Dataset Information

Diffusion-weighted (DW) MRI in lung cancers: ADC test-retest repeatability.

ABSTRACT: PURPOSE:To determine the test-retest repeatability of Apparent Diffusion Coefficient (ADC) measurements across institutions and MRI vendors, plus investigate the effect of post-processing methodology on measurement precision. METHODS:Thirty malignant lung lesions >2 cm in size (23 patients) were scanned on two occasions, using echo-planar-Diffusion-Weighted (DW)-MRI to derive whole-tumour ADC (b?=?100, 500 and 800smm-2). Scanning was performed at 4 institutions (3 MRI vendors). Whole-tumour volumes-of-interest were copied from first visit onto second visit images and from one post-processing platform to an open-source platform, to assess ADC repeatability and cross-platform reproducibility. RESULTS:Whole-tumour ADC values ranged from 0.66-1.94x10-3mm2s-1 (mean?=?1.14). Within-patient coefficient-of-variation (wCV) was 7.1% (95% CI 5.7-9.6%), limits-of-agreement (LoA) -18.0 to 21.9%. Lesions >3 cm had improved repeatability: wCV 3.9% (95% CI 2.9-5.9%); and LoA -10.2 to 11.4%. Variability for lesions <3 cm was 2.46 times higher. ADC reproducibility across different post-processing platforms was excellent: Pearson's R2?=?0.99; CoV 2.8% (95% CI 2.3-3.4%); and LoA -7.4 to 8.0%. CONCLUSION:A free-breathing DW-MRI protocol for imaging malignant lung tumours achieved satisfactory within-patient repeatability and was robust to changes in post-processing software, justifying its use in multi-centre trials. For response evaluation in individual patients, a change in ADC >21.9% will reflect treatment-related change. KEY POINTS:• In lung cancer, free-breathing DWI-MRI produces acceptable images with evaluable ADC measurement. • ADC repeatability coefficient-of-variation is 7.1% for lung tumours >2 cm. • ADC repeatability coefficient-of-variation is 3.9% for lung tumours >3 cm. • ADC measurement precision is unaffected by the post-processing software used. • In multicentre trials, 22% increase in ADC indicates positive treatment response.

SUBMITTER: Weller A

PROVIDER: S-EPMC6175053 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Diffusion-weighted (DW) MRI in lung cancers: ADC test-retest repeatability.

Weller Alex A Papoutsaki Marianthi Vasiliki MV Waterton John C JC Chiti Arturo A Stroobants Sigrid S Kuijer Joost J Blackledge Matthew M Morgan Veronica V deSouza Nandita M NM

European radiology 20170410 11

<h4>Purpose</h4>To determine the test-retest repeatability of Apparent Diffusion Coefficient (ADC) measurements across institutions and MRI vendors, plus investigate the effect of post-processing methodology on measurement precision.<h4>Methods</h4>Thirty malignant lung lesions >2 cm in size (23 patients) were scanned on two occasions, using echo-planar-Diffusion-Weighted (DW)-MRI to derive whole-tumour ADC (b = 100, 500 and 800smm<sup>-2</sup>). Scanning was performed at 4 institutions (3 MRI v ...[more]

PMID: 28396997

Similar Datasets

Project description:ObjectiveTo assess the test-retest reproducibility and intra/interobserver agreement of apparent diffusion coefficient (ADC) measurements of myeloma lesions using whole body diffusion-weighted MRI (WB-DW-MRI) at 3T MRI.MethodsFollowing ethical approval, 11 consenting patients with relapsed multiple myeloma were prospectively recruited and underwent baseline WB-DW-MRI. For a single bed position, axial DWI was repeated after a short interval to permit test-retest measurements.Mean ADC measurement was performed by two experienced observers. Intra- and interobserver agreement and test-retest reproducibility were assessed, using coefficient of variation (CV) and interclass correlation coefficient (ICC) measures, for diffuse and focal lesions (small ≤10 mm and large >10 mm).Results47 sites of disease were outlined (23 focal, 24 diffuse) in different bed positions (pelvis = 22, thorax = 20, head and neck = 5). For all lesions, there was excellent intraobserver agreement with ICC of 0.99 (0.98-0.99) and COV of 5%. For interobserver agreement, ICC was 0.89 (0.8-0.934) and COV was 17%. There was poor interobserver agreement for diffuse disease (ICC = 0.46) and small lesions (ICC = 0.54).For test-retest reproducibility, excellent ICC (0.916) and COV (14.5%) values for mean ADC measurements were observed. ICCs of test-retest were similar between focal lesions (0.83) and diffuse infiltration (0.80), while ICCs were higher in pelvic (0.95) compared to thoracic (0.81) region and in small (0.96) compared to large (0.8) lesions.ConclusionADC measurements of focal lesions in multiple myeloma are repeatable and reproducible, while there is more variation in ADC measurements of diffuse disease in patients with multiple myeloma.Advances in knowledgeMean ADC measurements are repeatable and reproducible in focal lesions in multiple myeloma, while the ADC measurements of diffuse disease in multiple myeloma are more subject to variation. The evidence supports the future potential role of ADC measurements as predictive quantitative biomarker in multiple myeloma.

