Accuracy Of Apparent Diffusion Coefficient Research Articles

Fully automated detection of prostate transition zone tumors on T2‐weighted and apparent diffusion coefficient (ADC) map MR images using U‐Net ensemble

Accurate detection of transition zone (TZ) prostate cancer (PCa) on magnetic resonance imaging (MRI) remains challenging using clinical subjective assessment due to overlap between PCa and benign prostatic hyperplasia (BPH). The objective of this paper is to describe a deep-learning-based framework for fully automated detection of PCa in the TZ using T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images. This was a single-center IRB-approved cross-sectional study of men undergoing 3T MRI on two systems. The dataset consisted of 196 patients (103 with and 93 without clinically significant [Grade Group 2 or higher] TZ PCa) to train and test our proposed methodology, with an additional 168 patients with peripheral zone PCa used only for training. We proposed an ensemble of classifiers in which multiple U-Net-based models are designed for prediction of TZ PCa location on ADC map MR images, with initial automated segmentation of the prostate to guide detection. We compared accuracy of ADC alone to T2W and combined ADC+T2W MRI for input images, and investigated improvements using ensembles over their constituent models with different methods of diversity in individual models by hyperparameter configuration, loss function and model architecture. Our developed algorithm reported sensitivity and precision of 0.829 and 0.617 in 56 test cases containing 31 instances of TZ PCa and in 25 patients without clinically significant TZ tumors. Patient-wise classification accuracy had an area under receiver operator characteristic curve (AUROC) of 0.974. Single U-Net models using ADC alone (sensitivity 0.829, precision 0.534) outperformed assessment using T2W (sensitivity 0.086, precision 0.081) and assessment using combined ADC+T2W (sensitivity 0.687, precision 0.489). While the ensemble of U-Nets with varying hyperparameters demonstrated the highest performance, all ensembles improved PCa detection compared to individual models, with sensitivities and precisions close to the collective best of constituent models. We describe a deep-learning-based method for fully automated TZ PCa detection using ADC map MR images that outperformed assessment by T2W and ADC+T2W.

Diagnostic accuracy of apparent diffusion coefficient (ADC) value in differentiating malignant from benign solid liver lesions: a systematic review and meta-analysis.

We undertook a systematic review and meta-analysis of the diagnostic performance of mean apparent diffusion coefficient (ADC) values derived by diffusion-weighted (DW)-MRI in the characterization of solid benign and malignant liver lesions, and to assess their value in discriminating these lesions in daily routine practice. A systematic review of PubMed, Embase, Scopus, and Web of Science was conducted to retrieve studies that used ADC values for differentiating solid benign/dysplastic nodules and malignant liver lesions. A bivariate random-effects model with pooled sensitivity and specificity values with 95% CI (confidence interval) was used. This meta-analysis was performed on the per-lesion basis. Summary receiver operating characteristic (SROC) plot and area under curve (AUC) were created. A total of 14 original articles were retrieved. The combined (95% CI) sensitivity and specificity of mean ADC values for differentiating solid benign from malignant lesions were 78% (67-86%) and 74% (64-81%), respectively. The pooled (95% CI) positive and negative LRs were respectively 3 (2.3-3.8) and 0.3 (0.21-0.43). The DOR (95% CI) was 10 (7-15). The AUC (95% CI) of the SROC plot was 82% (78-85%). Reporting bias was negligible (p value of regression test = 0.36). Mean size of malignant lesions and breathing pattern of MRI were found to be sources of heterogeneity of pooled sensitivity. ADC measurement independently may not be an optimal diagnostic imaging method for differentiating solid malignant from solid benign hepatic lesions. The meta-analysis showed that ADC measurement had moderate diagnostic accuracy for characterizing solid liver lesions. Further prospective and comparative studies with pre-specified ADC thresholds could be performed to investigate the best MRI protocol and ADC threshold for characterizing solid liver lesions. ADC measurement by DW-MRI does not have a good diagnostic performance to differentiate solid malignant from solid benign lesions. Therefore, we suggest not using ADC values in clinical practice to evaluate solid liver lesions.

Respiratory triggered diffusion-weighted imaging with a single diffusion sensitising gradient to reduce image acquisition time – A feasibility study in the workup of hepatocellular carcinoma

