Mean Apparent Diffusion Coefficient Research Articles

Detecting High-Dose Methotrexate-Induced Brain Changes in Pediatric and Young Adult Cancer Survivors Using [18F]FDG PET/MRI: A Pilot Study.

Significant improvements in treatments for children with cancer have resulted in a growing population of childhood cancer survivors who may face long-term adverse outcomes. Here, we aimed to diagnose high-dose methotrexate-induced brain injury on [18F]FDG PET/MRI and correlate the results with cognitive impairment identified by neurocognitive testing in pediatric cancer survivors. Methods: In this prospective, single-center pilot study, 10 children and young adults with sarcoma (n = 5), lymphoma (n = 4), or leukemia (n = 1) underwent dedicated brain [18F]FDG PET/MRI and a 2-h expert neuropsychologic evaluation on the same day, including the Wechsler Abbreviated Scale of Intelligence, second edition, for intellectual functioning; Delis-Kaplan Executive Function System (DKEFS) for executive functioning; and Wide Range Assessment of Memory and Learning, second edition (WRAML), for verbal and visual memory. Using PMOD software, we measured the SUVmean, cortical thickness, mean cerebral blood flow (CBFmean), and mean apparent diffusion coefficient of 3 different cortical regions (prefrontal cortex, cingulate gyrus, and hippocampus) that are routinely involved during the above-specified neurocognitive testing. Standardized scores of different measures were converted to z scores. Pairs of multivariable regression models (one for z scores < 0 and one for z scores > 0) were fitted for each brain region, imaging measure, and test score. Heteroscedasticity regression models were used to account for heterogeneity in variances between brain regions and to adjust for clustering within patients. Results: The regression analysis showed a significant correlation between the SUVmean of the prefrontal cortex and cingulum and DKEFS-sequential tracking (DKEFS-TM4) z scores (P = 0.003 and P = 0.012, respectively). The SUVmean of the hippocampus did not correlate with DKEFS-TM4 z scores (P = 0.111). The SUVmean for any evaluated brain regions did not correlate significantly with WRAML-visual memory (WRAML-VIS) z scores. CBFmean showed a positive correlation with SUVmean (r = 0.56, P = 0.01). The CBFmean of the cingulum, hippocampus, and prefrontal cortex correlated significantly with DKEFS-TM4 (all P < 0.001). In addition, the hippocampal CBFmean correlated significantly with negative WRAML-VIS z scores (P = 0.003). Conclusion: High-dose methotrexate-induced brain injury can manifest as a reduction in glucose metabolism and blood flow in specific brain areas, which can be detected with [18F]FDG PET/MRI. The SUVmean and CBFmean of the prefrontal cortex and cingulum can serve as quantitative measures for detecting executive functioning problems. Hippocampal CBFmean could also be useful for monitoring memory problems.

Differentiation of submandibular sialadenitis based on apparent diffusion coefficient.

This study aimed to quantify the submandibular gland in suppurative sialadenitis, primary Sjögren's syndrome (pSS) and radiation-induced sialadenitis using the apparent diffusion coefficient (ADC) for differential diagnosis. This retrospective study included 16 patients with suppurative sialadenitis (n = 9), pSS (n = 3) and radiation-induced sialadenitis (n = 4) who underwent magnetic resonance imaging between June 2006 and May 2022. The ADC of the submandibular glands in each state was calculated, and the differences were analysed using a one-way analysis of variance and Tukey's post hoc test. Receiver operating characteristic curves were used to assess the ability of the ADC to distinguish each condition. Statistical significance was set at p < 0.05. The mean ADC value (×10-3 mm2/s) ± standard deviation in the control (non-affected side of the suppurative sialadenitis group), suppurative sialadenitis, pSS and radiation-induced groups were 0.94 ± 0.16, 1.24 ± 0.16, 1.33 ± 0.13 and 1.5 ± 0.12, respectively (p < 0.001). The diagnostic value for distinguishing each group was ≥0.75. ADC values are useful for quantitatively assessing and distinguishing submandibular glands in suppurative sialadenitis, primary Sjögren's syndrome and radiation-induced sialadenitis.