Project description:BackgroundRenal multiparametric magnetic resonance imaging (MRI) is a promising tool for diagnosis, prognosis, and treatment monitoring in kidney disease.PurposeTo determine intrasubject test-retest repeatability of renal MRI measurements.Study typeProspective.PopulationNineteen healthy subjects aged over 40 years.Field strength/sequencesT1 and T2 mapping, R2 * mapping or blood oxygenation level-dependent (BOLD) MRI, diffusion tensor imaging (DTI), and intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI), 2D phase contrast, arterial spin labelling (ASL), dynamic contrast enhanced (DCE) MRI, and quantitative Dixon for fat quantification at 3T.AssessmentSubjects were scanned twice with ~1 week between visits. Total scan time was ~1 hour. Postprocessing included motion correction, semiautomated segmentation of cortex and medulla, and fitting of the appropriate signal model.Statistical testTo assess the repeatability, a Bland-Altman analysis was performed and coefficients of variation (CoVs), repeatability coefficients, and intraclass correlation coefficients were calculated.ResultsCoVs for relaxometry (T1 , T2 , R2 */BOLD) were below 6.1%, with the lowest CoVs for T2 maps and highest for R2 */BOLD. CoVs for all diffusion analyses were below 7.2%, except for perfusion fraction (FP ), with CoVs ranging from 18-24%. The CoV for renal sinus fat volume and percentage were both around 9%. Perfusion measurements were most repeatable with ASL (cortical perfusion only) and 2D phase contrast with CoVs of 10% and 13%, respectively. DCE perfusion had a CoV of 16%, while single kidney glomerular filtration rate (GFR) had a CoV of 13%. Repeatability coefficients (RCs) ranged from 7.7-87% (lowest/highest values for medullary mean diffusivity and cortical FP , respectively) and intraclass correlation coefficients (ICCs) ranged from -0.01 to 0.98 (lowest/highest values for cortical FP and renal sinus fat volume, respectively).Data conclusionCoVs of most MRI measures of renal function and structure (with the exception of FP and perfusion as measured by DCE) were below 13%, which is comparable to standard clinical tests in nephrology.Level of evidence2 TECHNICAL EFFICACY: Stage 1.

Project description:ObjectivesTo evaluate short-term test-retest repeatability of a deep learning architecture (U-Net) in slice- and lesion-level detection and segmentation of clinically significant prostate cancer (csPCa: Gleason grade group > 1) using diffusion-weighted imaging fitted with monoexponential function, ADCm.MethodsOne hundred twelve patients with prostate cancer (PCa) underwent 2 prostate MRI examinations on the same day. PCa areas were annotated using whole mount prostatectomy sections. Two U-Net-based convolutional neural networks were trained on three different ADCm b value settings for (a) slice- and (b) lesion-level detection and (c) segmentation of csPCa. Short-term test-retest repeatability was estimated using intra-class correlation coefficient (ICC(3,1)), proportionate agreement, and dice similarity coefficient (DSC). A 3-fold cross-validation was performed on training set (N = 78 patients) and evaluated for performance and repeatability on testing data (N = 34 patients).ResultsFor the three ADCm b value settings, repeatability of mean ADCm of csPCa lesions was ICC(3,1) = 0.86-0.98. Two CNNs with U-Net-based architecture demonstrated ICC(3,1) in the range of 0.80-0.83, agreement of 66-72%, and DSC of 0.68-0.72 for slice- and lesion-level detection and segmentation of csPCa. Bland-Altman plots suggest that there is no systematic bias in agreement between inter-scan ground truth segmentation repeatability and segmentation repeatability of the networks.ConclusionsFor the three ADCm b value settings, two CNNs with U-Net-based architecture were repeatable for the problem of detection of csPCa at the slice-level. The network repeatability in segmenting csPCa lesions is affected by inter-scan variability and ground truth segmentation repeatability and may thus improve with better inter-scan reproducibility.Key points• For the three ADCm b value settings, two CNNs with U-Net-based architecture were repeatable for the problem of detection of csPCa at the slice-level. • The network repeatability in segmenting csPCa lesions is affected by inter-scan variability and ground truth segmentation repeatability and may thus improve with better inter-scan reproducibility.