PurposeWe evaluated respiratory triggered unidirectional single-shot echo-planar imaging (u-SSEPI) as a time-saving measure in diffusion imaging of the upper abdomen. Specifically, we compared the ADC values obtained from unidirectional DWI (u-SSEPI) and routine DWI (4t-SSEPI) and also the diagnostic accuracies of unidirectional and routine DWI sequences in the identification of focal liver lesions in the setting of chronic liver disease (CLD). Materials and methodsThis prospective, IRB approved study, included 48 patients of CLD who underwent-DCE-MRI on a 1.5 T scanner for hepatocellular carcinoma (HCC) workup. In addition to 4t-SSEPI, u-SSEPI was acquired with the diffusion sensitising gradient being applied in only one direction; keeping all other parameters same as 4t-SSEPI. Two blinded radiologists evaluated the DWI studies for image quality and detection of liver lesions. A composite gold standard was established using DCE-MRI, follow-up imaging and patient clinical details. The apparent diffusion coefficients (ADCs) of the liver, spleen and the lesions were compared between the two sequences. ROC analysis evaluated the diagnostic accuracy of ADC from both the sequences in identifying HCC. ResultsEighty-eight lesions were identified using the composite gold standard. u-SSEPI resulted in 3 times faster image acquisition. No statistically significant differences were demonstrated between the unidirectional and routine DWI sequences for image quality parameters and lesion detection rates. Lesion wise comparison of the ADC values from both the sequences was not statistically different (p = 0.8) with a coefficient of variation = 12–14 %. The Bland- Altman plots and the Passing-Bablock regression analysis demonstrated a systematic and proportional bias between the ADC values obtained. The AUC of the ROC curve, however, was 0.63–observer1; 0.62–oobserver2 for routine DWI and 0.65; 0.62 for unidirectional DWI when ADC was used to identify HCC (the AUCs were not statistically different (p = 0.6−0.8)). ConclusionNo significant differences were demonstrated in the diagnostic accuracies of unidirectional and routine DWI in the diagnosis of HCC. Unidirectional diffusion may be further evaluated in other organs where diffusion is isotropic, especially in respiratory triggered sequences where the imaging time dividend is significant.

Empirical validation of gradient field models for an accurate ADC measured on clinical 3T MR systems in body oncologic applications.

To empirically corroborate vendor-provided gradient nonlinearity (GNL) characteristics and demonstrate efficient GNL bias correction for human brain apparent diffusion coefficient (ADC) across 3T MR systems and spatial locations. Spatial distortion vector fields (DVF) were mapped in 3D using a surface fiducial array phantom for individual gradient channels on three 3T MR platforms from different vendors. Measured DVF were converted into empirical 3D GNL tensors and compared with their theoretical counterparts derived from vendor-provided spherical harmonic (SPH) coefficients. To illustrate spatial impact of GNL on ADC, diffusion weighted imaging using three orthogonal gradient directions was performed on a volunteer brain positioned at isocenter (as a reference) and offset superiorly by 10-17cm (>10% predicted GNL bias). The SPH tensor-based GNL correction was applied to individual DWI gradient directions, and derived ADC was compared with low-bias reference for human brain white matter (WM) ROIs. Empiric and predicted GNL errors were comparable for all three studied 3T MR systems, with <1.0% differences in the median and width of spatial histograms for individual GNL tensor elements. Median (±width) of ADC (10-3mm2/s) histograms measured at isocenter in WM reference ROIs from three MR systems were: 0.73±0.11, 0.71±0.14, 0.74±0.17, and at off-isocenters (before versus after GNL correction) were respectively 0.63±0.14 versus 0.72±0.11, 0.53±0.16 versus 0.74±0.18, and 0.65±0.16 versus 0.76±0.18. The phantom-based spatial distortion measurements validated vendor-provided gradient fields, and accurate WM ADC was recovered regardless of spatial locations and clinical MR platforms using system-specific tensor-based GNL correction for routine DWI.

On the dependence of quantitative diffusion-weighted imaging on scanner system characteristics and acquisition parameters: A large multicenter and multiparametric phantom study with unsupervised clustering analysis.

The purpose of this multicenter phantom study was to exploit an innovative approach, based on an extensive acquisition protocol and unsupervised clustering analysis, in order to assess any potential bias in apparent diffusion coefficient (ADC) estimation due to different scanner characteristics. Moreover, we aimed at assessing, for the first time, any effect of acquisition plan/phase encoding direction on ADC estimation. Water phantom acquisitions were carried out on 39 scanners. DWI acquisitions (b-value=0-200-400-600-800-1000s/mm2) with different acquisition plans (axial, coronal, sagittal) and phase encoding directions (anterior/posterior and right/left, for the axial acquisition plan), for 3 orthogonal diffusion weighting gradient directions, were performed. For each acquisition setup, ADC values were measured in-center and off-center (6 different positions), resulting in an entire dataset of 84×39=3276 ADC values. Spatial uniformity of ADC maps was assessed by means of the percentage difference between off-center and in-center ADC values (Δ). No significant dependence of in-center ADC values on acquisition plan/phase encoding direction was found. Ward unsupervised clustering analysis showed 3 distinct clusters of scanners and an association between Δ-values and manufacturer/model, whereas no association between Δ-values and maximum gradient strength, slew rate or static magnetic field strength was revealed. Several acquisition setups showed significant differences among groups, indicating the introduction of different biases in ADC estimation. Unsupervised clustering analysis of DWI data, obtained from several scanners using an extensive acquisition protocol, allows to reveal an association between measured ADC values and manufacturer/model of scanner, as well as to identify suboptimal DWI acquisition setups for accurate ADC estimation.