190 Do Fractional Anisotropy and Apparent Diffusion Coefficient From Diffusion Tensor Imaging Correlate to Symptoms of Radiculopathy? A Case Series From BACPAC MRI

INTRODUCTION: Quantitative diffusion tensor imaging (DTI) parameters such as fractional anisotropy (FA) and apparent diffusion coefficient (ADC) are used to detect axonal pathology in nerve injuries. METHODS: Ganglia of the lumbar plexus nerves (L1-5) were input as seeds to track the nerve fibers using Constant Solid Angle reconstruction. Fiber tracts and values of FA and ADC were extracted along the lumbar plexus nerve profiles. Change in pain (VAS) and disability (ODI) after interlaminar epidural steroid injection at site of compression was compared to the changes in FA and ADC values. RESULTS: Five exams from three patients with bilateral L4-5 foraminal narrowing were included in this study. Two patients were imaged at two time points, three months apart. The average age of the cohort was 62.6 ± 5.3 years. Mean FA values were lowest at L3 (left: 0.32 ± 0.08; right: 0.35 ± 0.05) and L4 (left: 0.39 ± 0.06; right: 0.38 ± 0.03). Correspondingly, mean ADC values were highest at L3 (left: 0.0017 ± 0.0002; right: 0.0016 ± 0.0001) and L4 (left: 0.0.0013 ± 0.0001; right: 0.0015 ± 0.0002). For one patient, pain did not change in severity and ODI improved from severe to moderate disability after steroid injection: FA increased with no significant change in ADC. For another patient, pain increased from moderate to severe and ODI stayed the same after injection: FA decreased and ADC did not significantly change. CONCLUSIONS: The tractography generated from the segmentation model demonstrated lowest FA and highest ADC values corresponding to the level of compression. For patient outcomes, the FA values were observed to be more sensitive to changes in pain and disability compared to the ADC values.

399 Source Localization of Burst Suppression in Post-Cardiac Arrest Patients

OBJECTIVES/GOALS: Burst suppression is a neurophysiological marker associated with severe hypoxic-ischemic injury following cardiac arrest. The goal of this study is to identify the anatomical regions of the brain associated with burst suppression post-cardiac arrest. METHODS/STUDY POPULATION: 86 comatose patients post-cardiac arrest admitted to the neurological-ICU from Massachusetts General Hospital and Brigham and Women’s Hospital were included in this study. EEG data after return of spontaneous circulation were preprocessed and artifact was rejected. Burst segments were extracted for source localization analysis from epochs with burst suppression. Four bursts for each patients were manually selected. The source of the bursts were obtained using the Champagne algorithm and mapped on the Desikan-Killiany atlas. The source for each burst was defined as any region of interest (ROI) with power &gt; = 75th percentile relative to all ROIs. The power of the bursts at each source was correlated with the burden of brain injury measured using apparent diffusion coefficient (ADC) per ROI. RESULTS/ANTICIPATED RESULTS: 48 (56%) patients had burst suppression. 5 (10.4%) of patients with burst suppression were independent at the time of hospital discharge. Preliminary analyses was performed on 6 patients (24 bursts in total). ROI’s determined to be sources in a majority of the burst (&gt;=13) were bilateral superior frontal, rostral middle frontal, parstriangularis precentral, superior parietal, inferior parietal, right post central, superior temporal, lateral occipital, and left middle temporal ROI. A lower mean ADC intensity was associated with a higher EEG power in the bilateral superior frontal (r = -0.80, p &lt; 0.0001; r = -0.677, p &lt; 0.001, respectively), left superior parietal (r = -0.53, p = 0.009), left middle temporal (r = -0.43, p = 0.042) ROI. DISCUSSION/SIGNIFICANCE: The source of bursts in patients post-cardiac arrest experiencing burst suppression is not well defined. This study will improve our understanding of how burst suppression is a measure of cortical injury, how it may relate to the burden of injury found on ADC imaging, and patient outcomes.

Diffusion tensor imaging in differentiation of spinal cord lesions

Background Spinal lesions sometimes remain insufficiently visualized by ‘conventional’ magnetic resonance imaging techniques; indeed very often the lesions seen on T2 weighted images do not completely display the effects of pathology on the long tracts. Diffusion imaging, which enables the imaging of molecular water motion, has been applied recently to spinal cord diseases Aim The purpose of this study was to assess the role of Diffusion tensor imaging in in differentiation between different spinal cord lesions Patients &amp; methods This prospective study included 30 patients with myelopathy symptoms. All patients were subjected to full history taking, clinical examination, magnetic resonance imaging and diffusion tensor imaging MRI. Results The mean FA values at the site of the lesion was significantly lower than the normal appearing spinal cord in inflammation/demyelination, compressive myelopathy and traumatic spinal cord injury groups while the mean apparent diffusion coefficient values at the site of the lesions was significantly higher than the normal appearing spinal cord in inflammation/demyelination group. FA was sensitive than apparent diffusion coefficient in the detection of the spinal cord abnormalities with a sensitivity of 90% versus 60% respectively. Conclusion Diffusion tensor imaging with its quantitative indices is a non-invasive tool that is used in functional assessment of normal and pathologic spinal cord white matter, giving detailed information about different pathology and helping early management.

