Is Diffusion Tensor Imaging-Guided Radiotherapy the New State-of-the-Art? A Review of the Current Literature and Technical Insights

Despite the increasing precision of radiotherapy delivery, it is still frequently associated with neurological complications. This is in part due to damage to eloquent white matter (WM) tracts, which is made more likely by the fact they cannot be visualised on standard structural imaging. WM is additionally more vulnerable than grey matter to radiation damage. Primary brain malignancies also are known to spread along the WM. Diffusion tensor imaging (DTI) is the only in vivo method of delineating WM tracts. DTI is an imaging technique that models the direction of diffusion and therefore can infer the orientation of WM fibres. This review article evaluates the current evidence for using DTI to guide intracranial radiotherapy and whether it constitutes a new state-of-the-art technique. We provide a basic overview of DTI and its known applications in radiotherapy, which include using tractography to reduce the radiation dose to eloquent WM tracts and using DTI to detect or predict tumoural spread. We evaluate the evidence for DTI-guided radiotherapy in gliomas, metastatic disease, and benign conditions, finding that the strongest evidence is for its use in arteriovenous malformations. However, the evidence is weak in other conditions due to a lack of case-controlled trials

Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas

Objective: This study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2–4) from 3 different centers. Methods: To quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space. Results: The Mean slope of increase (MSI) and the K1 parameter of the bidirectional exchange model exhibited the highest performance with (ACC 74.3% AUROC 74.2%) and (ACC 75% AUROC 70.5%) respectively. Conclusion: The proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation

Repeatability of perfusion measurements in adult gliomas using pulsed and pseudo-continuous arterial spin labelling MRI

Objectives: To investigate the repeatability of perfusion measures in gliomas using pulsed- and pseudo-continuous-arterial spin labelling (PASL, PCASL) techniques, and evaluate different regions-of-interest (ROIs) for relative tumour blood flow (rTBF) normalisation. / Materials and methods: Repeatability of cerebral blood flow (CBF) was measured in the Contralateral Normal Appearing Hemisphere (CNAH) and in brain tumours (aTBF). rTBF was normalised using both large/small ROIs from the CNAH. Repeatability was evaluated with intra-class-correlation-coefficient (ICC), Within-Coefficient-of-Variation (WCoV) and Coefficient-of-Repeatability (CR). / Results: PASL and PCASL demonstrated high reliability (ICC > 0.9) for CNAH-CBF, aTBF and rTBF. PCASL demonstrated a more stable signal-to-noise ratio (SNR) with a lower WCoV of the SNR than that of PASL (10.9–42.5% vs. 12.3–29.2%). PASL and PCASL showed higher WCoV in aTBF and rTBF than in CNAH CBF in WM and GM but not in the caudate, and higher WCoV for rTBF than for aTBF when normalised using a small ROI (PASL 8.1% vs. 4.7%, PCASL 10.9% vs. 7.9%, respectively). The lowest CR was observed for rTBF normalised with a large ROI. / Discussion: PASL and PCASL showed similar repeatability for the assessment of perfusion parameters in patients with primary brain tumours as previous studies based on volunteers. Both methods displayed reasonable WCoV in the tumour area and CNAH. PCASL’s more stable SNR in small areas (caudate) is likely to be due to the longer post-labelling delays

Prognostic value of preoperative dynamic contrast-enhanced MRI perfusion parameters for high-grade glioma patients

INTRODUCTION: The prognostic value of the dynamic contrast-enhanced (DCE) MRI perfusion and its histogram analysis-derived metrics is not well established for high-grade glioma (HGG) patients. The aim of this prospective study was to investigate DCE perfusion transfer coefficient (Ktrans), vascular plasma volume fraction (vp), extracellular volume fraction (ve), reverse transfer constant (kep), and initial area under gadolinium concentration time curve (IAUGC) as predictors of progression-free (PFS) and overall survival (OS) in HGG patients. METHODS: Sixty-nine patients with suspected anaplastic astrocytoma or glioblastoma underwent preoperative DCE-MRI scans. DCE perfusion whole tumor region histogram parameters, clinical details, and PFS and OS data were obtained. Univariate, multivariate, and Kaplan–Meier survival analyses were conducted. Receiver operating characteristic (ROC) curve analysis was employed to identify perfusion parameters with the best differentiation performance. RESULTS: On univariate analysis, ve and skewness of vp had significant negative impacts, while kep had significant positive impact on OS (P < 0.05). ve was also a negative predictor of PFS (P < 0.05). Patients with lower ve and IAUGC had longer median PFS and OS on Kaplan–Meier analysis (P < 0.05). Ktrans and ve could also differentiate grade III from IV gliomas (area under the curve 0.819 and 0.791, respectively). CONCLUSIONS: High ve is a consistent predictor of worse PFS and OS in HGG glioma patients. vp skewness and kep are also predictive for OS. Ktrans and ve demonstrated the best diagnostic performance for differentiating grade III from IV gliomas

The role of preoperative diffusion tensor imaging in predicting and improving functional outcome in pediatric patients undergoing epilepsy surgery: a systematic review

