Apparent diffusion coefficient reproducibility in brain tumors measured on 1.5 and 3 T clinical scanners: A pilot study

Normal Brain and Brain Tumor ADC: Changes Resulting From Variation of Diffusion Time and/or Echo Time in Pulsed-Gradient Spin Echo Diffusion Imaging

OBJECTIVES

Increasing gradient performance on modern magnetic resonance imaging scanners has profoundly reduced the attainable diffusion and echo times for clinically available pulsed-gradient spin echo (PGSE) sequences. This study investigated how this may impact the measured apparent diffusion coefficient (ADC), which is considered an important diagnostic marker for differentiation between normal and abnormal brain tissue and for therapeutic follow-up.

MATERIALS AND METHODS

Diffusion time and echo time dependence of the ADC were evaluated on a high-performance 3 T magnetic resonance imaging scanner. Diffusion PGSE brain scans were performed in 10 healthy volunteers and in 10 brain tumor patients using diffusion times of 16, 40, and 70 ms, echo times of 60, 75, and 104 ms at 3 b-values (0, 100, and 1000 s/mm 2 ), and a maximum gradient amplitude of 68 mT/m. A low gradient performance system was also emulated by reducing the diffusion encoding gradient amplitude to 19 mT/m. In healthy subjects, the ADC was measured in 6 deep gray matter regions and in 6 white matter regions. In patients, the ADC was measured in the solid part of the tumor.

RESULTS

With increasing diffusion time, a small but significant ADC increase of up to 2.5% was observed for 6 aggregate deep gray matter structures. With increasing echo time or reduced gradient performance, a small but significant ADC decrease of up to 2.6% was observed for 6 aggregate white matter structures. In tumors, diffusion time-related ADC changes were inconsistent without clear trend. For tumors with diffusivity above 1.0 μm 2 /ms, with prolonged echo time, there was a pronounced ADC increase of up to 12%. Meanwhile, for tumors with diffusivity at or below 1.0 μm 2 /ms, no change or a reduction was observed. Similar results were observed for gradient performance reduction, with an increase of up to 21%. The coefficient of variation determined in repeat experiments was 2.4%.

CONCLUSIONS

For PGSE and the explored parameter range, normal tissue ADC changes seem negligible. Meanwhile, observed tumor ADC changes can be relevant if ADC is used as a quantitative biomarker and not merely assessed by visual inspection. This highlights the importance of reporting all pertinent timing parameters in ADC studies and of considering these effects when building scan protocols for use in multicenter investigations.

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

BACKGROUND AND PURPOSE

Before quantitative imaging biomarkers (QIBs) acquired with magnetic resonance imaging (MRI) can be used for interventional trials in radiotherapy (RT), technical validation of these QIBs is necessary. The aim of this study was to assess the reproducibility of apparent diffusion coefficient (ADC) values, derived from diffusion-weighted (DW) MRI, in head and neck cancer using a 1.5 T MR-Linac (MRL) by comparison to a 3 T diagnostic scanner (DS).

MATERIAL AND METHODS

DW-MRIs were acquired on MRL and DS for 15 head and neck cancer patients before RT and in week 2 and rigidly registered to the planning computed tomography. Mean ADC values were calculated for submandibular (SG) and parotid (PG) glands as well as target volumes (TV, gross tumor volume and lymph nodes), which were delineated based on computed tomography. Mean absolute ADC differences as well as within-subject coefficient of variation (wCV) and intraclass correlation coefficients (ICCs) were calculated for all volumes of interest.

RESULTS

A total of 23 datasets were analyzed. Mean ADC difference (DS-MRL) for SG, PG and TV resulted in 142, 254 and 93·10-6 mm2/s. wCVs/ICCs, comparing MRL and DS, were determined as 13.7 %/0.26, 24.4 %/0.23 and 16.1 %/0.73 for SG, PG and TV, respectively.