Prediction of histopathologic grades of bladder cancer with radiomics based on MRI: Comparison with traditional MRI

PurposeTo compare biparametric magnetic resonance imaging (bp-MRI) radiomics signatures and traditional MRI model for the preoperative prediction of bladder cancer (BCa) grade. Materials and methodsThis retrospective study included 255 consecutive patients with pathologically confirmed 113 low-grade and 142 high-grade BCa. The traditional MRI nomogram model was developed using univariate and multivariate logistic regression by the mean apparent diffusion coefficient (ADC), vesical imaging reporting and data system, tumor size, and the number of tumors. Volumes of interest were manually drawn on T2-weighted imaging (T2WI) and ADC maps by 2 radiologists. Using one-way analysis of variance, correlation, and least absolute shrinkage and selection operator methods to select features. Then, a logistic regression classifier was used to develop the radiomics signatures. Receiver operating characteristic (ROC) analysis was used to compare the diagnostic abilities of the radiomics and traditional MRI models by the DeLong test. Finally, decision curve analysis was performed by estimating the clinical usefulness of the 2 models. ResultsThe area under the ROC curves (AUCs) of the traditional MRI model were 0.841 in the training cohort and 0.806 in the validation cohort. The AUCs of the 3 groups of radiomics model [ADC, T2WI, bp-MRI (ADC and T2WI)] were 0.888, 0.875, and 0.899 in the training cohort and 0.863, 0.805, and 0.867 in the validation cohort, respectively. The combined radiomics model achieved higher AUCs than the traditional MRI model. decision curve analysis indicated that the radiomics model had higher net benefits than the traditional MRI model. ConclusionThe bp-MRI radiomics model may help distinguish high-grade and low-grade BCa and outperforming the traditional MRI model. Multicenter validation is needed to acquire high-level evidence for its clinical application.

Correlation of shear-wave elastography stiffness and apparent diffusion coefficient values with tumor characteristics in breast cancer

We aimed to investigate the correlation between shear-wave elastography (SWE) and apparent diffusion coefficient (ADC) values in breast cancer and to identify the associated characteristics. We included 91 breast cancer patients who underwent SWE and breast MRI prior to surgery between January 2016 and November 2017. We measured the lesion’s mean (Emean) and maximum (Emax) elasticities of SWE and ADC values. We evaluated the correlation between SWE, ADC values and tumor size. The mean SWE and ADC values were compared for categorical variable of the pathological/imaging characteristics. ADC values showed negative correlation with Emean (r = − 0.315, p = 0.002) and Emax (r = − 0.326, p = 0.002). SWE was positively correlated with tumor size (r = 0.343–0.366, p < 0.001). A higher SWE value indicated a tendency towards a higher T stage (p < 0.001). Triple-negative breast cancer showed the highest SWE values (p = 0.02). SWE were significantly higher in breast cancers with posterior enhancement, vascularity, and washout kinetics (p < 0.02). SWE stiffness and ADC values were negatively correlated in breast cancer. SWE values correlated significantly with tumor size, and were higher in triple-negative subtype and aggressive imaging characteristics.

Visualising cervical lymph nodes in jaw osteonecrosis using diffusion-weighted imaging.

This study aimed to apply diffusion-weighted imaging to the evaluation of cervical lymph nodes affected by medication-related osteonecrosis of the jaw (MRONJ). This retrospective study analysed the diffusion-weighted imaging data of 70 patients with or without MRONJ (Stages 0-3). The mean apparent diffusion coefficient (ADC) values of the submandibular (level IB) and superior internal jugular (level IIA) nodes in the control and MRONJ groups were calculated, and differences were analysed using the Mann-Whitney test. Moreover, receiver operating characteristic (ROC) curve analysis was performed to evaluate the ability of ADC values to predict lymph nodes that were affected by MRONJ. p < 0.05 was considered statistically significant. The median [interquartile range] (×10-3 mm2/s) of level IB was 0.74 [0.7-0.81] and 0.93 [0.84-1.09] and that of level IIA was 0.79 [0.76-0.85] and 0.97 [0.84-1.06] in the control and MRONJ groups respectively. ROC analysis revealed that the ADC value had excellent ability to discriminate between the control and MRONJ groups. The study findings indicate that diffusion-weighted imaging can contribute to differentiation of MRONJ from other cervical lymph node diseases and facilitate early detection of MRONJ.