Project description:SignificanceThe Ohio Contrast Cards are a repeatable test of contrast sensitivity, and they reveal higher contrast sensitivity for low-vision patients than is shown by the Pelli-Robson chart.PurposeThis study aimed to compare the contrast sensitivity results and test/retest ±limits of agreement for the Ohio Contrast Cards and the Pelli-Robson letter contrast sensitivity chart on two challenging groups of participants, and to compare the Ohio Contrast Card results with grating acuity and the Pelli-Robson results with letter acuity.MethodsThe Ohio Contrast Card and Pelli-Robson tests were each performed twice by two different examiners within one visit on 40 elder patients in Primary Vision Care (>65 years old) and 23 to 27 low-vision school-aged students. Grating acuity was measured using the Teller Acuity Cards (all participants), and letter acuity was measured using ClearChart (elders) or the Bailey-Lovie chart (students).ResultsThe ±95% limits of agreement were similar for the Ohio Contrast Cards and the Pelli-Robson chart. The elders' limits of agreement were ±0.27 (Ohio Contrast Cards) and ±0.28 (Pelli-Robson); the students' limits of agreement were ±0.42 (Ohio Contrast Cards) and ±0.51 (Pelli-Robson). However, Ohio Contrast Card results were 0.41 log10 Michelson units more sensitive than the Pelli-Robson chart (over one line on the Pelli-Robson chart) for the elders and 0.90 log10 Michelson units (three lines on the Pelli-Robson chart) more sensitive for the elders (0.11 and 0.6 log10 Weber units, respectively). The Pelli-Robson results were correlated with letter acuities and Ohio Contrast Card results for both groups, and the Ohio Contrast Card results were correlated with Teller Acuity Card acuities for the elders.ConclusionsThe Ohio Contrast Cards and Pelli-Robson chart are similarly repeatable. Both contrast sensitivity tests can provide additional clinical information that is not available through visual acuity testing, and Ohio Contrast Card may provide additional information not available from the Pelli-Robson chart.

Project description:ObjectivesMultiple b-value gas diffusion-weighted MRI (DW-MRI) enables non-invasive and quantitative assessment of lung morphometry, but its long acquisition time is not well-tolerated by patients. We aimed to accelerate multiple b-value gas DW-MRI for lung morphometry using deep learning.MethodsA deep cascade of residual dense network (DC-RDN) was developed to reconstruct high-quality DW images from highly undersampled k-space data. Hyperpolarized 129Xe lung ventilation images were acquired from 101 participants and were retrospectively collected to generate synthetic DW-MRI data to train the DC-RDN. Afterwards, the performance of the DC-RDN was evaluated on retrospectively and prospectively undersampled multiple b-value 129Xe MRI datasets.ResultsEach slice with size of 64 × 64 × 5 could be reconstructed within 7.2 ms. For the retrospective test data, the DC-RDN showed significant improvement on all quantitative metrics compared with the conventional reconstruction methods (p < 0.05). The apparent diffusion coefficient (ADC) and morphometry parameters were not significantly different between the fully sampled and DC-RDN reconstructed images (p > 0.05). For the prospectively accelerated acquisition, the required breath-holding time was reduced from 17.8 to 4.7 s with an acceleration factor of 4. Meanwhile, the prospectively reconstructed results showed good agreement with the fully sampled images, with a mean difference of -0.72% and -0.74% regarding global mean ADC and mean linear intercept (Lm) values.ConclusionsDC-RDN is effective in accelerating multiple b-value gas DW-MRI while maintaining accurate estimation of lung microstructural morphometry, facilitating the clinical potential of studying lung diseases with hyperpolarized DW-MRI.Key points• The deep cascade of residual dense network allowed fast and high-quality reconstruction of multiple b-value gas diffusion-weighted MRI at an acceleration factor of 4. • The apparent diffusion coefficient and morphometry parameters were not significantly different between the fully sampled images and the reconstructed results (p > 0.05). • The required breath-holding time was reduced from 17.8 to 4.7 s and each slice with size of 64 × 64 × 5 could be reconstructed within 7.2 ms.

Dataset Information

Diffusion-weighted (DW) MRI in lung cancers: ADC test-retest repeatability.

Publications

Diffusion-weighted (DW) MRI in lung cancers: ADC test-retest repeatability.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets