Between-Scanner and Between-Visit Variation in Normal White Matter Apparent Diffusion Coefficient Values in the Setting of a Multi-Center Clinical Trial

Histogram Analysis of Apparent Diffusion Coefficient Maps Provides Genotypic and Pretreatment Phenotypic Information in Pediatric and Young Adult Rhabdomyosarcoma

INTRODUCTION

We evaluate the role of apparent diffusion coefficient (ADC) histogram metrics in stratifying pediatric and young adult rhabdomyosarcomas.

METHODS

We retrospectively evaluated baseline diffusion-weighted imaging (DWI) from 38 patients with rhabdomyosarcomas (Not otherwise specified: 2; Embryonal: 21; Spindle Cell: 2; Alveolar: 13, mean ± std dev age: 8.1 ± 7.76 years). The diffusion images were obtained on a wide range of 1.5 T and 3 T scanners at multiple sites. FOXO1 fusion status was available for 35 patients, nine of whom harbored the fusion. 13 patients were TNM stage 1, eight had stage 2 disease, nine were stage 3, and eight had stage 4 disease. 23 patients belonged to Clinical Group III and seven to Group IV, while two and five were CG I and II, respectively. Nine patients were classified as low risk, while 21 and five were classified as intermediate and high risk respectively. Histogram parameters of the apparent diffusion coefficient (ADC) map from the entire tumor were obtained based on manual tumor contouring. A two-tailed Mann-Whitney U test was used for all two-group, and the Kruskal-Wallis's test was used for multiple-group comparisons. Bootstrapped receiver operating characteristic (ROC) curves and areas under the curve (AUC) were generated for the statistically significant histogram parameters to differentiate genotypic and phenotypic parameters.

RESULTS

Alveolar rhabdomyosarcomas had a statistically significant lower 10th Percentile (586.54 ± 164.52, mean ± std dev, values are in ×10-6mm2/s) than embryonal rhabdomyosarcomas (966.51 ± 481.33) with an AUC of 0.85 (95%CI. 0.73-0.95) for differentiating the two. The 10th percentile was also significantly different between FOXO1 fusion-positive (553.87 ± 187.64) and negative (898.07 ± 449.38) rhabdomyosarcomas with an AUC of 0.83 (95% CI 0.71-0.94). Alveolar rhabdomyosarcomas also had statistically significant lower Mean, Median, and Root Mean Squared ADC histogram values than embryonal rhabdomyosarcomas. Four, five, and seven of the 18 histogram parameters evaluated demonstrated a statistically significant increase with higher TNM stage, clinical group, assignment, and pretreatment risk stratification, respectively. For example, Entropy had an AUC of 0.8 (95% CI. 0.67-0.92) for differentiating TNM stage 1 from ≥ stage 2 and 0.9 (95% CI. 0.8-0.98) for differentiating low from intermediate or high-risk stratification.

CONCLUSION

Our findings demonstrate the potential of ADC histogram metrics to predict clinically relevant variables for rhabdomyosarcoma, including FOXO1 fusion status, histopathology, Clinical Group, TNM staging, and risk stratification.

Feasibility of quantitative diffusion-weighted imaging during intra-procedural MRI-guided brachytherapy of locally advanced cervical and vaginal cancers

PURPOSE

To determine the feasibility of quantitative apparent diffusion coefficient (ADC) acquisition during magnetic resonance imaging-guided brachytherapy (MRgBT) using reduced field-of-view (rFOV) diffusion-weighted imaging (DWI).

METHODS AND MATERIALS

T2-weighted (T2w) MR and full-FOV single-shot echo planar (ssEPI) DWI were acquired in 7 patients with cervical or vaginal malignancy at baseline and prior to brachytherapy, while rFOV-DWI was acquired during MRgBT following brachytherapy applicator placement. The gross target volume (GTV) was contoured on the T2w images and registered to the ADC map. Voxels at the GTV's maximum Maurer distance comprised a central sub-volume (GTVcenter). Contour ADC mean and standard deviation were compared between timepoints using repeated measures ANOVA.