Prostate diffusion-weighted imaging (DWI) in MR-guided radiotherapy: Reproducibility assessment on 1.5 T MR-Linac and 1.5 T MR-simulator

PurposeDiffusion-weighted imaging (DWI) holds promise for image-guided radiotherapy (MRgRT) in prostate cancer. However, challenges persist due to image distortion, artifacts, and apparent diffusion coefficient (ADC) reproducibility issues. This study aimed to assess DWI image quality and ADC reproducibility on both a 1.5 T MR-simulator and a 1.5 T MR-Linac, employing measurements from both an ACR MRI phantom and prostate cancer patients undergoing MRgRT. MethodsDW-MRI scans were conducted on 19 patients (mean age = 69 ± 8 years, with 23 MR-visible intra-prostatic lesions) and an ACR MRI phantom using a 1.5 T MR-simulator (b-values = 0, 800, 1400s/mm2) and a 1.5 T MR-Linac (b-values = 50, 500, 800 s/mm2). ADC homogeneity in the phantom was evaluated via 1D profile flatness (FL) in three directions. Image quality was assessed through qualitative 5-point Likert scale ratings and quantitative ADC and signal-to-noise ratio (SNR) measurements. Intra-observer reproducibility of image quality scores was evaluated using ICC(1, 2). Geometric distortion was measured by comparing landmark sizes on the ACR phantom against the ground truth. Mean ADC and reproducibility were assessed using Bland-Altman plots. ResultsBoth MR-simulator and MR-Linac demonstrated high ADC homogeneity (FL > 87.5% - MR-simulator: 97.23 ± 0.62%, MR-Linac: 94.75 ± 0.62%, p < 0.05) in the phantom. Image quality scores revealed acceptable ratings (≥3) for capsule demarcation, image artifacts, and geometric distortion in patients. However, intra-prostatic lesions were barely discernible in b800 images for both MR-simulator (average score = 2.37 ± 1.33) and MR-Linac (average score = 2.16 ± 1.28). While MR-Linac DWI scans exhibited significantly more severe geometric distortion than MR-simulator scans (p < 0.01), most phantom measurements fell within the image in-plane resolution of 3 mm. Significant differences were noted in MR-simulator ADC (CTV: 1.20 ± 0.14 × 10−3 mm2/s (MR-simulator) vs 1.06 ± 0.10 × 10–3 mm2/s (MR-Linac); GTV: 1.05 ± 0.21 × 10−3 mm2/s vs 0.91 ± 0.16 × 10 mm2/s, all p < 0.05), with a small non-zero bias observed in the Bland-Altman analysis (CTV: 12.3%; GTV: 14.5%). ConclusionThe significantly larger MR-simulator ADC and the small non-zero bias hint at potential systematic differences in ADC values acquired from an MR-simulator and an MR-Linac, both at 1.5 T. Although acceptable ADC homogeneity was noted, caution is warranted in interpreting MR-Linac DWI images due to occasional severe artifacts. Further studies are essential to validate DWI and ADC as reliable imaging markers in prostate cancer MRgRT.

Repeatability and reproducibility of prostate apparent diffusion coefficient values on a 1.5 T magnetic resonance linear accelerator

Background and PurposeIntegrated magnetic resonance linear accelerator (MR-Linac) systems offer potential for biologically based adaptive radiation therapy using apparent diffusion coefficient (ADC). Accurate tracking of longitudinal ADC changes is key to establishing ADC-driven dose adaptation. Here, we report repeatability and reproducibility of intraprostatic ADC using deformable image registration (DIR) to correct for inter-fraction prostate changes. Materials and MethodsThe study included within-fraction repeat ADC measurements for three consecutive fractions for 20 patients with prostate cancer treated on a 1.5 T MR-Linac. We deformably registered successive fraction T2-weighted images and applied the deformation vector field to corresponding ADC maps to align to fraction 2. We delineated gross tumour volume (GTV), peripheral zone (PZ) and prostate clinical target volume (CTV) regions-of-interest (ROIs) on T2-weighted MRI and copied to ADC maps. We computed intraclass correlation coefficients (ICC) and percent repeatability coefficient (%RC) to determine within-fraction repeatability and between-fraction reproducibility for individual voxels, mean and 10th percentile ADC values per ROI. ResultsThe ICC between repeats and fractions was excellent for mean and 10th percentile ADC in all ROIs (ICC > 0.86), and moderate repeatability and reproducibility existed for individual voxels (ICC > 0.542). Similarly, low %RC within-fraction (4.2–17.9 %) mean and 10th percentile ADC existed, with greater %RC between fractions (10.2–36.8 %). Higher %RC existed for individual voxel within-fraction (21.7–30.6 %) and between-fraction (32.1–34.5 %) ADC. ConclusionsResults suggest excellent ADC repeatability and reproducibility in clinically relevant ROIs using DIR to correct between-fraction anatomical changes. We established the precision of voxel-level ADC tracking for future biologically based adaptation implementation.

Halka Şekilli Lateral Ventrikül Nodüllerinin Konvansiyonel ve Diffüzyon MR Görüntüleme Özellikleri

Objective: The aim of this study is to evaluate the conventional and diffusion MRI findings of ring-shaped lateral ventricular nodules (RSLVN) along with clinical features.&#x0D; Methods: MR images of all patients who underwent contrast-enhanced brain MRI between 2019 and 2023 were retrospectively evaluated. The number, shape, maximal diameter, and signal intensity of RSLVNs on T1-weighted (T1W), T2-weighted (T2W), fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI), and contrast-enhancement status were evaluated. Apparent diffusion coefficient (ADC) values and normalized ADC ratios of nodules were also determined. If follow-up MRIs were performed, morphological changes of RSLVNs were evaluated. &#x0D; Results: RSLVN was observed in fifteen (0.51%) of 2920 patients. Multiple RSLVNs were observed in five patients and therefore a total of 23 RSLVNs were identified in fifteen patients. Nodules were located on the roof of the lateral ventricle in eight nodules (34.8%), in the frontal horn in twelve nodules (52.2%), and in the septum pellucidum in three. 6 of 23 RSLVNs (26.1%) were larger than 1 cm. All RSLVNs were isointense on T1W and T2W, while hyperintense on FLAIR. On DWI, 20 of 23 RSLVNs had isointense signal and the remaining 3 lesions were hyperintense. The mean ADC value and nADC ratio were 1.42 ± 0.29 x 10-3mm2 and 1.87 ± 0.31, respectively.&#x0D; Conclusion: RSLVNs may be more frequent than previously reported. Their uniform MRI appearance and typical localizations are distinctive, and they can reach relatively large sizes. Morphological stability during follow-up and the ADC values of these lesions suggest a possible benign nature.

Repeatability quantification of brain diffusion-weighted imaging for future clinical implementation at a low-field MR-linac

BackgroundLongitudinal assessments of apparent diffusion coefficients (ADCs) derived from diffusion-weighted imaging (DWI) during intracranial radiotherapy at magnetic resonance imaging-guided linear accelerators (MR-linacs) could enable early response assessment by tracking tumor diffusivity changes. However, DWI pulse sequences are currently unavailable in clinical practice at low-field MR-linacs. Quantifying the in vivo repeatability of ADC measurements is a crucial step towards clinical implementation of DWI sequences but has not yet been reported on for low-field MR-linacs. This study assessed ADC measurement repeatability in a phantom and in vivo at a 0.35 T MR-linac.MethodsEleven volunteers and a diffusion phantom were imaged on a 0.35 T MR-linac. Two echo-planar imaging DWI sequence variants, emphasizing high spatial resolution (“highRes”) and signal-to-noise ratio (“highSNR”), were investigated. A test–retest study with an intermediate outside-scanner-break was performed to assess repeatability in the phantom and volunteers’ brains. Mean ADCs within phantom vials, cerebrospinal fluid (CSF), and four brain tissue regions were compared to literature values. Absolute relative differences of mean ADCs in pre- and post-break scans were calculated for the diffusion phantom, and repeatability coefficients (RC) and relative RC (relRC) with 95% confidence intervals were determined for each region-of-interest (ROI) in volunteers.ResultsBoth DWI sequence variants demonstrated high repeatability, with absolute relative deviations below 1% for water, dimethyl sulfoxide, and polyethylene glycol in the diffusion phantom. RelRCs were 7% [5%, 12%] (CSF; highRes), 12% [9%, 22%] (CSF; highSNR), 9% [8%, 12%] (brain tissue ROIs; highRes), and 6% [5%, 7%] (brain tissue ROIs; highSNR), respectively. ADCs measured with the highSNR variant were consistent with literature values for volunteers, while smaller mean values were measured for the diffusion phantom. Conversely, the highRes variant underestimated ADCs compared to literature values, indicating systematic deviations.ConclusionsHigh repeatability of ADC measurements in a diffusion phantom and volunteers’ brains were measured at a low-field MR-linac. The highSNR variant outperformed the highRes variant in accuracy and repeatability, at the expense of an approximately doubled voxel volume. The observed high in vivo repeatability confirms the potential utility of DWI at low-field MR-linacs for early treatment response assessment.

Evaluation of multiplexed sensitivity encoding diffusion-weighted imaging in detecting uterine lesions: Image quality optimization

PurposeTo compare the image quality of multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) and single-shot echo-planar imaging (SS-EPI-DWI) techniques in uterine MRI. MethodsEighty-eight eligible patients underwent MUSE-DWI and SS-EPI-DWI examinations simultaneously using a 3.0 T MRI system. Two radiologists independently performed quantitative and qualitative analysis of the two groups of images using a double-blind method. The weighted Kappa test was used to evaluate the interobserver agreement. Wilcoxon's rank sum test was used for qualitative parameters, and paired t-test was used for quantitative parameters. Spearman rank correlation analysis was used to obtained correlation between pathological results and mean apparent diffusion coefficient (ADC) value. ResultsThe qualitative and quantitative analysis of the images by the two radiologists were in good or excellent agreement, with weighted kappa value ranging from 0.636 to 0.981. The scores of total subjective image quality (15.4 ± 0.99) and signal-to-noise ratio (158.99 ± 60.71) of MUSE-DWI were significantly higher than those of SS-EPI-DWI (12.93 ± 1.62 P < 0.001; 130.23 ± 48.29 P < 0.05). It effectively reduced image distortion and artifact, and had better lesion conspicuity. There was no significant difference in contrast-to-noise ratio score and average ADC values between the two DWI sequences. The average ADC values of the two DWI sequences were highest in the normal uterus group and lowest in the endometrial cancer group, with statistically significant differences among groups (P < 0.01). In addition, the average ADC values of the two DWI sequences were negatively correlated with the type of lesions, decreasing with the malignancy of the lesions (r = −0.805 P < 0.01, r = −0.815 P < 0.01). ConclusionCompared to SS-EPI-DWI, MUSE-DWI can significantly reduce distortion, artifacts, and fuzziness in MRI of uterine lesions, which is more conducive to lesion detection.

Cancer Radiomic and Perfusion Imaging Automated Framework: Validation on Musculoskeletal Tumors.

Limitations from commercial software applications prevent the implementation of a robust and cost-efficient high-throughput cancer imaging radiomic feature extraction and perfusion analysis workflow. This study aimed to develop and validate a cancer research computational solution using open-source software for vendor- and sequence-neutral high-throughput image processing and feature extraction. The Cancer Radiomic and Perfusion Imaging (CARPI) automated framework is a Python-based software application that is vendor- and sequence-neutral. CARPI uses contour files generated using an application of the user's choice and performs automated radiomic feature extraction and perfusion analysis. This workflow solution was validated using two clinical data sets, one consisted of 40 pelvic chondrosarcomas and 42 sacral chordomas with a total of 82 patients, and a second data set consisted of 26 patients with undifferentiated pleomorphic sarcoma (UPS) imaged at multiple points during presurgical treatment. Three hundred sixteen volumetric contour files were processed using CARPI. The application automatically extracted 107 radiomic features from multiple magnetic resonance imaging sequences and seven semiquantitative perfusion parameters from time-intensity curves. Statistically significant differences (P < .00047) were found in 18 of 107 radiomic features in chordoma versus chondrosarcoma, including six first-order and 12 high-order features. In UPS postradiation, the apparent diffusion coefficient mean increased 41% in good responders (P = .0017), while firstorder_10Percentile (P = .0312) was statistically significant between good and partial/nonresponders. The CARPI processing of two clinical validation data sets confirmed the software application's ability to differentiate between different types of tumors and help predict patient response to treatment on the basis of radiomic features. Benchmark comparison with five similar open-source solutions demonstrated the advantages of CARPI in the automated perfusion feature extraction, relational database generation, and graphic report export features, although lacking a user-friendly graphical user interface and predictive model building.

Evaluation of cranial nerve involvement in chordomas and chondrosarcomas: a retrospective imaging study.

Cranial nerve involvement (CNI) influences the treatment strategies and prognosis of head and neck tumors. However, its incidence in skull base chordomas and chondrosarcomas remains to be investigated. This study evaluated the imaging features of chordoma and chondrosarcoma, with a focus on the differences in CNI. Forty-two patients (26 and 16 patients with chordomas and chondrosarcomas, respectively) treated at our institution between January 2007 and January 2023 were included in this retrospective study. Imaging features, such as the maximum diameter, tumor location (midline or off-midline), calcification, signal intensity on T2-weighted image, mean apparent diffusion coefficient (ADC) values, contrast enhancement, and CNI, were evaluated and compared using Fisher's exact test or the Mann-Whitney U-test. The odds ratio (OR) was calculated to evaluate the association between the histological type and imaging features. The incidence of CNI in chondrosarcomas was significantly higher than that in chordomas (63% vs. 8%, P < 0.001). An off-midline location was more common in chondrosarcomas than in chordomas (86% vs. 13%; P < 0.001). The mean ADC values of chondrosarcomas were significantly higher than those of chordomas (P < 0.001). Significant associations were identified between chondrosarcomas and CNI (OR = 20.00; P < 0.001), location (OR = 53.70; P < 0.001), and mean ADC values (OR = 1.01; P = 0.002). The incidence of CNI and off-midline location in chondrosarcomas was significantly higher than that in chordomas. CNI, tumor location, and the mean ADC can help distinguish between these entities.

