproducible contrast enhancement curve (CEC) was developed. A phantom package including perfusion and static phantoms were imaged simultaneously with each of three healthy human volunteers in two different 3T MR scanners. Look-up tables correlating reference (known) contrast concentrations with measured ones were created using either the static or perfusion phantom. Contrast maps of image slices showing four organs (liver, spleen, pancreas, and paravertebral muscle) were generated before and after data correction using the look-up tables. The contrast concentrations at 4.5 min after dosing in each of the four organs were averaged for each volunteer. The mean contrast concentrations (4 organs × 3 volunteers = 12) were compared for the two scanners, and the intra-class correlation coefficient (ICC) was calculated. Also, the ICC of the mean Ktrans values between the two scanners was calculated before and after data correction. RESULTS:The repeatability coefficient of CECs of perfusion phantom was higher than 0.997 in all measurements. The ICC of the tissue contrast concentrations between the two scanners was 0.693 before correction, but increased to 0.974 after correction using the look-up tables (LUTs) of perfusion phantom. However, the ICC was not increased after correction using static phantom (ICC: 0.617). Similarly, the ICC of the Ktrans values was 0.899 before correction, but increased to 0.996 after correction using perfusion phantom LUTs. The ICC of the Ktrans values, however, was not increased when static phantom LUTs were used (ICC: 0.866). CONCLUSIONS:The perfusion phantom reduced variability in quantitating contrast concentration and Ktrans values of human abdominal tissues across different MR units, but static phantom did not. The perfusion phantom has the potential to facilitate multi-institutional clinical trials employing quantitative DCE-MRI to evaluate various abdominal malignancies.

Project description:PurposeTo develop a disposable point-of-care portable perfusion phantom (DP4) and validate its clinical utility in a multi-institutional setting for quantitative dynamic contrast-enhanced magnetic resonance imaging (qDCE-MRI).MethodsThe DP4 phantom was designed for single-use and imaged concurrently with a human subject so that the phantom data can be utilized as the reference to detect errors in qDCE-MRI measurement of human tissues. The change of contrast-agent concentration in the phantom was measured using liquid chromatography-mass spectrometry. The repeatability of the contrast enhancement curve (CEC) was assessed with five phantoms in a single MRI scanner. Five healthy human subjects were recruited to evaluate the reproducibility of qDCE-MRI measurements. Each subject was imaged concurrently with the DP4 phantom at two institutes using three 3T MRI scanners from three different vendors. Pharmacokinetic (PK) parameters in the regions of liver, spleen, pancreas, and paravertebral muscle were calculated based on the Tofts model (TM), extended Tofts model (ETM), and shutter speed model (SSM). The reproducibility of each PK parameter over three measurements was evaluated with the intraclass correlation coefficient (ICC) and compared before and after DP4-based error correction.ResultsThe contrast-agent concentration in the DP4 phantom was linearly increased over 10 min (0.17 mM/min, measurement accuracy: 96%) after injecting gadoteridol (100 mM) at a constant rate (0.24 ml/s, 4 ml). The repeatability of the CEC within the phantom was 0.997 when assessed by the ICC. The reproducibility of the volume transfer constant, Ktrans , was the highest of the PK parameters regardless of the PK models. The ICCs of Ktrans in the TM, ETM, and SSM before DP4-based error correction were 0.34, 0.39, and 0.72, respectively, while those increased to 0.93, 0.98, and 0.86, respectively, after correction.ConclusionsThe DP4 phantom is reliable, portable, and capable of significantly improving the reproducibility of qDCE-MRI measurements.

Project description:BackgroundComputer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learning is rarely reported.PurposeTo evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)-based, radiomics and deep-learning methods, by taking peritumor tissues into consideration.Study typeRetrospective.PopulationIn all, 133 patients with histologically confirmed 91 malignant and 62 benign mass lesions for training (74 patients with 48 malignant and 26 benign lesions for testing).Field strength/sequence3T, using the volume imaging for breast assessment (VIBRANT) dynamic contrast-enhanced (DCE) sequence.Assessment3D tumor segmentation was done automatically by using fuzzy-C-means algorithm with connected-component labeling. A total of 99 texture and histogram parameters were calculated for each case, and 15 were selected using random forest to build a radiomics model. Deep learning was implemented using ResNet50, evaluated with 10-fold crossvalidation. The tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs.Statistical testsThe malignancy probability was calculated using each model, and the threshold of 0.5 was used to make a diagnosis.ResultsIn the training dataset, the diagnostic accuracy was 76% using three ROI-based parameters, 84% using the radiomics model, and 86% using ROI + radiomics model. In deep learning using the per-slice basis, the area under the receiver operating characteristic (ROC) was comparable for tumor alone, smallest and 1.2 times box (AUC = 0.97-0.99), which were significantly higher than 1.5 and 2.0 times box (AUC = 0.86 and 0.71, respectively). For per-lesion diagnosis, the highest accuracy of 91% was achieved when using the smallest bounding box, and that decreased to 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the per-lesion diagnostic accuracy was also the highest when using the smallest bounding box, 89%.Data conclusionDeep learning using ResNet50 achieved a high diagnostic accuracy. Using the smallest bounding box containing proximal peritumor tissue as input had higher accuracy compared to using tumor alone or larger boxes.Level of evidence3 Technical Efficacy: Stage 2.

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Diagnosis of spinal lesions using perfusion parameters measured by DCE-MRI and metabolism parameters measured by PET/CT.

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Diagnosis of spinal lesions using perfusion parameters measured by DCE-MRI and metabolism parameters measured by PET/CT.

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