RESULTS

ssEPI-DWI mean ADC increased between baseline and prebrachytherapy from 1.03 ± 0.18 10-3 mm2/s to 1.34 ± 0.28 10-3 mm2/s for the GTV (p = 0.06) and from 0.84 ± 0.13 10-3 mm2/s to 1.26 ± 0.25 10-3 mm2/s at the level of the GTVcenter (p = 0.03), consistent with early treatment response. rFOV-DWI during MRgBT demonstrated mean ADC values of 1.28 ± 0.14 10-3 mm2/s and 1.28 ± 0.19 10-3 mm2/s for the GTV and GTVcenter, respectively (p = 0.02 and p = 0.03 relative to baseline). No significant differences were observed between ssEPI-DWI and rFOV-DWI ADC measurements.

CONCLUSIONS

Quantitative ADC measurement in the setting of MRI guided brachytherapy implant placement for cervical and vaginal cancers is feasible using rFOV-DWI, with comparable mean ADC comparable to prebrachytherapy ssEPI-DWI, and may enable MRI-guided radiotherapy targeting of low ADC, radiation resistant sub-volumes of tumor.

Age-related topographic map of magnetic resonance diffusion metrics in neonatal brains

Accelerated maturation of brain parenchyma close to term-equivalent age leads to rapid changes in diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) metrics of neonatal brains, which can complicate the evaluation and interpretation of these scans. In this study, we characterized the topography of age-related evolution of diffusion metrics in neonatal brains. We included 565 neonates who had MRI between 0 and 3 months of age, with no structural or signal abnormality-including 162 who had DTI scans. We analyzed the age-related changes of apparent diffusion coefficient (ADC) values throughout brain and DTI metrics (fractional anisotropy [FA] and mean diffusivity [MD]) along white matter (WM) tracts. Rate of change in ADC, FA, and MD values across 5 mm cubic voxels was calculated. There was significant reduction of ADC and MD values and increase of FA with increasing gestational age (GA) throughout neonates' brain, with the highest temporal rates in subcortical WM, corticospinal tract, cerebellar WM, and vermis. GA at birth had significant effect on ADC values in convexity cortex and corpus callosum as well as FA/MD values in corpus callosum, after correcting for GA at scan. We developed online interactive atlases depicting age-specific normative values of ADC (ages 34-46 weeks), and FA/MD (35-41 weeks). Our results show a rapid decrease in diffusivity metrics of cerebral/cerebellar WM and vermis in the first few weeks of neonatal age, likely attributable to myelination. In addition, prematurity and low GA at birth may result in lasting delay in corpus callosum myelination and cerebral cortex cellularity.

Whole-tumour apparent diffusion coefficient (ADC) histogram analysis to identify MYCN-amplification in neuroblastomas: preliminary results

OBJECTIVES

To determine the role of apparent diffusion coefficient (ADC) histogram analysis in the identification of MYCN-amplification status in neuroblastomas.

METHODS

We retrospectively evaluated imaging records from 62 patients with neuroblastomas (median age: 15 months (interquartile range (IQR): 7-24 months); 38 females) who underwent magnetic resonance imaging at our institution before the initiation of any therapy or biopsy. Fourteen patients had MYCN-amplified (MYCNA) neuroblastoma. Histogram parameters of ADC maps from the entire tumour was obtained from the baseline images and the normalised images. The Mann-Whitney U test was used to compare the absolute and normalised histogram parameters amongst neuroblastomas with and without MYCN-amplification. Receiver operating characteristic (ROC) curves and area under the curves (AUC) were generated for the statistically significant histogram parameters. Cut-offs obtained from the ROC curves were evaluated on an external validation set (n-15, MYCNA-6, F-7, age 24 months (10-60)). A logistic regression model was trained to predict MYCNA by combining statistically significant histogram parameters and was evaluated on the validation set.

RESULTS

MYCN-amplified neuroblastomas had statistically significant higher maximum ADC and lower minimum ADC than non-amplified neuroblastomas. They also demonstrated higher entropy, variance, energy, and lower uniformity than non-amplified neoplasms (p > 0.05). Energy, entropy, and maximum ADC had AUC of 0.85, 0.79, and 0.82, respectively.

CONCLUSIONS

Whole tumour ADC histogram analysis of neuroblastomas can differentiate between tumours with and without MYCN-amplification. These parameters can help identify areas for targeted biopsies or can be used to predict subtypes of these high-risk tumours before biopsy results are available.