Apparent diffusion coefficient measurement using thin-slice diffusion-weighted magnetic resonance imaging: assessment of measurement errors and repeatability.

We investigated the measurement error and repeatability of the apparent diffusion coefficient (ADC) obtained using thin-slice imaging. Diffusion-weighted images of an ice-water phantom were acquired using 1.5-T and 3.0-T scanners with 1-, 3-, and 5-mm thickness. ADC maps were generated at b = 0 and 1000mm2/s using five consecutive scans. Measurement errors were assessed with accuracy and precision. Repeatability was assessed using the within-subject coefficient of variation. The ADC accuracy of both scanners agreed with the ADC of water at 0°C. At 1-mm, precisions were 2.9% and 8.4% for the 3.0-T and 1.5-T scanners, respectively. The repeatabilities of 1-mm thickness were 1.3% and 3.4% in the 3.0-T and 1.5-T scanners, respectively. The 3.0-T scanner showed acceptable measurement errors and moderate repeatability compared with Quantitative Imaging Biomarkers Alliance recommendation. A 3.0-T scanner can be used for reliable ADC measurement, even with a 1-mm thickness at a reasonable scan time.

Brain Abscess Apparent Diffusion Coefficient is Associated With Microbial Culture Yields.

Obtaining pus for microbial cultures is one of the surgical aims in patients with brain abscess. Predictors of microbial yields are necessary as they help in treatment planning. To investigate the relationship between microbial culture yields of brain abscesses and their apparent diffusion coefficient (ADC) values and clinical characteristics. Retrospective. Eighty-four patients diagnosed with brain abscess by surgery and histopathology (59 with positive abscess cultures). Diffusion-weighted, T2-weigthed, and contrast-enhanced T1-weighted imaging at 1.5 T and 3 T. Contrast-enhanced T1-weighted images were co-registered to ADC maps. Three neuroradiologists determined abscess imaging characteristics (distribution, location, and ventricular rupture), and two measured abscess volumes and ADC values. Clinical characteristics collected included sex, age, fever, underlying diseases, infection sources, white blood cell count, percentage of segmented neutrophils, C-reactive protein level, regimen and duration of empirical antibiotics, and types of surgery. Interobserver differences were assessed with Fleiss kappa and intraclass correlation coefficients. The differences in clinical and imaging factors between the positive and negative culture groups were compared with Chi-square analysis or Student's t test. All factors were subjected to multivariable logistic regression analysis to assess their associations with microbial culture yields, and factors with statistical significance were evaluated with receiver operating characteristic curve analysis to assess their diagnostic performance in discriminating the two groups. Mean ADC (×10-6 mm2 /s) of culture-negative abscesses (841 ± 173) was significantly higher (P < 0.05) than that of culture-positive abscesses (536 ± 90). On multivariable analysis, mean ADC was the only significant factor (P < 0.05) related to culture yields. With 660 as the cutoff value, the sensitivity, specificity, and accuracy of ADC for discriminating culture yields were 93.2%, 88.0%, and 91.7%, respectively. ADC could be used to discriminate between culture-positive and culture-negative abscesses. 4 TECHNICAL EFFICACY: Stage 2.

Quantitative 3-tesla multiparametric MRI in differentiation between renal cell carcinoma subtypes

BackgroundMRI provides several distinct quantitative parameters that may better differentiate renal cell carcinoma (RCC) subtypes. The purpose of the study is to evaluate the diagnostic accuracy of apparent diffusion coefficient (ADC), chemical shift signal intensity index (SII), and contrast enhancement in differentiation between different subtypes of renal cell carcinoma.ResultsThere were 63 RCC as regard surgical histopathological analysis: 43 clear cell (ccRCC), 12 papillary (pRCC), and 8 chromophobe (cbRCC). The mean ADC ratio for ccRCC (0.75 ± 0.13) was significantly higher than that of pRCC (0.46 ± 0.12, P < 0.001) and cbRCC (0.41 ± 0.15, P < 0.001). The mean ADC value for ccRCC (1.56 ± 0.27 × 10−3 mm2/s) was significantly higher than that of pRCC (0.96 ± 0.25 × 10−3 mm2/s, P < 0.001) and cbRCC (0.89 ± 0.29 × 10−3 mm2/s, P < 0.001). The mean SII of pRCC (1.49 ± 0.04) was significantly higher than that of ccRCC (0.93 ± 0.01, P < 0.001) and cbRCC (1.01 ± 0.16, P < 0.001). The ccRCC absolute corticomedullary enhancement (196.7 ± 81.6) was significantly greater than that of cbRCC (177.8 ± 77.7, P < 0.001) and pRCC (164.3 ± 84.6, P < 0.001).ConclusionOur study demonstrated that multiparametric MRI is able to afford some quantitative features such as ADC ratio, SII, and absolute corticomedullary enhancement which can be used to accurately distinguish different subtypes of renal cell carcinoma.

Differentiating malignant from benign salivary gland lesions: a multiparametric non-contrast MR imaging approach

The purpose of this study is to determine whether multiparametric non-contrast MR imaging including diffusion-weighted imaging (DWI), arterial spin labeling (ASL), and amide proton transfer (APT) weighted imaging can help differentiate malignant from benign salivary gland lesions. The study population consisted of 42 patients, with 31 benign and 11 malignant salivary gland lesions. All patients were evaluated using DWI, three-dimensional pseudo-continuous ASL, and APT-weighted imaging on 3 T MR imaging before treatment. Apparent diffusion coefficient (ADC), tumor blood flow (TBF), and APT-related signal intensity (APTSI) values within the lesion were compared between the malignant and benign lesions by Mann–Whitney U test. For each parameter, optimal cutoff values were chosen using a threshold criterion that maximized the Youden index for predicting malignant lesions. The performance of ADC, TBF, APTSI, individually and combined, was evaluated in terms of diagnostic ability for malignant lesions. Diagnostic performance was compared by McNemar test. APTSI was significantly higher in malignant lesions (2.18 ± 0.89%) than in benign lesions (1.57 ± 1.09%, p = 0.047). There was no significant difference in ADC or TBF between benign and malignant lesions (p = 0.155 and 0.498, respectively). The accuracy of ADC, TBF, and APTSI for diagnosing malignant lesions was 47.6%, 50.0%, and 66.7%, respectively; whereas the accuracy of the three parameters combined was 85.7%, which was significantly higher than that of each parameter alone (p = 0.001, 0.001, and 0.008, respectively). Therefore, the combination of ADC, TBF, and APTSI can help differentiate malignant from benign salivary gland lesions.

Accuracy of apparent diffusion coefficient in differentiation of glioblastoma from metastasis.

Brain metastasis and glioblastoma multiforme are two of the most common malignant brain neoplasms. There are many difficulties in distinguishing these diseases from each other. The purpose of this study was to determine whether the mean apparent diffusion coefficient and absolute standard deviation derived from apparent diffusion coefficient measurements can be used to differentiate glioblastoma multiforme from brain metastasis based on cellularity levels. Magnetic resonance images of 34 patients with histologically verified brain tumors were evaluated retrospectively. Apparent diffusion coefficient and standard deviation values were measured in the enhancing tumor, peritumoral region, and contralateral healthy white matter. Then, to determine whether there was a statistical difference between brain metastasis and glioblastoma multiforme, we analyzed different variables between the two groups. Neither mean apparent diffusion coefficient values and ratios nor standard deviation values and ratios were significantly different between glioblastoma multiforme and brain metastasis. Receiver operating characteristic curve analysis of the logistic model with backward stepwise feature selection yielded an area under the curve of 0.77, a specificity of 84%, a sensitivity of 67%, a positive predictive value of 83.33%, and a negative predictive value of 78.26% for distinguishing between glioblastoma multiforme and brain metastasis. The absolute standard deviation and standard deviation ratios were significantly higher in the peritumoral edema compared to the tumor region in each case. Apparent diffusion coefficient values and ratios, as well as standard deviation values and ratios in peritumoral edema, cannot be used to differentiate edema with infiltration of tumor cells from vasogenic edema. However, standard deviation values could successfully characterize areas of peritumoral edema from the tumoral region in each case.

Role of Apparent Diffusion Coefficient in Characterisation of Neck Masses- A Cross-sectional Study

Introduction: Neck masses are frequently encountered in clinical practice and differential diagnoses of a patient presenting with a neck mass is wide. Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is an imaging technique based on molecular diffusion. It is a very helpful complementary technique in distinguishing neoplastic from non neoplastic tissue and has numerous applications in the evaluation of head and neck masses especially in head and neck lymphadenopathy. Aim: To evaluate the accuracy of Apparent Diffusion Coefficient (ADC) in differentiating benign and malignant neck masses. Materials and Methods: A single-centre cross-sectional study comprising of 50 patients of all age groups with clinical suspicion of neck swelling was conducted in Department of Radiodiagnosis at Sri Guru Ram Das Institute of Medical Sciences and Research, Amritsar, Punjab, India from March 2018 to February 2020. All patients included were subjected to general physical and detailed local examination. MRI was carried out on 1.5 Tesla Unit On Philips Intera Achieva using Sense Body Coil with b factor of 0 and 1000 s/mm² and ADC maps were generated and the results were compared with histopathological results and/or follow-up. Statistical assessment was carried out using Statistical Package for the Social Sciences (SPSS) version 24.0. Qualitative data were compared using the Chi-square test. Results: The most common cause of neck mass was lymph nodal masses (18%) with highest numbers of patients in age group 40-60 years (34%). When a cut-off ADC value of 1.1×10-3 s/ mm² was used, sensitivity of 95.23% and specificity of 89.65% were obtained for characterisation of malignant lesions which was statistically significant. There was a strong agreement (k value=0.838) in diagnosing the nature of lesion using ADC value on MRI with histopathological findings. Conclusion: Diffusion Weighted Imaging (DWI) and ADC values are new promising aspect of MRI and can help in differentiating the nature of the neck lesions. DWI can readily differentiate between benign and malignant lesions of neck with good efficacy and reproducibility.

The role of MRI in evaluation of cervical carcinoma

ObjectiveTo review the role of MRI in the diagnosis and staging of cervical carcinoma.BackgroundIt is widely accepted that MRI is the preferred imaging modality for detection of cervical carcinoma nowadays; different techniques are needed for early detection of the disease, its correct staging, and treatment.Patients and methodsThis case–control study included 62 patients divided into a case group of 32 patients diagnosed clinically or by transvaginal ultrasound with cervical cancer, confirmed by biopsy, and a control group of 30 patients who had pelvic MRI for other reasons. All patients were assessed by history and review of previous ultrasound reports and underwent pelvic MRI with diffusion-weighted MRI and dynamic contrast-enhanced MRI.ResultsThis study revealed a statistically significant difference between the case and control groups regarding apparent diffusion coefficient (ADC) value (P < 0.001). There was no significant difference in ADC value between different subtypes of cervical cancer. The diagnostic accuracy of ADC in discrimination of patients and control in defining the best cutoff value of ADC (×10–3), which was 1.10, was 99.5%, with sensitivity of 96.6% and specificity of 96.9%.ConclusionDiffusion-weighted imaging is a potentially useful adjunct to conventional MRI in evaluating cervical carcinoma, thus improving overall diagnostic accuracy, tumor staging, prediction of response to therapy, and treatment follow-up. ADC values may vary not only with different imaging parameters but also with different types of MRI systems.

Comparison and evaluation of distortion correction techniques on an MR-guided radiotherapy system.

To evaluate two distortion correction techniques for diffusion-weighted single-shot echo-planar imaging (DW-ssEPI) on a 0.35T magnetic resonance-guided radiotherapy (MRgRT) system. The effects of sequence optimization through enabling parallel imaging (PI) and selecting appropriate bandwidth on spatial distortion were first evaluated on the 0.35T MRgRT system using a spatial integrity phantom. Field map (FM) and reversed gradient (RG) corrections were then performed on the optimized protocol to further reduce distortion. An open-source toolbox was used to quantify the spatial displacement before and after distortion correction. To evaluate ADC accuracy and repeatability of the optimized protocol, as well as impacts of distortion correction on ADC values, the optimized protocol was scanned twice on a diffusion phantom. The calculated ADC values were compared with reference ADCs using paired t-test. Intraclass correlation coefficient (ICC) between the two repetitions, as well as between before and after FM/RG correction was calculated to evaluate ADC repeatability and effects of distortion correction. Six patients were recruited to assess the in-vivo performance. The optimal distortion correction technique was identified by visual assessment. To quantify distortion reduction, tumor and critical structures were contoured on the turbo spin echo (TSE) image (reference image), the DW-ssEPI image, and the distortion corrected images independently by two radiation oncologists. Mean distance to agreement (MDA) and DICE coefficient between contours on the reference images and the diffusion images were calculated. Tumor apparent diffusion coefficient (ADC) values from the original DW-ssEPI images and the distortion corrected images were compared using Bland-Altman analysis. Sequence optimization played a vital role in improving the spatial integrity, and spatial distortion was proportional to the total readout time. Before the correction, distortion of the optimized protocol (PI and high bandwidth) was 1.50±0.89mm in a 100mm radius and 2.21±1.39mm in a 175mm radius for the central plane. FM corrections reduced the distortions to 0.42±0.27mm and 0.67±0.49mm respectively, and RG reduced distortion to 0.40±0.22mm and 0.64±0.47mm, respectively. The optimized protocol provided accurate and repeatable ADC quantification on the diffusion phantom. The calculated ADC values were consistent before and after FM/RG correction. For the patient study, the FM correction was unable to reduce chemical shift artifacts whereas the RG method successfully mitigated the chemical shift. MDA reduced from 2.52±1.29mm to 1.11±0.72mm after the RG correction. The DICE coefficient increased from 0.80±0.13 to 0.91±0.06. A Bland-Altman plot showed that there was a good agreement between ADC measurements before and after application of the RG correction. Two distortion correction techniques were evaluated on a commercial low-field MRgRT system. Overall, the RG correction was able to drastically improve spatial distortion and preserve ADC accuracy.

Evaluation of DWI and ADC Sequences' Diagnostic Values in Benign and Malignant Pulmonary Lesions.

The gold standard for the diagnosis of lung cancer is conducting a histopathologic study. It is also diagnosed based on some features of a computed tomography (CT) scan. Imposed radiation is a prominent side effect of a CT scan. Diffusion-weighted imaging (DWI) apparent diffusion coefficient (ADC) images have currently been used in the diagnosis of different lesions, including those of the brain and breast, and their uses in lung lesions are being evaluated. In this study, to find a safe, sensitive, and specific method, we aimed to assess DWI imaging to replace the CT scan and the positron emission tomography scan. A total of 29 patients were enrolled in the study. In b800 images in DWI, spinal cord and lesion signals were measured, and the lesion-to-cord-signal ratio (LCR) was calculated. The ADC value was measured in a quantitative way. Lesions were also graded qualitatively in b800 DWI sequences. There was a significant difference between malignant and benign lesions in terms of DWI grading in b800 images (p<0.001). There was a significant difference between ADC means of a malignant and benign lesion (p=0.003). The mean LCR for malignant lung lesions was significantly higher than that of the benign ones (p<0.001). Considering Grade 3 as the cutoff in DWI grading results in sensitivity, specificity, and accuracy of 89%, 90%, and 89.6%, respectively. For ADC values, sensitivity, specificity, and accuracy of 79%, 80%, and 79.3%, respectively, were obtained when the cutoff was 1.027×10-3 sec/mm2. The sensitivity of 84%, the specificity of 90%, and the accuracy of 86.2% were calculated for the LCR in a cutoff of 0.983. In this study, all three parameters had an area under the curve of ≥0.8, meaning that these variables were valuable for the differentiation of benign and malignant lesions. Diffusion-weighted magnetic resonance imaging is a noninvasive tool, with no contrast agent and requiring ionizing radiations, which could be used for the qualitative, quantitative, and semiquantitative assessment of pulmonary lesions.

Apparent Diffusion Coefficient Values for Neuroendocrine Liver Metastases

We aimed to investigate whether there are any differences in apparent diffusion coefficient (ADC) values obtained from liver metastases due to gastroenteropancreatic neuroendocrine tumors (GEP-NET) and adenocarcinomas. We included 54 patients with 167 liver metastases due to gastroenteropancreatic tumors. We divided the patients into two groups as liver metastases due to GEP-NETs (seven patients with 51 lesions, mean age: 48) and adenocarcinomas (47 patients with 116 lesions, mean age: 61.2). We used the independent samples t-test to compare the ADC and ADCmean values of the two groups and performed a receiver-operating characteristic analysis. ADC and ADCmean values were significantly lower in the GEP-NET group compared with the adenocarcinoma group. Receiver-operating characteristic curve analysis showed a significant difference for ADC and ADCmean values, and area under the curve values were 0.733 and 0.790, respectively. The cut-off values were 933x10-6 mm2/s for ADC and 801x10-6 mm2/s for ADCmean. Diagnostic accuracies of ADC (Sensitivity = 80.2, Specificity = 64.7, PPV = 83.8, NPV = 58.9) and ADCmean (Sensitivity = 63.8, Specificity = 82.4, PPV = 89.2, NPV = 50) were calculated in differentiating adenocarcinoma metastases from GEP-NET metastases. The lower ADC and ADCmean values of liver metastases suggest GEP-NET rather than adenocarcinomas. ADC and ADCmean values obtained from liver metastases may be used to differentiate NETs from adenocarcinomas.

ADC measurements on the Unity MR-linac – A recommendation on behalf of the Elekta Unity MR-linac consortium

Background and purposeDiffusion-weighted imaging (DWI) for treatment response monitoring is feasible on hybrid magnetic resonance linear accelerator (MR-linac) systems. The MRI scanner of the Elekta Unity system has an adjusted design compared to diagnostic scanners. We investigated its impact on measuring the DWI-derived apparent diffusion coefficient (ADC) regarding three aspects: the choice of b-values, the spatial variation of the ADC, and scanning during radiation treatment. The aim of this study is to give recommendations for accurate ADC measurements on Unity systems. Materials and methodsSignal-to-noise ratio (SNR) measurements with increasing b-values were done to determine the highest bvalue that can be measured reliably. The spatial variation of the ADC was assessed on six Unity systems with a cylindrical phantom of 40 cm diameter. The influence of gantry rotation and irradiation was investigated by acquiring DWI images before and during treatment of 11 prostate cancer patients. ResultsOn the Unity system, a maximum b-value of 500 s/mm2 should be used for ADC quantification, as a trade-off between SNR and diffusion weighting. Accurate ADC values were obtained within 7 cm from the iso-center, while outside this region ADC values deviated more than 5%. The ADC was not influenced by the rotating linac or irradiation during treatment. ConclusionWe provide Unity system specific recommendations for measuring the ADC. This will increase the consistency of ADC values acquired in different centers on the Unity system, enabling large cohort studies for biomarker discovery and treatment response monitoring.

3D isotropic resolution diffusion-prepared magnitude-stabilized bSSFP imaging with high geometric fidelity at 1.5 Tesla.

MRI has been increasingly used in radiation therapy to facilitate tumor and organ delineation and assess treatment response. Diffusion MRI can provide cellularity information and may enable functional-based treatment planning and adaptation. However, strong distortion associated with the conventional diffusion-weighted single-shot echo-planar imaging (DW-ssEPI) sequence is problematic for accurate target delineation. The goal of this work is to propose a 3D diffusion sequence with minimal distortion for radiation therapy applications. A 3D diffusion-prepared magnitude-stabilized balanced steady-state free precession sequence (DP-MS-bSSFP) was developed. A 2D navigator was acquired during the linear catalyzation stage of the bSSFP readout to estimate the phase, which was then used in a plane-by-plane low-rank constrained reconstruction to correct the shot-to-shot k-space inconsistency. A diffusion phantom was scanned to evaluate and compare the geometric reliability and apparent diffusion coefficient (ADC) accuracy with the conventional DW-ssEPI. Eight landmarks were selected on each slice of the images to calculate the target registration error (TRE), which was used as a surrogate for geometric fidelity. The phantom was scanned under both 0℃ and room temperature. Brain scans were performed on five healthy volunteers. In the first volunteer, protocols of 1, 2, and 4 shots per Kz plane were compared. In vivo geometric fidelity and ADC accuracy were evaluated on the remaining four volunteers using the protocol of four shots per Kz plane. In the geometric fidelity study, 8-10 landmarks were picked on each slice to calculate the TRE. Regions of interest were placed on the white matter, the cerebellum, and the cerebrospinal fluid region to evaluate the ADC agreement between DW-ssEPI and DP-MS using the Bland-Altman plot. All scans were performed at 1.5mm isotropic resolution to meet the high-resolution requirement of many radiotherapy applications. The DP-MS had drastically improved geometric accuracy compared with DW-ssEPI on the phantom. The mean TRE decreased from 2.09mm to 0.70mm. The percentage difference of the ADC values between the two diffusion sequences were less than 5.5% and 7% for the 0℃ and room temperature study, respectively. The DW-ssEPI had strong distortion and susceptibility-related artifacts at tissue air boundary, whereas distortion was minimal in DP-MS images. Overall, the mean/max TRE was over 2mm/7mm in the volunteers for DW-ssEPI, whereas less than 0.8mm/2mm for DP-MS. Good ADC agreement was observed for the white matter, the cerebellum, and the CSF based on the Bland-Altman plots. A 3D diffusion sequence was developed and validated. It provided high-resolution diffusion imaging with mean distortion less than 1mm at 1.5T, and is a promising imaging technique for treatment planning and adaptive radiotherapy.

Differentiating Benign from Suspicious Vertebral Marrow Lesions Detected with Conventional Magnetic Resonance Imaging Using Apparent Diffusion Coefficient and Diffusion-Weighted Image

BACKGROUND: Advanced magnetic resonance imaging (MRI) sequences, include the apparent diffusion coefficient (ADC) and diffusion-weighted imaging (DWI), are of great benefit in assessing troublesome vertebral marrow lesion in patients presented with primary malignancy outside the spines.&#x0D; OBJECTIVES: The aim of the study was to evaluate the role of vertebral column MRI in characterization and evaluation of marrow lesion in patients with primary malignancy utilizing ADC and DWI in addition to conventional MRI sequences.&#x0D; METHODS: This is a cross-sectional study which includes 80 patients referred to MR unit 58 of them referred from the oncology department in oncology teaching hospital having primary cancer outside the spine and also referred complaining of back pain and worry from metastasis while the remaining 22 are cancer-free patients referred to MRI because of lower back pain – the study was performed in Medical City, Baghdad, Iraq, during the period from the beginning of September 2018–October 2019.&#x0D; RESULTS: The study sample consists of 55 females and 25 males, the breast cancer is main primary in females and represents 30/58 patients, while the prostate is the main cancer in males and represents 7/58 patients. When correlating the MR sequences (T1W, Fat sat and DWI) all together and after addition of ADC value (&lt;9x10-3 mm2/sec) in diagnosis of malignancy there is decreasing frequency of positive cases from 39% before ADC to 22.4% after adding it which is close to histopathology results (17% ). The sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of ADC in detecting metastasis within the vertebral marrow are 94%, 95%, 88.9%, 97.5%, and 94.8%, respectively.&#x0D; CONCLUSION: Conventional MRI using standard T1W, T2 weighted, and fat suppression sequences cannot discriminate between benign and pathological vertebral marrow lesions. Using DWI improves the recognition of pathological bony lesion(s) and this is strengthened when enforced by ADC value and by that, it can replace the need for intravenous contrast administration. DWI and ADC are beneficial in follow-up of previously detected restricted lesion and in assessment its response to treatment that was depicted by measuring its ADC that consequently elevated in healing phase.

Diagnostic test accuracy of ADC values for identification of clear cell renal cell carcinoma: systematic review and meta-analysis

To perform a systematic review on apparent diffusion coefficient (ADC) values of renal tumor subtypes and meta-analysis on the diagnostic performance of ADC for differentiation of localized clear cell renal cell carcinoma (ccRCC) from other renal tumor types. Medline, Embase, and the Cochrane Library databases were searched for studies published until May 1, 2019, that reported ADC values of renal tumors. Methodological quality was evaluated. For the meta-analysis on diagnostic test accuracy of ADC for differentiation of ccRCC from other renal lesions, we applied a bivariate random-effects model and compared two subgroups of ADC measurement with vs. without cystic and necrotic areas. We included 48 studies (2588 lesions) in the systematic review and 13 studies (1126 lesions) in the meta-analysis. There was no significant difference in ADC of renal parenchyma using b values of 0-800 vs. 0-1000 (p = 0.08). ADC measured on selected portions (sADC) excluding cystic and necrotic areas differed significantly from whole-lesion ADC (wADC) (p = 0.002). Compared to ccRCC, minimal-fat angiomyolipoma, papillary RCC, and chromophobe RCC showed significantly lower sADC while oncocytoma exhibited higher sADC. Summary estimates of sensitivity and specificity to differentiate ccRCC from other tumors were 80% (95% CI, 0.76-0.88) and 78% (95% CI, 0.64-0.89), respectively, for sADC and 77% (95% CI, 0.59-0.90) and 77% (95% CI, 0.69-0.86) for wADC. sADC offered a higher area under the receiver operating characteristic curve than wADC (0.852 vs. 0.785, p = 0.02). ADC values of kidney tumors that exclude cystic or necrotic areas more accurately differentiate ccRCC from other renal tumor types than whole-lesion ADC values. • Selective ADC of renal tumors, excluding cystic and necrotic areas, provides better discriminatory ability than whole-lesion ADC to differentiate clear cell RCC from other renal lesions, with area under the receiver operating characteristic curve (AUC) of 0.852 vs. 0.785, respectively (p = 0.02). • Selective ADC of renal masses provides moderate sensitivity and specificity of 80% and 78%, respectively, for differentiation of clear cell renal cell carcinoma (RCC) from papillary RCC, chromophobe RCC, oncocytoma, and minimal-fat angiomyolipoma. • Selective ADC excluding cystic and necrotic areas are preferable to whole-lesion ADC as an additional tool to multiphasic MRI to differentiate clear cell RCC from other renal lesions whether the highest b value is 800 or 1000.

Accuracy of ADC derived from DWI for differentiating high-grade from low-grade gliomas: Systematic review and meta-analysis.

Objective:Quantitative apparent diffusion coefficient (ADC) values of diffusion weighted imaging (DWI) could be applied to grade gliomas. This meta-analysis was conducted to assess the accuracy of ADC analysis in differentiating high-grade (HGGs) from low-grade gliomas (LGGs).Methods:PubMed, Cochrane library, Science Direct, and Embase were searched to identify suitable studies up to September 1, 2018. The quality of studies was evaluated by the quality assessment of diagnostic accuracy studies (QUADAS 2). We estimated the pooled sensitivity, specificity, positive and negative likelihood ratios (LR), diagnostic accuracy ratio (DOR) with 95% confidence intervals (CI), and determined the accuracy of the data by using the summary receiver operating characteristic (SROC) and calculating the area under the curve (AUC) to identity the accuracy of ADC analysis in grading gliomas.Results:Eighteen studies including 1172 patients were included and analyzed. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC with 95% CIs of DWI with b values of 1000 s/mm2 for separating HGGs from LGGs were 0.81 (95% CI 0.75–0.86), 0.87 (95% CI 0.81–0.91), 6.1 (95% CI 4.2–8.9), 0.22 (95% CI 0.17–0.29), 28 (95% CI 17–45), and 0.91 (95% CI 0.88–0.93), respectively. DWI with b values of 3000 s/mm2 showed slightly higher accuracy than that of 1000 (sensitivity 0.80, specificity 0.90 and AUC 0.92). Meta-regression analyses showed that field strengths and b values had significant impacts on diagnostic efficacy. Deeks testing confirmed no significant publication bias in all studies.Conclusions:This meta-analysis suggested that ADC analysis of DWI have high accuracy in differentiating HGGs from LGGs. Standardized methodology is warranted to guide the use of this technique for clinical decision-making.