Comparison of magnetic resonance elastography and diffusion-weighted imaging for differentiating benign and malignant liver lesions

BackgroundImaging is a crucial diagnostic tool in focal liver lesions (FLLs) diagnosis. Without the need for an intravenous contrast agent, two such MRI methods that can distinguish between benign and malignant FLLs are diffusion-weighted imaging (DWI) and magnetic resonance elastography (MRE). The purpose of this study was to assess the utility of diffusion-weighted magnetic resonance imaging and magnetic resonance elastography in the identification and differentiation of benign and malignant hepatic focal lesions.MethodsThis cross-sectional study was carried out on ninety patients (with mean age 52 years) with hepatic focal lesions (29 benign and 61 malignant). Both MRE and DWI were performed on the patients. A modified gradient-echo sequence was used for MRE, and respiratory-triggered fat-suppressed single-shot echoplanar DW imaging (b = 0.800) was used for DWI. Maps of the apparent diffusion coefficient (ADC) and stiffness were produced. Regions of interest were placed over the FLLs on stiffness and ADC maps to get FLL ADC values and mean stiffness. Receiver operating curve (ROC) analysis was used to compare the roles of MRE and DWI in the differentiation of benign and malignant FLL.ResultsThe ADC of FLLs and MRE stiffness exhibited strong negative correlation [(r: −0.559; p < 0.001)]. Compared to malignant FLLs, benign FLLs had much higher mean ADC values. However, compared to benign FLLs, malignant FLLs exhibited much greater mean stiffness. FNH has the lowest mean stiffness of all FLLs, at less than 2.22 kPa. Among FLLs, CCAs had the lowest mean ADC values and the highest mean stiffness. The results showed that the MRE and DWI cutoff values were > 4.23 and ≤ 1.43, respectively; the area under the curve (AUC) values were 0.991 and 0.894, and the sensitivity and specificity results were 96.7%, 93.1%, and 85.2%, 89.7%, respectively.ConclusionsMRE was found to be more sensitive method for identifying benign and malignant hepatic focal lesions than DWI.

Prognostic value of preoperative diffusion restriction in glioblastoma.

Although glioblastoma (GBM) has a very poor prognosis, overall survival (OS) in treated patients shows great difference varying from few days to several months. Identifying factors explaining this difference would improve management of patient treatment. To determine the relevance of diffusion restriction in newly diagnosed treatment-naïve GBM patients. Preoperative magnetic resonance scans of 33 patients with GBM were reviewed. Regions of interest including all the T2 hyperintense lesion were drawn on diffusion weighted B0 images and transferred to the apparent diffusion coefficient (ADC) map. For each patient, a histogram displaying the ADC values within in the regions of interest was generated. Volumetric parameters including tumor regions with restricted diffusion, parameters derived from histogram and mean ADC value of the tumor were calculated. Their relationship with OS was analyzed. Patients with mean ADC value < 1415x10-6 mm2/s had a significantly shorter OS (p=0.021). Among volumetric parameters, the percentage of volume within T2 lesion with a normalized ADC value <1.5 times that in white matter was significantly associated with OS (p=0.0045). Patients with a percentage>23.92% had a shorter OS. Among parameters derived from histogram, the 50th percentile showed a trend towards significance for OS (p=0.055) with patients living longer when having higher values of 50th percentile. A difference in OS was observed between patients according to ADC peak of histogram but this difference did not reach statistical significance (p=0.0959). Diffusion magnetic resonance imaging may provide useful information for predicting GBM prognosis.

Improved diagnostic accuracy of readout-segmented echo-planar imaging for peripheral zone clinically significant prostate cancer: a retrospective 3T MRI study

This study compares the readout-segmented echo-planar imaging (rsEPI) from the conventional single-shot EPI (ssEPI) diffusion-weighted imaging (DWI) for the discrimination of patients with clinically significant prostate cancer (csPCa) within the peripheral zone (PZ) using apparent diffusion coefficient (ADC) maps and pathology report from magnetic resonance imaging (MRI)-targeted biopsy. We queried a retrospective monocentric database of patients with targeted biopsy. csPCa patients were defined as an International Society of Urological Pathology grade group ≥ 2. Group-level analyses and diagnostic accuracy of mean ADC values (ADCmean) within the tumor volume were assessed from Kruskal–Wallis tests and receiving operating characteristic curves, respectively. Areas under the curve (AUC) and optimal cut-off values were calculated. 159 patients (105 rsEPI, 54 ssEPI; mean age ± standard deviation: 65 ± 8 years) with 3T DWI, PZ lesions and targeted biopsy were selected. Both DWI sequences showed significantly lower ADCmean values for patients with csPCa. The rsEPI sequence better discriminates patients with csPCa (AUCrsEPI = 0.84, AUCssEPI = 0.68, p < 0.05) with an optimal cut-off value of 1232 μm2/s associated with a sensitivity–specificity of 97%-63%. Our study showed that the rsEPI DWI sequence enhances the discrimination of patients with csPCa.

Can the ADC Value Be Used as an Imaging “Biopsy” in Endometrial Cancer?

Background: The tumor histological grade is closely related to the prognosis of patients with endometrial cancer (EC). Multiparametric MRI, including diffusion-weighted imaging (DWI), provides information about the cellular density that may be useful to differentiate between benign and malignant uterine lesions. However, correlations between apparent diffusion coefficient (ADC) values and histopathological grading in endometrial cancer remain controversial. Material and methods: We retrospectively evaluated 92 patients with endometrial cancers, including both endometrioid adenocarcinomas (64) and non-endometrioid adenocarcinomas (28). All patients underwent DWI procedures, and mean ADC values were calculated in a region of interest. These values were then correlated with the tumor grading offered by the histopathological examination, which was considered the gold standard. In this way, the patients were divided into three groups (G1, G2, and G3). The ADC values were then compared to the results offered by the biopsy to see if the DWI sequence and ADC map could replace this procedure. We also compared the mean ADC values to the myometrial invasion (&lt;/&gt;50%) and lymphovascular space invasion. Results: We have divided the ADC values into three categories corresponding to three grades: &gt;0.850 × 10−3 mm2/s (ADC1), 0.730–0.849 × 10−3 mm2/s (ADC2) and &lt;0.730 × 10−3 mm2/s (ADC3). The diagnostic accuracy of the ADC value was 85.71% for ADC1, 75.76% for ADC2, and 91.66% for ADC3. In 77 cases out of 92, the category in which they were placed using the ADC value corresponded to the result offered by the histopathological exam with an accuracy of 83.69%. For only 56.52% of patients, the biopsy result included the grading system. For each grading category, the mean ADC value showed better results than the biopsy; for G1 patients, the mean ADC value had an accuracy of 85.71% compared to 66.66% in the biopsy, G2 had 75.76% compared to 68.42%, and G3 had 91.66 compared to 75%. For both deep myometrial invasion and lymphovascular space invasion, there is a close, inversely proportional correlation with the mean ADC value. Conclusions: Mean endometrial tumor ADC on MR-DWI is inversely related to the histological grade, deep myometrial invasion and lymphovascular space invasion. Using this method, the patients could be better divided into risk categories for personalized treatment.

Comparison of two region-of-interest placement methods for histogram analysis of apparent diffusion coefficient maps for glioma grading.

We assessed the value of histogram analysis (HA) of apparent diffusion coefficient (ADC) maps for grading low-grade (LGG) and high-grade (HGG) gliomas. We compared the diagnostic performance of two region-of-interest (ROI) placement methods (ROI 1: the entire tumor; ROI 2: the tumor excluding cystic and necrotic portions). We retrospectively evaluated 54 patients with supratentorial gliomas (18 LGG and 36 HGG). All subjects underwent standard 3T contrast-enhanced magnetic resonance imaging. Histogram parameters of ADC maps calculated with the two segmentation methods comprised mean, median, maxi-mum, minimum, kurtosis, skewness, entropy, standard deviation (sd), mean of positive pixels (mpp), uniformity of positive pixels, and their ratios (r) between lesion and normal white matter. They were compared using the independent t-test, chi-square test, or Mann-Whitney U test. For statistically significant results, receiver operating characteristic curves were constructed, and the optimal cutoff value, sensitivity, and specificity were determined by maximizing Youden's index. The ROI 1 method resulted in significantly higher rADC mean, rADC median, and rADC mpp for LGG than for HGG; these parameters had value for predicting the histological glioma grade with a cutoff (sensitivity, specificity) of 1.88 (77.8%, 61.1%), 2.25 (44.4%, 97.2%), and 1.88 (77.8%, 63.9%), respectively. The ROI 2 method resulted in significantly higher ADC mean, ADC median, ADC mpp, ADC sd, ADC max, rADC median, rADC mpp, rADC mean, rADC sd, and rADC max for LGG than for HGG, while skewness was lower for LGG than for HGG (0.27 [0.98] vs 0.91 [0.81], p = 0.014). In ROI 2, ADC median, ADC mpp, ADC mean, rADC median, rADC mpp, and rADC mean performed well in differentiating glioma grade with cutoffs (sensitivity, specificity) of 1.28 (77.8%, 88.9%), 1.28 (77.8%, 88.9%), 1.25 (77.8%, 91.7%), 1.81 (83.3%, 91.7%), 1.74 (83.3%, 91.7%), and 1.81 (83.3%, 91.7%), respectively. HA parameters had value for grading gliomas. Ex-cluding cystic and necrotic portions of the tumor for measuring HA parameters was preferable to using the entire tumor as the ROI. In this segmentation, rADC median showed the highest performance in predicting histological glioma grade, followed by rADC mpp, rADC mean, ADC median, ADC mpp, and ADC mean.