KEY POINTS

• MYCN-amplification significantly affects treatment decisions in neuroblastomas. • MYCN-amplified neuroblastomas had significantly different ADC histogram metrics as compared to tumours without amplification. • ADC histogram metrics can be used to predict MYCN-amplification status based on imaging.

Challenges of imaging interpretation to predict oligodendroglioma grade: a report from the Neuro-Oncology Branch

Background: To illustrate challenges of imaging interpretation in patients with oligodendroglioma seen at a referral center and evaluate interrater reliability. Methods: Two neuro-oncologists reviewed diagnostic preradiation MRIs of oligodendroglioma patients; interrater reliability was calculated with the kappa coefficient (k). A neuroradiologist measured presurgical apparent diffusion coefficient (ADC), if available. Results: Extensive enhancement was noted in four of 58 patients, k = 0.7; necrosis in seven of 58, k = 0.61; calcification in seven of 17, k = 1.0; diffusion restriction in two of 39 patients, k = 1.0 (all only in grade 3). ADC values with receiver operator characteristic analysis for area under the curve were 0.473, not significantly different from the null hypothesis (p = 0.14). Conclusions: Extensive enhancement, necrosis and calcification correlated with grade 3 oligodendroglioma in our sample. However, interrater variability is an important limitation when assessing radiographic features, supporting the need for standardization of imaging protocols and their interpretation.

Reproducibility of diffusion tensor image analysis along the perivascular space (DTI-ALPS) for evaluating interstitial fluid diffusivity and glymphatic function: CHanges in Alps index on Multiple conditiON acquIsition eXperiment (CHAMONIX) study

PURPOSE

The diffusion tensor image analysis along the perivascular space (DTI-ALPS) method was developed to evaluate the brain's glymphatic function or interstitial fluid dynamics. This study aimed to evaluate the reproducibility of the DTI-ALPS method and the effect of modifications in the imaging method and data evaluation.

MATERIALS AND METHODS

Seven healthy volunteers were enrolled in this study. Image acquisition was performed for this test-retest study using a fixed imaging sequence and modified imaging methods which included the placement of region of interest (ROI), imaging plane, head position, averaging, number of motion-proving gradients, echo time (TE), and a different scanner. The ALPS-index values were evaluated for the change of conditions listed above.

RESULTS

This test-retest study by a fixed imaging sequence showed very high reproducibility (intraclass coefficient = 0.828) for the ALPS-index value. The bilateral ROI placement showed higher reproducibility. The number of averaging and the difference of the scanner did not influence the ALPS-index values. However, modification of the imaging plane and head position impaired reproducibility, and the number of motion-proving gradients affected the ALPS-index value. The ALPS-index values from 12-axis DTI and 3-axis diffusion-weighted image (DWI) showed good correlation (r = 0.86). Also, a shorter TE resulted in a larger value of the ALPS-index.

CONCLUSION

ALPS index was robust under the fixed imaging method even when different scanners were used. ALPS index was influenced by the imaging plane, the number of motion-proving gradient axes, and TE in the imaging sequence. These factors should be uniformed in the planning ALPS method studies. The possibility to develop a 3-axis DWI-ALPS method using three axes of the motion-proving gradient was also suggested.

Widespread effects of dMRI data quality on diffusion measures in children

Diffusion magnetic resonance imaging (dMRI) datasets are susceptible to several confounding factors related to data quality, which is especially true in studies involving young children. With the recent trend of large‐scale multicenter studies, it is more critical to be aware of the varied impacts of data quality on measures of interest. Here, we investigated data quality and its effect on different diffusion measures using a multicenter dataset. dMRI data were obtained from 691 participants (5–17 years of age) from six different centers. Six data quality metrics—contrast to noise ratio, outlier slices, and motion (absolute, relative, translation, and rotational)—and four diffusion measures—fractional anisotropy, mean diffusivity, tract density, and length—were computed for each of 36 major fiber tracts for all participants. The results indicated that four out of six data quality metrics (all except absolute and translation motion) differed significantly between centers. Associations between these data quality metrics and the diffusion measures differed significantly across the tracts and centers. Moreover, these effects remained significant after applying recently proposed harmonization algorithms that purport to remove unwanted between‐site variation in diffusion data. These results demonstrate the widespread impact of dMRI data quality on diffusion measures. These tracts and measures have been routinely associated with individual differences as well as group‐wide differences between neurotypical populations and individuals with neurological or developmental disorders. Accordingly, for analyses of individual differences or group effects (particularly in multisite dataset), we encourage the inclusion of data quality metrics in dMRI analysis.

