Relative Apparent Diffusion Coefficient Research Articles

Noninvasive assessment of Ki-67 labeling index in glioma patients based on multi-parameters derived from advanced MR imaging.

To investigate the predictive value of multi-parameters derived from advanced MR imaging for Ki-67 labeling index (LI) in glioma patients. One hundred and nine patients with histologically confirmed gliomas were evaluated retrospectively. These patients underwent advanced MR imaging, including dynamic susceptibility-weighted contrast enhanced MR imaging (DSC), MR spectroscopy imaging (MRS), diffusion-weighted imaging (DWI) and diffusion-tensor imaging (DTI), before treatment. Twenty-one parameters were extracted, including the maximum, minimum and mean values of relative cerebral blood flow (rCBF), relative cerebral blood volume (rCBV), relative mean transit time (rMTT), relative apparent diffusion coefficient (rADC), relative fractional anisotropy (rFA) and relative mean diffusivity (rMD) respectively, and ration of choline (Cho)/creatine (Cr), Cho/N-acetylaspartate (NAA) and NAA/Cr. Stepwise multivariate regression was performed to build multivariate models to predict Ki-67 LI. Pearson correlation analysis was used to investigate the correlation between imaging parameters and the grade of glioma. One-way analysis of variance (ANOVA) was used to explore the differences of the imaging parameters among the gliomas of grade II, III, and IV. The multivariate regression showed that the model of five parameters, including rCBVmax (RC=0.282), rCBFmax (RC=0.151), rADCmin (RC= -0.14), rFAmax (RC=0.325) and Cho/Cr ratio (RC=0.157) predicted the Ki-67 LI with a root mean square (RMS) error of 0. 0679 (R2 = 0.8025).The regression check of this model showed that there were no multicollinearity problem (variance inflation factor: rCBVmax, 3.22; rCBFmax, 3.14; rADCmin, 1.96; rFAmax, 2.51; Cho/Cr ratio, 1.64), and the functional form of this model was appropriate (F test: p=0.682). The results of Pearson correlation analysis showed that the rCBVmax, rCBFmax, rFAmax, the ratio of Cho/Cr and Cho/NAA were positively correlated with Ki-67 LI and the grade of glioma, while the rADCmin and rMDmin were negatively correlated with Ki-67 LI and the grade of glioma. Combining multiple parameters derived from DSC, DTI, DWI and MRS can precisely predict the Ki-67 LI in glioma patients.

The performance of the node reporting and data system 1.0 (Node-RADS) and DWI-MRI in staging patients with cervical carcinoma according to the new FIGO classification (2018).

To evaluate the diagnostic accuracy of the Node-RADS score and the utility of apparent diffusion coefficient (ADC) values in predicting metastatic lymph nodes (LNs) involvement in cervical cancer (CC) patients using magnetic resonance imaging (MRI). The applicability of the Node RADS score across three readers with different years of experience in pelvic imaging was also assessed. Among 140 patients, 68 underwent staging MRI, neoadjuvant chemotherapy and radical surgery, forming the study cohort. Node-RADS scores of the main pelvic stations were retrospectively determined to assess LN metastatic likelihood and compared with the histological findings. Mean ADC, relative ADC (rADC), and correct ADC (cADC) values of LNs classified as Node-RADS ≥ 3 were measured and compared with histological reports, considered as gold standard. Sensitivity, specificity, positive and negative predictive values (PPVs and NPVs), and accuracy were calculated for different Node-RADS thresholds. Node RADS ≥ 3 showed a sensitivity of 92.8% and specificity of 72.5%. Node RADS ≥ 4 yielded a sensitivity of 71.4% and specificity of 100%, while Node RADS 5 yielded 42.9% and 100%, respectively. The diagnostic performance of mean ADC, cADC and rADC values from 78 LNs with Node-RADS score ≥ 3 was assessed, with ADC demonstrating the highest area under the curve (AUC 0.820), compared to cADC and rADC values. The Node-RADS score provides a standardized LNs assessment, enhancing diagnostic accuracy in CC patients. Its ease of use and high inter-observer concordance support its clinical utility. ADC measurement of LNs shows promise as an additional tool for optimizing patient diagnostic evaluation.

Predicting glioblastoma recurrence using multiparametric MR imaging of non-enhancing peritumoral regions at baseline

BackgroundTo assess the feasibility of multiparametric magnetic resonance imaging in predicting tumor recurrence in nonenhancing peritumoral regions in patients with glioblastoma at baseline. MethodsFifty-eight patients with recurrent glioblastoma underwent multiparametric magnetic resonance imaging, including T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast perfusion-weighted imaging. Nonenhancing peritumoral regions with glioblastoma recurrence were identified by coregistering preoperative and post-recurrent magnetic resonance images. Regions of interest were placed in nonenhancing peritumoral regions with and without tumor recurrence to calculate the apparent diffusion coefficient value, and relative ratios of T2-weighted fluid-attenuated inversion recovery signal intensity, apparent diffusion coefficient, and cerebral blood volume values. ResultsSignificant lower relative T2-weighted fluid-attenuated inversion recovery signal intensity, apparent diffusion coefficient, and relative apparent diffusion coefficient but higher relative cerebral blood volume values were found in the nonenhancing peritumoral regions with tumor recurrence than without recurrence (all P < 0.05). The threshold values ≥ 0.89 for relative cerebral blood volume provide the optimal performance for predicting the nonenhancing peritumoral regions with future tumor recurrence, with the sensitivity, specificity, and accuracy of 84.7%, 83.6%, and 85.8%, respectively. The combination of relative T2-weighted fluid-attenuated inversion recovery signal intensity, apparent diffusion coefficient, and relative cerebral blood volume can provide better predictive performance than relative cerebral blood volume (P = 0.015). ConclusionThe combined use of T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast perfusion-weighted imaging can effectively estimate the risk of future tumor recurrence at baseline.

The diagnostic efficiency of integration of 2HG MRS and IVIM versus individual parameters for predicting IDH mutation status in gliomas in clinical scenarios: A retrospective study.

Currently, there remains a scarcity of established preoperative tests to accurately predict the isocitrate dehydrogenase (IDH) mutation status in clinical scenarios, with limited research has explored the potential synergistic diagnostic performance among metabolite, perfusion, and diffusion parameters. To address this issue, we aimed to develop an imaging protocol that integrated 2-hydroxyglutarate (2HG) magnetic resonance spectroscopy (MRS) and intravoxel incoherent motion (IVIM) by comprehensively assessing metabolic, cellular, and angiogenic changes caused by IDH mutations, and explored the diagnostic efficiency of this imaging protocol for predicting IDH mutation status in clinical scenarios. Patients who met the inclusion criteria were categorized into two groups: IDH-wild type (IDH-WT) group and IDH-mutant (IDH-MT) group. Subsequently, we quantified the 2HG concentration, the relative apparent diffusion coefficient (rADC), the relative true diffusion coefficient value (rD), the relative pseudo-diffusion coefficient (rD*) and the relative perfusion fraction value (rf). Intergroup differences were estimated using t-test and Mann-Whitney U test. Finally, we performed receiver operating characteristic (ROC) curve and DeLong's test to evaluate and compare the diagnostic performance of individual parameters and their combinations. 64 patients (female, 21; male, 43; age, 47.0 ± 13.7years) were enrolled. Compared with IDH-WT gliomas, IDH-MT gliomas had higher 2HG concentration, rADC and rD (P < 0.001), and lower rD* (P = 0.013). The ROC curve demonstrated that 2HG + rD + rD* exhibited the highest areas under curve (AUC) value (0.967, 95%CI 0.889-0.996) for discriminating IDH mutation status. Compared with each individual parameter, the predictive efficiency of 2HG + rADC + rD* and 2HG + rD + rD* shows a statistically significant enhancement (DeLong's test: P < 0.05). The integration of 2HG MRS and IVIM significantly improves the diagnostic efficiency for predicting IDH mutation status in clinical scenarios.

Deep learning assisted atlas-based delineation of the skeleton from Whole-Body Diffusion Weighted MRI in patients with malignant bone disease

BackgroundWhole-Body Diffusion Weighted MRI (WBDWI) can provide a basis for quantifying disease volume and apparent diffusion coefficient (ADC) to aid disease staging and response assessment of metastatic bone disease. In this study, we develop atlas-based registration assisted by two AI-based models (i) body-region classification for automatic identification of legs, pelvis, lumbar/thoracic/cervical spine and head, and (ii) automated spinal cord segmentation, to automatically delineate the whole skeleton on WBDWI. MethodsTraining and validation of AI-based models were performed on 40 patients (32:8 split) with confirmed Advanced Prostate Cancer (APC) who underwent baseline and after treatment WBDWI scans. Consequent atlas-based skeleton segmentation accuracy was validated using leave-one-out cross-validation in a cohort of 15 patients with Multiple Myeloma (MM). ResultsAtlas-based registration in combination with AI-based models demonstrated higher performance in segmenting the spinal column when compared with the traditional techniques (average dice score for the lumbar, thoracic and cervical spine of 0.77/0.78/0.68 compared to 0.71/0.69/0.51 from the conventional method, respectively). Similar performances were observed for long bones, pelvic girdle and ribcage (average dice score of 0.65/0.7/0.47, respectively). Our approach demonstrated lower relative median ADC and skeleton volume difference between estimation and ground truth compared to conventional atlas-based methods (average relative median ADC and skeleton volume difference of 10.3/2.1% compared to 13/2.7%, respectively). ConclusionAI-augmented atlas-based registration substantially improves the accuracy of derived skeletal segmentations from WBDWI compared with traditional atlas-based registration.

The value of diffusion-weighted imaging in the natural history of meningiomas: a predictor of tumor growth.

The general trend in meningioma treatment is shifting from surgery to active surveillance. However, the natural history of meningioma still needs to be clarified, and a simple, practical method is needed to identify fast-growing tumors. The authors aimed to determine whether diffusion-weighted imaging (DWI) could be a valuable imaging modality for predicting meningioma growth. Consecutive asymptomatic patients with a meningioma diagnosed on MRI and followed up at the authors' institution between July 2011 and July 2019 were eligible for inclusion in this retrospective study. Univariable and multivariable Cox regression analyses were used to explore whether the relative apparent diffusion coefficient (rADC) was an independent predictor of meningioma growth. Correlations between tumor growth rate (TGR), tumor volume doubling time (VDT), Ki-67, and rADC were assessed using the Pearson correlation coefficient. The predictive ability of rADC was evaluated using receiver operating characteristic (ROC) curves and validated with internal validation data. Sixty-four patients (47 females, 17 males) with a mean age of 62.2 ± 1.4 years were included in this study. Univariable and multivariable analyses revealed that rADC was an independent predictor of meningioma growth (p < 0.05). ROC curve analysis showed that baseline rADC had good predictive power for growing meningiomas (AUC = 0.88, 95% CI 0.78-0.96), as well as slow- or fast-growing meningiomas (AUC = 0.83, 95% CI 0.59-0.98). Moreover, rADC still had a good ability to discriminate between growing and nongrowing meningiomas in the validation set (AUC = 0.85, 95% CI 0.64-1.00). In the 20 patients with tumor growth, baseline rADC was moderately negatively correlated with TGR (r = -0.50, p = 0.02) and strongly positively correlated with VDT (r = 0.63, p = 0.003). Moreover, Ki-67 was significantly associated with rADC in 8 patients who had undergone surgery (r = -0.75, p = 0.03). In asymptomatic meningiomas, the lower the rADC at baseline, the faster the TGR and the shorter the VDT. DWI could be a valuable tool in predicting meningioma growth in asymptomatic patients.

The Role of Diffusion Weighted MRI in Diagnosis of Sacroiliitis

Background: The main aim of this study is to investigate the effectiveness of Apparent Diffusion Coefficient (ADC) and Diffusion Weighted Imaging (DWI) in identifying and detecting active sacro-iliitis. The study seeks to evaluate the potential of these imaging techniques as diagnostic tools for this particular condition. Materials and Methods: A total of 79 people underwent MRI examination for low back pain. Thirty-four of these individuals did not show any pathology in the sacroiliac joints and were included in the control group. 45 people diagnosed with sacroiliitis formed the patient group. All participants underwent spin echo, planar echo, diffusion-weighted MR imaging at a b=1000 s/mm². ADC values were determined by assessing the subchondral regions adjacent to both sacroiliac joints. Relative ADC values were obtained for both the patient and control groups. Results: The study found a significant difference (p&amp;lt;0.05) in mean ADC values between the pa-tient group (1202.4±268.2 mm²/s) and the control group (423.9±71.9 mm²/s). These results indicate distinct diffusion patterns between the two groups. Additionally, the relative ADC values were significantly different (p&amp;lt;0.05) with 0.434 in the patient group and 0.153 in the control group, further supporting the observed disparities in diffusion characteristics. Conclusions: Diffusion-weighted imaging has similar diagnostic efficacy to other MRI methods in detecting active sacroiliitis. In addition, it is thought that the relative ADC values may contribute more to the diagnosis of sacroiliitis.

Reproducibility of diffusion-weighted magnetic resonance imaging in head and neck cancer assessed on a 1.5 T MR-Linac and comparison to parallel measurements on a 3 T diagnostic scanner

Background and purposeBefore quantitative imaging biomarkers (QIBs) acquired with magnetic resonance imaging (MRI) can be used for interventional trials in radiotherapy (RT), technical validation of these QIBs is necessary. The aim of this study was to assess the reproducibility of apparent diffusion coefficient (ADC) values, derived from diffusion-weighted (DW) MRI, in head and neck cancer using a 1.5 T MR-Linac (MRL) by comparison to a 3 T diagnostic scanner (DS). Material and methodsDW-MRIs were acquired on MRL and DS for 15 head and neck cancer patients before RT and in week 2 and rigidly registered to the planning computed tomography. Mean ADC values were calculated for submandibular (SG) and parotid (PG) glands as well as target volumes (TV, gross tumor volume and lymph nodes), which were delineated based on computed tomography. Mean absolute ADC differences as well as within-subject coefficient of variation (wCV) and intraclass correlation coefficients (ICCs) were calculated for all volumes of interest. ResultsA total of 23 datasets were analyzed. Mean ADC difference (DS-MRL) for SG, PG and TV resulted in 142, 254 and 93·10-6 mm2/s. wCVs/ICCs, comparing MRL and DS, were determined as 13.7 %/0.26, 24.4 %/0.23 and 16.1 %/0.73 for SG, PG and TV, respectively. ConclusionADC values, measured on the 1.5 T MRL, showed reasonable reproducibility with an ADC underestimation in contrast to the DS. This ADC shift must be validated in further experiments and considered for future translation of QIB candidates from DS to MRL for response adaptive RT.

Evaluations of the diagnostic performance of ZOOMit diffusion-weighted imaging and conventional diffusion-weighted imaging for breast lesions.

Diffusion-weighted imaging (DWI) is valuable in the screening, diagnosis, and grading of breast lesions. However, conventional DWI (C-DWI) is prone to chemical shift and distortion. ZOOMit DWI (Z-DWI), as an advanced magnetic resonance imaging (MRI) technique, applies two spatially selective parallel excitation pulses to focus sampling in the hope of obtaining more valuable information. This study aimed to evaluate and compare the image quality and feasibility of Z-DWI with those of C-DWI in breast lesions. The study included 51 patients with breast lesions who underwent breast MRI from May 2021 to February 2022. All patients received Z-DWI and C-DWI sequences, with b values selected as 50 and 800 s/mm2 (Z-DWIb50, Z-DWIb800, C-DWIb50, and C-DWIb800). Apparent diffusion coefficient (ADC) values based on Z-DWI and C-DWI were calculated. For qualitative analysis, four image quality parameters were selected and assessed on a 4-point Likert scale (1 = poor and 4 = excellent). For quantitative analysis, ADC, relative ADC (rADC), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and tumor-to-parenchymal contrast (TPC) values were selected for comparison. Z-DWI had higher scores compared to C-DWI in terms of lesion conspicuity, anatomical details, distortion and artifacts, and overall image quality (P<0.05). Meanwhile, the agreement between the two readers was reasonably good [intraclass correlation coefficient (ICC) range, 0.360-0.881]. The SNR of Z-DWIb800 was better than that of C-DWIb800 (P<0.001). The Z-DWI ADC and rADC values of breast lesions were statistically significantly lower than those of C-DWI (mean ADC: P<0.001; rADC; P=0.005). Z-DWI sequences were shown to have superior image quality. The ADC map of Z-DWI is more conducive to the imaging evaluation of breast lesions.

Differentiating primary central nervous system lymphoma from glioblastoma by time-dependent diffusion using oscillating gradient

BackgroundThis study aimed to elucidate the impact of effective diffusion time setting on apparent diffusion coefficient (ADC)-based differentiation between primary central nervous system lymphomas (PCNSLs) and glioblastomas (GBMs) and to investigate the usage of time-dependent diffusion magnetic resonance imaging (MRI) parameters.MethodsA retrospective study was conducted involving 21 patients with PCNSLs and 66 patients with GBMs using diffusion weighted imaging (DWI) sequences with oscillating gradient spin-echo (Δeff = 7.1 ms) and conventional pulsed gradient (Δeff = 44.5 ms). In addition to ADC maps at the two diffusion times (ADC7.1 ms and ADC44.5 ms), we generated maps of the ADC changes (cADC) and the relative ADC changes (rcADC) between the two diffusion times. Regions of interest were placed on enhancing regions and non-enhancing peritumoral regions. The mean and the fifth and 95th percentile values of each parameter were compared between PCNSLs and GBMs. The area under the receiver operating characteristic curve (AUC) values were used to compare the discriminating performances among the indices.ResultsIn enhancing regions, the mean and fifth and 95th percentile values of ADC44.5 ms and ADC7.1 ms in PCNSLs were significantly lower than those in GBMs (p = 0.02 for 95th percentile of ADC44.5 ms, p = 0.04 for ADC7.1 ms, and p < 0.01 for others). Furthermore, the mean and fifth and 95th percentile values of cADC and rcADC were significantly higher in PCNSLs than in GBMs (each p < 0.01). The AUC of the best-performing index for ADC7.1 ms was significantly lower than that for ADC44.5 ms (p < 0.001). The mean rcADC showed the highest discriminating performance (AUC = 0.920) among all indices. In peritumoral regions, no significant difference in any of the three indices of ADC44.5 ms, ADC7.1 ms, cADC, and rcADC was observed between PCNSLs and GBMs.ConclusionsEffective diffusion time setting can have a crucial impact on the performance of ADC in differentiating between PCNSLs and GBMs. The time-dependent diffusion MRI parameters may be useful in the differentiation of these lesions.

Magnetic Resonance Imaging Combined with Histological Techniques for Dynamic Assessment of Cytotoxic Edema after Cerebral Ischemia-Reperfusion Injury.

Reperfusion therapy after ischemic cerebral stroke may cause cerebral ischemia-reperfusion injury (CIRI), and cerebral edema is an important factor that may aggravate CIRI. Our study aimed to dynamically monitor the development of early cytotoxic edema after CIRI by magnetic resonance imaging (MRI) and to validate it using multiple histological imaging methods. Male Sprague Dawley rats were divided into sham and CIRI groups. T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI)-MRI scans were performed in the sham and CIRI groups after reperfusion. Relative apparent diffusion coefficient (rADC) values were calculated and the midline shift (MLS) was measured. A series of histological detection techniques were performed to observe changes in the cerebral cortex and striatum of CIRI rats. Correlation analysis of rADC values with aquaporin-4 (AQP4) and sodium-potassium-chloride cotransport protein 1 (Na+-K+-2Cl-- cotransporter 1; NKCC1) was performed. rADC values began to increase and reached a relatively low value in the cerebral cortex and striatum at 24 h after reperfusion, and the MLS reached relatively high values at 24 h after reperfusion (all p < 0.05). Hematoxylin-eosin (HE) staining showed that the nerve cells in the cortex and striatum of the sham group were regular in morphology and neatly arranged, and in the CIRI-24 h group were irregular, disorganized, and loosely structured. Using terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining, the number of TUNEL+ cells in the ischemic cortex and striatum in CIRI-24 h group was shown to increase significantly compared with the sham group (p < 0.05). Transmission electron microscopy showed that the perivascular astrocytic foot processes were swollen in the cortex and striatum of the CIRI-24 h group. Pearson correlation analysis demonstrated that rADC values were negatively correlated with the number of anti-glial fibrillary acidic protein (GFAP)+AQP4+ and GFAP+NKCC1+ cells of the CIRI rats. MRI combined with histological techniques can dynamically assess cytotoxic edema after CIRI, in a manner that is clear and intuitive for scientific researchers and clinicians, and provides a scientific basis for the application of MRI techniques for monitoring the dynamic progress of CIRI.

Differentiating brain metastasis from glioblastoma by time-dependent diffusion MRI

BackgroundThis study was designed to investigate the use of time-dependent diffusion magnetic resonance imaging (MRI) parameters in distinguishing between glioblastomas and brain metastases.MethodsA retrospective study was conducted involving 65 patients with glioblastomas and 27 patients with metastases using a diffusion-weighted imaging sequence with oscillating gradient spin-echo (OGSE, 50 Hz) and a conventional pulsed gradient spin-echo (PGSE, 0 Hz) sequence. In addition to apparent diffusion coefficient (ADC) maps from two sequences (ADC50Hz and ADC0Hz), we generated maps of the ADC change (cADC): ADC50Hz − ADC0Hz and the relative ADC change (rcADC): (ADC50Hz − ADC0Hz)/ ADC0Hz × 100 (%).ResultsThe mean and the fifth and 95th percentile values of each parameter in enhancing and peritumoral regions were compared between glioblastomas and metastases. The area under the receiver operating characteristic curve (AUC) values of the best discriminating indices were compared. In enhancing regions, none of the indices of ADC0Hz and ADC50Hz showed significant differences between metastases and glioblastomas. The mean cADC and rcADC values of metastases were significantly higher than those of glioblastomas (0.24 ± 0.12 × 10−3mm2/s vs. 0.14 ± 0.03 × 10−3mm2/s and 23.3 ± 9.4% vs. 14.0 ± 4.7%; all p < 0.01). In peritumoral regions, no significant difference in all ADC indices was observed between metastases and glioblastomas. The AUC values for the mean cADC (0.877) and rcADC (0.819) values in enhancing regions were significantly higher than those for ADC0Hz5th (0.595; all p < 0.001).ConclusionsThe time-dependent diffusion MRI parameters may be useful for differentiating brain metastases from glioblastomas.

MRI characteristics predict BRAF V600E status in gangliogliomas and pleomorphic xanthoastrocytomas and provide survival prognostication.

BRAF V600E mutation is a common genomic alteration in gangliogliomas (GGs) and pleomorphic xanthoastrocytomas (PXAs) with prognostic and therapeutic implications. To investigate the ability of magnetic resonance imaging (MRI) features to predict BRAF V600E status in GGs and PXAs and their prognostic values. A cohort of 44 patients with histologically confirmed GGs and PXAs was reviewed retrospectively. BRAF V600E status was determined by immunohistochemistry (IHC) staining and fluorescence quantitative polymerase chain reaction (PCR). Demographics and MRI characteristics of the two groups were evaluated and compared. Univariate and multivariate Cox regression analyses were performed to identify MRI features that were prognostic for progression-free survival (PFS). T1/FLAIR ratio, enhancing margin, and mean relative apparent diffusion coefficient (rADCmea) value showed significant differences between the BRAF V600E-mutant and BRAF V600E-wild groups (all P < 0.05). Binary logistic regression analysis revealed only rADCmea value was the independent predictive factor for BRAF V600E status (P = 0.027). Univariate Cox regression analysis showed age at diagnosis (P = 0.032), WHO grade (P = 0.020), enhancing margin (P = 0.029), and rADCmea value (P = 0.005) were significant prognostic factors for PFS. In multivariate Cox regression analysis, increasing age (P = 0.040, hazard ratio [HR] = 1.04, 95% confidence interval [CI] = 1.002-1.079) and lower rADCmea values (P = 0.021, HR = 0.036, 95% CI = 0.002-0.602) were associated with poor PFS in GGs and PXAs. Imaging features are potentially predictive of BRAF V600E status in GGs and PXAs. Furthermore, rADCmea value is a valuable prognostic factor for patients with GGs or PXAs.

Multi-nuclear sodium, diffusion, and perfusion MRI in human gliomas

PurposeThere is limited knowledge about the associations between sodium and proton MRI measurements in brain tumors. The purpose of this study was to quantify intra- and intertumoral correlations between sodium, diffusion, and perfusion MRI in human gliomas.MethodsTwenty glioma patients were prospectively studied on a 3T MRI system with multinuclear capabilities. Three mutually exclusive tumor volumes of interest (VOIs) were segmented: contrast-enhancing tumor (CET), T2/FLAIR hyperintense non-enhancing tumor (NET), and necrosis. Median and voxel-wise associations between apparent diffusion coefficient (ADC), normalized relative cerebral blood volume (nrCBV), and normalized sodium measurements were quantified for each VOI.ResultsBoth relative sodium concentration and ADC were significantly higher in areas of necrosis compared to NET (P = 0.003 and P = 0.008, respectively) and CET (P = 0.02 and P = 0.02). Sodium concentration was higher in CET compared to NET (P = 0.04). Sodium and ADC were higher in treated compared to treatment-naïve gliomas within NET (P = 0.006 and P = 0.01, respectively), and ADC was elevated in CET (P = 0.03). Median ADC and sodium concentration were positively correlated across patients in NET (r = 0.77, P < 0.0001) and CET (r = 0.84, P < 0.0001), but not in areas of necrosis (r = 0.45, P = 0.12). Median nrCBV and sodium concentration were negatively correlated across patients in areas of NET (r=-0.63, P = 0.003). Similar associations were observed when examining voxel-wise correlations within VOIs.ConclusionSodium MRI is positively correlated with proton diffusion MRI measurements in gliomas, likely reflecting extracellular water. Unique areas of multinuclear MRI contrast may be useful in future studies to understand the chemistry of the tumor microenvironment.

Atypical primary central nervous system lymphoma and glioblastoma: multiparametric differentiation based on non-enhancing volume, apparent diffusion coefficient, and arterial spin labeling

ObjectivesTo evaluate the multiparametric diagnostic performance with non-enhancing tumor volume, apparent diffusion coefficient (ADC), and arterial spin labeling (ASL) to differentiate between atypical primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM).MethodsOne hundred and fifty-eight patients with pathologically confirmed typical PCNSL (n = 59), atypical PCNSL (hemorrhage, necrosis, or heterogeneous contrast enhancement, n = 29), and GBM (n = 70) were selected. Relative minimum ADC (rADCmin), mean (rADCmean), maximum (rADCmax), and rADCmax-min (rADCdif) were obtained by standardization of the contralateral white matter. Maximum cerebral blood flow (CBFmax) was obtained according to the ASL-CBF map. The regions of interests (ROIs) were manually delineated on the inner side of the tumor to further generate a 3D-ROI and obtain the non-enhancing tumor (nET) volume. The area under the curve (AUC) was used to evaluate the diagnostic performance.ResultsAtypical PCNSLs showed significantly lower rADCmax, rADCmean, and rADCdif than that of GBMs. GBMs showed significantly higher CBFmax and nET volume ratios than that of atypical PCNSLs. Combined three-variable models with rADCmean, CBFmax, and nET volume ratio were superior to one- and two-variable models. The AUC of the three-variable model was 0.96, and the sensitivity and specificity were 90% and 96.55%, respectively.ConclusionThe combined evaluation of rADCmean, CBFmax, and nET volume allowed for reliable differentiation between atypical PCNSL and GBM.Key Points• Atypical PCNSL is easily misdiagnosed as glioblastoma, which leads to unnecessary surgical resection.• The nET volume, ADC, and ASL-derived parameter (CBF) were lower for atypical PCNSL than that for glioblastoma.• The combination of multiple parameters performed well (AUC = 0.96) in the discrimination between atypical PCNSL and glioblastoma.

How can we differentiate supratentorial tumor recurrence from postradiation imaging changes in children treated for primary malignant brain tumors?