CONCLUSION

ADC values, measured on the 1.5 T MRL, showed reasonable reproducibility with an ADC underestimation in contrast to the DS. This ADC shift must be validated in further experiments and considered for future translation of QIB candidates from DS to MRL for response adaptive RT.

Correlation between Multiparametric MR Imaging and Molecular Genetics in Pontine Pediatric High-Grade Glioma

BACKGROUND AND PURPOSE

Molecular profiling is a crucial feature in the "integrated diagnosis" of CNS tumors. We aimed to determine whether radiomics could distinguish molecular types of pontine pediatric high-grade gliomas that have similar/overlapping phenotypes on conventional anatomic MR images.

MATERIALS AND METHODS

Baseline MR images from children with pontine pediatric high-grade gliomas were analyzed. Retrospective imaging studies included standard precontrast and postcontrast sequences and DTI. Imaging analyses included median, mean, mode, skewness, and kurtosis of the ADC histogram of the tumor volume based on T2 FLAIR and enhancement at baseline. Histone H3 mutations were identified through immunohistochemistry and/or Sanger or next-generation DNA sequencing. The log-rank test identified imaging factors prognostic of survival from the time of diagnosis. Wilcoxon rank-sum and Fisher exact tests compared imaging predictors among groups.

RESULTS

Eighty-three patients had pretreatment MR imaging and evaluable tissue sampling. The median age was 6 years (range, 0.7-17 years); 50 tumors had a K27M mutation in H3-3A, and 11, in H3C2/3. Seven tumors had histone H3 K27 alteration, but the specific gene was unknown. Fifteen were H3 wild-type. Overall survival was significantly higher in H3C2/3- compared with H3-3A-mutant tumors (P = .003) and in wild-type tumors compared with any histone mutation (P = .001). Lower overall survival was observed in patients with enhancing tumors (P = .02) compared with those without enhancement. H3C2/3-mutant tumors showed higher mean, median, and mode ADC_total values (P < .001) and ADC_enhancement (P < .004), with lower ADC_total skewness and kurtosis (P < .003) relative to H3-3A-mutant tumors.

CONCLUSIONS

ADC histogram parameters are correlated with histone H3 mutation status in pontine pediatric high-grade glioma.

Repeatability and Reproducibility of ADC Measurements and MRI Signal Intensity Measurements of Bone Marrow in Monoclonal Plasma Cell Disorders: A Prospective Bi-institutional Multiscanner, Multiprotocol Study

BACKGROUND/OBJECTIVES

Apparent diffusion coefficient (ADC) and signal intensity (SI) measurements play an increasing role in magnetic resonance imaging (MRI) of monoclonal plasma cell disorders. The purpose of this study was to assess interrater variability, repeatability, and reproducibility of ADC and SI measurements from bone marrow (BM) under variation of MRI protocols and scanners.

PATIENTS AND METHODS

Fifty-five patients with suspected or confirmed monoclonal plasma cell disorder were prospectively included in this institutional review board-approved study and underwent several measurements after the standard clinical whole-body MR scan, including repeated scan after repositioning, scan with a second MRI protocol, scan at a second 1.5 T scanner with a harmonized MRI protocol, and scan at a 3 T scanner. For T1-weighted, T2-weighted STIR, B800 images, and ADC maps, regions of interest were placed in the BM of the iliac crest and sacral bone, and in muscle tissue for image normalization. Bland-Altman plots were constructed, and absolute bias, relative bias to mean, limits of agreement, and coefficients of variation were calculated.

RESULTS

Interrater variability and repeatability experiments showed a maximal relative bias of -0.077 and a maximal coefficient of variation of 16.2% for all sequences. Although the deviations at the second 1.5 T scanner with harmonized MRI protocol to the first 1.5 T scanner showed a maximal relative bias of 0.124 for all sequences, the variation of the MRI protocol and scan at the 3 T scanner led to large relative biases of up to -0.357 and -0.526, respectively. When comparing the 3 T scanner to the 1.5 T scanner, normalization to muscle reduced the bias of T1-weighted and T2-weighted sequences, but not of ADC maps.

CONCLUSIONS

The MRI scanners with identical field strength and harmonized MRI protocols can provide relatively stable quantitative measurements of BM ADC and SI. Deviations in MRI field strength and MRI protocol should be avoided when applying ADC cutoff values, which were established at other scanners or when performing multicentric imaging trials.

Feasibility of apparent diffusion coefficient in predicting the technical outcome of MR-guided high-intensity focused ultrasound treatment of uterine fibroids - a comparison with the Funaki classification

PURPOSE

To investigate the feasibility of using an apparent diffusion coefficient (ADC) classification in predicting the technical outcome of magnetic resonance imaging-guided high-intensity focused ultrasound (MRgHIFU) treatment of symptomatic uterine fibroids and to compare it to the Funaki classification.

MATERIALS AND METHODS

Forty-two patients with forty-eight uterine fibroids underwent diffusion-weighted imaging (DWI) before MRgHIFU treatment. The DW images were acquired with five different b-values. Correlations between ADC values and treatment parameters were assessed. Optimal ADC cutoff values were determined to predict technical outcomes, that is, nonperfused volume ratios (NPVr) such that three classification groups were created (NPVr of <30%, 30-80%, or >80%). Results were compared to the Funaki classification using receiver-operating-characteristic (ROC) curve analysis, with statistical significance being tested with the Chi-square test.

RESULTS

A statistically significant negative correlation (Spearman's ρ = -0.31, p-value < 0.05) was detected between ADC values and NPV ratios. ROC curve analysis indicated that optimal ADC cutoff values of 980 × 10^-6mm²/s (NPVr > 80%) and 1800 × 10^-6mm²/s (NPVr < 30%) made it possible to classify fibroids into three groups: ADC I (NPVr > 80%), ADC II (NPVr 30-80%) and ADC III (NPVr < 30%). Analysis of the whole model area under the curve resulted in values of 0.79 for the ADC classification (p-value = 0.0007) and 0.62 for the Funaki classification (p-value = 0.0527).

CONCLUSIONS

Lower ADC values prior to treatment correlate with higher NPV ratios. The ADC classification seems to be able to predict the NPV ratio and may even outperform the Funaki classification. Based on these results DWI and ADC maps should be included in the MRI screening protocol.

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.

Quantitative characterization of extraocular orbital lesions in children using diffusion-weighted imaging

BACKGROUND

Diffusion-weighted imaging (DWI) has been shown to be helpful in providing information about cellular density and also predicting the histological features of aggressive tumors. Several studies have evaluated this technique for orbital tumors. However, very few articles have focused exclusively on evaluating pediatric orbital masses and, within those, only a small number of patients were included in the study.

OBJECTIVE

This study aimed to evaluate the use of DWI and apparent diffusion coefficient (ADC) values to differentiate between benign and malignant extraocular orbital lesions in children.

MATERIALS AND METHODS

This retrospective study included 73 patients under the age of 18 seen in our hospital between October 2016 and February 2019. The extraocular orbital lesions were evaluated clinically and radiologically using DWI. The diagnosis was confirmed by either histological examination (after biopsy or surgery) or based on clinical and radiologic evaluation.

RESULTS

The malignant lesions were found to have increased diffusion restriction in comparison to the benign lesions. The ADC values of the malignant lesions were significantly lower (P<0.0001). The use of a cutoff value of 0.99×10^-3 mm²/s allowed for the differentiation of the benign lesions and malignant lesions with a sensitivity of 75% and a specificity of 100% while the cutoff point of 1.26×10^-3 mm²/s had a sensitivity of 100% and a specificity of 73%.

CONCLUSION

Measurement of ADC in extraocular orbital lesions in children may help differentiate malignant lesions from benign lesions.

Cellular density of low-grade transition zone prostate cancer: A limiting factor to correlate restricted diffusion with tumor aggressiveness

OBJECTIVE

To compare the mean apparent diffusion coefficient (ADCmean) and glandular density of Gleason score (GS) 3 + 3 transition zone prostate cancers (TZ-PCa) with those of the peripheral zone (PZ-PCa).

MATERIAL & METHODS

Seventy-nine men (mean age: 65 ± 6 [SD] years; range: 52-81 years) with 37 TZ-PCa (37/79; 53 %) and 42 PZ-PCa (42/79; 47 %) had prostate MRI before radical prostatectomy. Glandular cell density was semi-quantitatively evaluated in all tumors. ADCmean and glandular cell density of GS3 + 3 TZ-PCa were compared to those of PZ-PCa. ADCmean was correlated to GS in each zone.

RESULTS

ADCmean of GS 3 + 3 tumors was significantly lower in the TZ (728 × 10^-6±52 [SD] mm²/s; range: 670-1060mm²/s) than in the PZ (865 × 10^-6 ±121 [SD] mm²/s; range: 670-1120mm²/s) (p = 0.0007), related to a significantly higher glandular density involving more than 50 % of the tumor in 58 % (7/12) of patients in GS3 + 3 TZ-PCa versus 7.6 % (1/13) in PZ-PCa (p = 0.03). ADCmean of GS3 + 3 TZ-PCa was not significantly different from that of GS 3 + 4 (p = 0.14) or GS>3 + 4 Ca (p = 0.9), whatever the zone of origin. In the PZ, ADCmean of GS 3 + 3-PCa was higher than that of Gleason>3 + 4 PZ-PCa (p = 0.02) and similar to that of GS 3 + 4 PZ-PCa (p = 0.24). Correlation between ADCmean and GS was weak for TZ-PCa (ρ = 0.32; p = 0.04) and moderate for PZ-PCa (ρ = 0.45; p = 0.003).

CONCLUSION

ADCmean of GS 3 + 3 TZ-PCa is significantly lower than that of GS 3 + 3 PZ-PCa, related to a unique dense histological pattern and reaches that of higher-grade PCa, whatever the zone of origin.

AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics

INTRODUCTION

The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes.

OBJECTIVE

The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model.

Comparison between MRI-derived ADC maps and 18FLT-PET in pre-operative glioblastoma

BACKGROUND AND PURPOSE

Among principal MRI sequences used for a better pre-therapeutic characterization of glioblastoma (GBM), DWI-derived ADC is expected to be a good parameter for the evaluation of cellularity, due to restricted water diffusivity. We aimed here to compare ADC maps to ¹⁸FLT-PET, a proliferation tracer, in GBM cases.

MATERIALS AND METHODS

Patients underwent ¹⁸FLT-PET, followed by multiparametric magnetic resonance imaging (MRI) just prior to surgery. We analysed in this study twenty GBM confirmed patients. The 5th percentile (5p) of the ADC values were thresholded to define the ADC_min ROI, while the 95th percentile (95p) of the SUV FLT values were used to define the FLT_max ROI. The statistical and spatial correlations between these two groups of ROIs were analyzed.

RESULTS

We did not observe any significant correlations between ADC_min and FLT_max cut-off values (R²=0.0285), neither between ADC_min and FLT_max ROIs (mean Dice=0.09±0.12). Mean ADC values in the FLT_max defined ROI were significantly higher than the values in the ADC_min ROI (P<0.001). Mean FLT values in the FLT_max ROI were significantly higher than the values in the ADC_min ROI (P<0.001).

CONCLUSIONS

When comparing ADC maps to ¹⁸FLT uptake, we did not observe significant anatomical overlap nor correlation, between the regions of low ADC and high FLT disabling to clearly link ADC values to cellular proliferation. The exact significance of ADC maps in GBM has yet to be elaborated.