Establishing ADC-Based Histogram and Texture Features for Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinoma

The purpose of this study was to assess baseline variability in histogram and texture features derived from apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (DW-MRI) examinations and to identify early treatment-induced changes to these features in patients with head and neck squamous cell carcinoma (HNSCC) undergoing definitive chemoradiation. Patients with American Joint Committee on Cancer Stage III–IV (7^th edition) HNSCC were prospectively enrolled on an IRB-approved study to undergo two pre-treatment baseline DW-MRI examinations, performed 1 week apart, and a third early intra-treatment DW-MRI examination during the second week of chemoradiation. Forty texture and six histogram features were derived from ADC maps. Repeatability of the features from the baseline ADC maps was assessed with the intra-class correlation coefficient (ICC). A Wilcoxon signed-rank test compared average baseline and early treatment feature changes. Data from nine patients were used for this study. Comparison of the two baseline ADC maps yielded 11 features with an ICC ≥ 0.80, indicating that these features had excellent repeatability: Run Gray-Level Non-Uniformity, Coarseness, Long Zone High Gray-Level, Variance (Histogram Feature), Cluster Shade, Long Zone, Variance (Texture Feature), Run Length Non-Uniformity, Correlation, Cluster Tendency, and ADC Median. The Wilcoxon signed-rank test resulted in four features with significantly different early treatment-induced changes compared to the baseline values: Run Gray-Level Non-Uniformity (p = 0.005), Run Length Non-Uniformity (p = 0.005), Coarseness (p = 0.006), and Variance (Histogram) (p = 0.006). The feasibility of histogram and texture analysis as a potential biomarker is dependent on the baseline variability of each metric, which disqualifies many features.

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Simple Summary

Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored.

Abstract

In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence

Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.

Scan-rescan repeatability and cross-scanner comparability of DTI metrics in healthy subjects in the SPRINT-MS multicenter trial

PURPOSE

To assess intrascanner repeatability and cross-scanner comparability for diffusion tensor imaging (DTI) metrics in a multicenter clinical trial.

METHODS

DTI metrics (including longitudinal diffusivity [LD], fractional anisotropy [FA], mean diffusivity [MD], and transverse diffusivity [TD]) from pyramidal tracts for healthy controls were calculated from images acquired on twenty-seven 3T MR scanners (Siemens and GE) with 6 different scanner models and 7 different software versions as part of the NN102/SPRINT-MS clinical trial. Each volunteer underwent two scanning sessions on the same scanner. Signal-to-noise ratio (SNR) and signal-to-noise floor ratio (SNFR) were also assessed.

RESULTS

DTI metrics showed good scan-rescan repeatability. There were no significant differences between scans and rescans in LD, FA, MD, or TD values. Although the cross-scanner coefficient of variation (CV) values for all DTI metrics were <5.7%, significant differences were observed for LD (p < 3.3e-5) and FA (p < 0.0024) when GE scanners were compared with Siemens scanners. Significant differences were also observed for SNR when comparing GE scanners and Siemens Skyra scanners (p < 1.4e-7) and when comparing Siemens Skyra scanners and TIM Trio scanners (p < 1.0e-10). Analysis of background signal also demonstrated differences between GE and Siemens scanners in terms of signal statistics. The measured signal intensity from a background noise region of interest was significantly higher for GE scanners than for Siemens scanners (p < 1.2e-12). Significant differences were also observed for SNFR when comparing GE scanners and Siemens Skyra scanners (p < 2.5e-11), GE scanners and Siemens Trio scanners (p < 7.5e-11), and Siemens Skyra scanners and TIM Trio scanners (p < 2.5e-9).

CONCLUSIONS

The good repeatability of the DTI metrics among the 27 scanners used in this study confirms the feasibility of combining DTI data from multiple centers using high angular resolution sequences. Our observations support the feasibility of longitudinal multicenter clinical trials using DTI outcome measures. The noise floor level and SNFR are important parameters that must be assessed when comparing studies that used different scanner models.

Improved detectability of acute and subacute brainstem infarctions by combining standard axial and thin-sliced sagittal DWI

BACKGROUND AND PURPOSE

Most false negative findings in DWI of ischemic stroke are in patients with minor deficits clinically localized to the brainstem. Our goal was to evaluate the benefit of a thin-sliced sagittal DWI in addition to conventional axial DWI at 1.5T for the detection of brainstem infarctions.

METHODS

Data of patients with symptoms consistent with acute and subacute brainstem infarction and an MRI examination including standard axial DWI and thin-sliced sagittal DWI were retrospectively analyzed. Patients with the later diagnosis of a TIA, an inflammation or a tumor of the brainstem were excluded from analysis. Diffusion restrictions were identified by two independent raters blinded for the final clinical diagnosis in three separate reading steps: First, only axial DWI, secondly only sagittal DWI, and lastly both DWIs together. Presence and size of DWI-lesions were documented for each plane. Differences between the observers were settled in consensus in a separate joint reading.

RESULTS

Of 73 included patients, 46 patients were clinically diagnosed with brainstem infarction. Inter-observer agreement was excellent for the detection of brainstem lesions in axial and sagittal DWI (kappa = 0.94 and 0.97). In 28/46 patients (60.9%) lesions were detected in the axial plane alone, whereas in 6 more patients (73.9%) lesions were detected in the review of both sequences together. All lesions undetectable in the axial plane were smaller than 5 mm in cranio-caudal direction.

CONCLUSIONS

Thin-sliced sagittal DWI in addition to axial DWI improves the detection rate of brainstem infarction with little additional expenditure of time.

Quantitative Apparent Diffusion Coefficient Mapping May Predict Seizure Onset in Children With Sturge-Weber Syndrome

BACKGROUND

Sturge-Weber syndrome (SWS) is often accompanied by seizures, stroke-like episodes, hemiparesis, and visual field deficits. This study aimed to identify early pathophysiologic changes that exist before the development of clinical symptoms and to evaluate if the apparent diffusion coefficient (ADC) map is a candidate early biomarker of seizure risk in patients with SWS.

METHODS

This is a prospective cross-sectional study using quantitative ADC analysis to predict onset of epilepsy. Inclusion criteria were presence of the port wine birthmark, brain MRI with abnormal leptomeningeal capillary malformation (LCM) and enlarged deep medullary veins, and absence of seizures or other neurological symptoms. We used our recently developed normative, age-specific ADC atlases to quantitatively identify ADC abnormalities, and correlated presymptomatic ADC abnormalities with risks for seizures.

RESULTS

We identified eight patients (three girls) with SWS, age range of 40 days to nine months. One patient had predominantly LCM, deep venous anomaly, and normal ADC values. This patient did not develop seizures. The remaining seven patients had large regions of abnormal ADC values, and all developed seizures; one of seven patients had late onset seizures.

CONCLUSIONS

Larger regions of decreased ADC values in the affected hemisphere, quantitatively identified by comparison with age-matched normative ADC atlases, are common in young children with SWS and were associated with later onset of seizures in this small study. Our findings suggest that quantitative ADC maps may identify patients at high risk of seizures in SWS, but larger prospective studies are needed to determine sensitivity and specificity.

The AGES-Reykjavik study atlases: Non-linear multi-spectral template and atlases for studies of the ageing brain

Quantitative analyses of brain structures from Magnetic Resonance (MR) image data are often performed using automatic segmentation algorithms. Many of these algorithms rely on templates and atlases in a common coordinate space. Most freely available brain atlases are generated from relatively young individuals and not always derived from well-defined cohort studies. In this paper, we introduce a publicly available multi-spectral template with corresponding tissue probability atlases and regional atlases, optimised to use in studies of ageing cohorts (mean age 75 ± 5 years). Furthermore, we provide validation data from a regional segmentation pipeline to assure the integrity of the dataset.