Distinguishing tumor recurrence from therapy-induced imaging changes (TIIC) on brain MRI in children treated for primary malignant brain tumors may be challenging. The authors aimed to assess the diagnostic ability of multimodal MRI in differentiating TIIC from tumor recurrence. The authors retrospectively included children with abnormal supratentorial brain MRI findings after treatment for primary malignant brain tumors (regardless of their localization) with complete resection and radiotherapy. A total of 18 patients with TIIC and 25 patients with tumor recurrence were compared, according to structural, apparent diffusion coefficient (ADC), and arterial spin labeling (ASL) imaging data accrued over time. TIIC were defined by a new MRI scan that was stable for at least 1 year or had regressed, or by histopathology findings in specimens obtained when the anomaly was surgically treated. The time interval between completion of radiotherapy and the appearance of abnormal brain MRI findings was significantly shorter in the TIIC group compared with the tumor recurrence group (median 6 vs 35 months; p < 0.001). TIIC appeared as foci of increased T2-weighted signal intensity, without nodule, associated with variable contrast enhancement. Tumor recurrence appeared as a well-defined nodule with intermediate signal intensity on T2-weighted images with nodular contrast enhancement. Relative ADC values were significantly higher in the TIIC group (median 1.43 vs 0.88; p < 0.001). Relative ASL-cerebral blood flow (CBF) values were significantly lower in the TIIC group (median 0.27 vs 0.43; p = 0.04). On follow-up MRI, TIIC could progress, regress, or remain stable. In most instances (72%), they decreased in size or remained stable at 4 years of follow-up. MRI features of TIIC include foci of increased signal intensity without a demonstrable nodule on T2-weighted images, high ADC values, and lower ASL-CBF values, whereas tumor recurrence appears as a well-defined nodule with low ADC values and higher ASL-CBF values.

Whole body MRI with DWI in people with NF1 and Schwannomatosis: Are qualitative and quantitative imaging features of peripheral lesions comparable to localized MRI?

PurposeTo compare the qualitative and quantitative features of peripheral lesions on localized (L) and whole-body (WB) magnetic resonance imaging (MRI) in people with neurofibromatosis type 1 (NF1) and schwannomatosis. Materials and methodsThis is a retrospective, HIPAA compliant study with twenty-seven patients (14 women, 13 men; mean age (years): 38 (3–67)) who underwent both L-MRI and WB-MRI without interval treatment. WB-MRI and L-MRI were comprised of T1-weighted, fat suppressed (FS) T2-weighted or short tau inversion recovery (STIR), diffusion-weighted imaging (DWI) using b-values of 50, 400, and 800 s/mm2, apparent diffusion coefficient (ADC) mapping and pre- and post-contrast FST1 sequences. Two readers recorded qualitative (T1 and T2/STIR signal intensity and heterogeneity, contrast enhancement and heterogeneity, perilesional enhancement, presence of a target sign and perilesional edema) and quantitative (size, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), ADC) features of peripheral lesions on L-MRI and WB-MRI.Descriptive statistics, Wilcoxon signed-rank test and McNemar’s test were used. ResultsThere were 31 peripheral lesions identified in 27 subjects, (mean size: 3.1 cm (range: 1–8.1 cm) on both L-MRI and WB-MRI).There were no differences in T1 signal and heterogeneity and T2/STIR signal and heterogeneity between WB-MRI and L-MRI ((p = 0.180, 0.083, 0.317 and 0.157 respectively). There were also no differences in contrast enhancement, heterogeneity and perilesional enhancement between WB-MRI and L-MRI (p = 1.000, 0.380 and 1.000 respectively). Presence of a target sign and perilesional edema did not differ between WB-MRI and L-MRI (p = 1.000 and 0.500 respectively). Craniocaudal (CC), mediolateral (ML) and anteroposterior (AP) size measurements on WB-MRI did not differ from CC, ML and AP size measurements on L-MRI (p = 0.597, 0.128 and 0.783 respectively). SNR on WB-DWI did not differ from SNR on L-DWI for b50, b400 and b800 images (p = 0.285, 0.166, and 0.974 respectively), and CNR on WB-DWI did not differ from CNR on L-DWI for b50, b400 and b800 images (p = 0.600, 0.124, and 0.787 respectively). There was no significant difference in minimum, mean and maximum ADC values between WB-DWI and L-DWI (p = 0.234, 0.481, and 0.441 respectively). Median minimum, mean and maximum ADC (×10(-3)mm(2)/s) differences between WB-DWI and L-DWI were 0.0 (range −1 to 0.7), 0.0 (range −0.5 to 0.6), and 0.1 (range −1.2 to 0.8) respectively. Relative ADC difference averages were 29.1% for minimum values, 10.1% for mean values, and 14.8% for maximum values. ConclusionWB-MRI yields qualitative and quantitative features for peripheral lesions, including DWI and ADC measurements, that are comparable to L-MRI scans. WB-DWI can be reliably used for the assessment of peripheral nerve sheath tumors, obviating the need for a repeat follow-up L-DWI acquisition.

Preoperative prediction of cytokeratin-19 expression for hepatocellular carcinoma using T1 mapping on gadoxetic acid-enhanced MRI combined with diffusion-weighted imaging and clinical indicators.

To explore the value of T1 mapping on gadoxetic acid-enhanced magnetic resonance imaging (MRI) in preoperative predicting cytokeratin 19 (CK19) expression for hepatocellular carcinoma (HCC). This retrospective study included 158 patients from two institutions with surgically resected treatment-native solitary HCC who underwent preoperative T1 mapping on gadoxetic acid-enhanced MRI. Patients from institution I (n = 102) and institution II (n = 56) were assigned to training and test sets, respectively. univariable and multivariable logistic regression analyses were performed to investigate the association of clinicoradiological variables with CK19. The receiver operating characteristic (ROC) curve and precision-recall (PR) curve were used to evaluate the performance for CK19 prediction. Then, a prediction nomogram was developed for CK19 expression. The performance of the prediction nomogram was evaluated by its discrimination, calibration, and clinical utility. Multivariable logistic regression analysis showed that AFP>400ng/ml (OR=4.607, 95%CI: 1.098-19.326; p=0.037), relative apparent diffusion coefficient (rADC)≤0.71 (OR=3.450, 95%CI: 1.126-10.567; p=0.030), T1 relaxation time in the 20-minute hepatobiliary phase (T1rt-HBP)>797msec (OR=4.509, 95%CI: 1.301-15.626; p=0.018) were significant independent predictors of CK19 expression. The clinical-quantitative model (CQ-Model) constructed based on these significant variables had the best predictive performance with an area under the ROC curve of 0.844, an area under the PR curve of 0.785 and an F1 score of 0.778. The nomogram constructed based on CQ-Model demonstrated satisfactory performance with C index of 0.844 (95%CI: 0.759-0.908) and 0.818 (95%CI: 0.693-0.902) in the training and test sets, respectively. T1 mapping on gadoxetic acid-enhanced MRI has good predictive efficacy for preoperative prediction of CK19 expression in HCC, which can promote the individualized risk stratification and further treatment decision of HCC patients.

Magnetic resonance imaging with perfusion and diffusion in assessment of intra-spinal lesions

BackgroundMRI is the imaging modality of choice in detecting and evaluating spinal lesions. However, advanced techniques as diffusion and perfusion studies can help more in differentiation between neoplastic and non-neoplastic lesions. Our study aimed to detect the role of diffusion and T2* perfusion weighted imaging in characterization of different spinal lesions by measuring the relative cerebral blood volume (rCBV) and apparent diffusion coefficient value (ADC) of all lesions, comparing perfusion of lesion in relation to normal cord and detection of diffusion pattern of each lesion by observing its signal intensity at diffusion images and ADC map.ResultsFifty patients were included in this study. Twenty-five patients were with non-neoplastic lesions and twenty-five patients were with neoplastic lesions. The cutoff value of rCBV was 1.2 in differentiation between neoplastic and non-neoplastic lesions with 92% sensitivity and 100% specificity. The cutoff value of ADC was 1 in differentiation between neoplastic and non-neoplastic lesions with 56% sensitivity and 80% specificity.ConclusionsDiffusion and perfusion weighted images help in differentiation between neoplastic and non-neoplastic spinal lesions.

Diffusion-Weighted Imaging of the Abdomen: Correction for Gradient Nonlinearity Bias in Apparent Diffusion Coefficient.

Gradient nonlinearity (GNL) introduces spatial nonuniformity bias in apparent diffusion coefficient (ADC) measurements, especially at large offsets from the magnet isocenter. To investigate the effects of GNL in abdominal ADC measurements and to develop an ADC bias correction procedure. Retrospective. Two homemade ultrapure water phantoms/25 patients with histologically confirmed pancreatic ductal adenocarcinoma (PDAC). A 3.0 T/diffusion-weighted imaging (DWI) with single-shot echo-planar imaging sequence. ADC bias was computed in the three orthogonal directions at different offset locations. The spatial-dependent correctors of ADC bias were generated from the ADCs of phantom 1. The ADCs were estimated before and after corrections for the phantom 1 with both the proposed approach and the theoretical GNL correction method. For the patients, ADCs were measured in abdominal tissues including left and right liver lobes, PDAC, spleen, bilateral kidneys, and bilateral paraspinal muscles. Friedman tests and Wilcoxon tests. The ADC bias measured by phantom 1 was 9.7% and 12.6% higher in the right-left and anterior-posterior directions and 9.2% lower in the superior-inferior direction at the 150 mm offsets from the magnetic isocenter. The corrected vs. the uncorrected ADCs measurements (median: 2.20× 10-3 mm2 /sec for both the proposed method and the theoretical GNL method vs. 2.31× 10-3 mm2 /sec, respectively) and their relative ADC errors (0.014, 0.016, and 0.054, respectively) were lower in the phantom 1. The relative ADC errors substantially decreased after correction in the phantom 2 (median: 0.048 and -0.008, respectively). The ADCs of all the abdominal tissues were lower after correction except for the left liver lobes (P=0.13). GNL bias in abdominal ADC can be measured by a DWI phantom. The proposed correction procedure was successfully applied for the bias correction in abdominal ADC. 3. Stage 1.