Objective: Diffusion tensor imaging (DTI) is a useful neuroimaging technique for surgical planning in adult patients. However, no systematic review has been conducted to determine its utility for pre-operative analysis and planning of Pediatric Epilepsy surgery. We sought to determine the benefit of pre-operative DTI in predicting and improving neurological functional outcome after epilepsy surgery in children with intractable epilepsy. Methods: A systematic review of articles in English using PubMed, EMBASE and Scopus databases, from inception to January 10, 2020 was conducted. All studies that used DTI as either predictor or direct influencer of functional neurological outcome (motor, sensory, language and/or visual) in pediatric epilepsy surgical candidates were included. Data extraction was performed by two blinded reviewers. Risk of bias of each study was determined using the QUADAS 2 Scoring System. Results: 13 studies were included (6 case reports/series, 5 retrospective cohorts, and 2 prospective cohorts) with a total of 229 patients. Seven studies reported motor outcome; three reported motor outcome prediction with a sensitivity and specificity ranging from 80 to 85.7 and 69.6 to 100%, respectively; four studies reported visual outcome. In general, the use of DTI was associated with a high degree of favorable neurological outcomes after epilepsy surgery. Conclusion: Multiple studies show that DTI helps to create a tailored plan that results in improved functional outcome. However, more studies are required in order to fully assess its utility in pediatric patients. This is a desirable field of study because DTI offers a non-invasive technique more suitable for children. Advances in knowledge: This systematic review analyses, exclusively, studies of pediatric patients with drug-resistant epilepsy and provides an update of the evidence regarding the role of DTI, as part of the pre-operative armamentarium, in improving post-surgical neurological sequels and its potential for outcome prediction

Apparent diffusion coefficient for molecular subtyping of non-gadolinium-enhancing WHO grade II/III glioma: volumetric segmentation versus two-dimensional region of interest analysis

OBJECTIVES: To investigate if quantitative apparent diffusion coefficient (ADC) measurements can predict genetic subtypes of non-gadolinium-enhancing gliomas, comparing whole tumour against single slice analysis. METHODS: Volumetric T2-derived masks of 44 gliomas were co-registered to ADC maps with ADC mean (ADCmean) calculated. For the slice analysis, two observers placed regions of interest in the largest tumour cross-section. The ratio (ADCratio) between ADCmeanin the tumour and normal appearing white matter was calculated for both methods. RESULTS: Isocitrate dehydrogenase (IDH) wild-type gliomas showed the lowest ADC values throughout (p < 0.001). ADCmeanin the IDH-mutant 1p19q intact group was significantly higher than in the IDH-mutant 1p19q co-deleted group (p < 0.01). A volumetric ADCmeanthreshold of 1201 × 10-6mm2/s identified IDH wild-type with a sensitivity of 83% and a specificity of 86%; a volumetric ADCratiocut-off value of 1.65 provided a sensitivity of 80% and a specificity of 92% (area under the curve (AUC) 0.9-0.94). A slice ADCratiothreshold for observer 1 (observer 2) of 1.76 (1.83) provided a sensitivity of 80% (86%), specificity of 91% (100%) and AUC of 0.95 (0.96). The intraclass correlation coefficient was excellent (0.98). CONCLUSIONS: ADC measurements can support the distinction of glioma subtypes. Volumetric and two-dimensional measurements yielded similar results in this study. KEY POINTS: • Diffusion-weighted MRI aids the identification of non-gadolinium-enhancing malignant gliomas • ADC measurements may permit non-gadolinium-enhancing glioma molecular subtyping • IDH wild-type gliomas have lower ADC values than IDH-mutant tumours • Single cross-section and volumetric ADC measurements yielded comparable results in this study

Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance

PURPOSE: Surveillance of patients with high-grade glioma (HGG) and identification of disease progression remain a major challenge in neurooncology. This study aimed to develop a support vector machine (SVM) classifier, employing combined longitudinal structural and perfusion MRI studies, to classify between stable disease, pseudoprogression and progressive disease (3-class problem). METHODS: Study participants were separated into two groups: group I (total cohort: 64 patients) with a single DSC time point and group II (19 patients) with longitudinal DSC time points (2-3). We retrospectively analysed 269 structural MRI and 92 dynamic susceptibility contrast perfusion (DSC) MRI scans. The SVM classifier was trained using all available MRI studies for each group. Classification accuracy was assessed for different feature dataset and time point combinations and compared to radiologists’ classifications. RESULTS: SVM classification based on combined perfusion and structural features outperformed radiologists’ classification across all groups. For the identification of progressive disease, use of combined features and longitudinal DSC time points improved classification performance (lowest error rate 1.6%). Optimal performance was observed in group II (multiple time points) with SVM sensitivity/specificity/accuracy of 100/91.67/94.7% (first time point analysis) and 85.71/100/94.7% (longitudinal analysis), compared to 60/78/68% and 70/90/84.2% for the respective radiologist classifications. In group I (single time point), the SVM classifier also outperformed radiologists’ classifications with sensitivity/specificity/accuracy of 86.49/75.00/81.53% (SVM) compared to 75.7/68.9/73.84% (radiologists). CONCLUSION: Our results indicate that utilisation of a machine learning (SVM) classifier based on analysis of longitudinal perfusion time points and combined structural and perfusion features significantly enhances classification outcome (p value= 0.0001)

Fiber tracking: A qualitative and quantitative comparison between four different software tools on the reconstruction of major white matter tracts

PURPOSE: Diffusion tensor imaging (DTI) enables in vivo reconstruction of white matter (WM) pathways. Considering the emergence of numerous models and fiber tracking techniques, we herein aimed to compare, both quantitatively and qualitatively, the fiber tracking results of four DTI software (Brainance, Philips FiberTrak, DSI Studio, NordicICE) on the reconstruction of representative WM tracts. MATERIALS AND METHODS: Ten healthy participants underwent 30-directional diffusion tensor imaging on a 3T-Philips Achieva TX MR-scanner. All data were analyzed by two independent sites of experienced raters with the aforementioned software and the following WM tracts were reconstructed: corticospinal tract (CST); forceps major (Fmajor); forceps minor (Fminor); cingulum bundle (CB); superior longitudinal fasciculus (SLF); inferior fronto-occipital fasciculus (IFOF). Visual inspection of the resulted tracts and statistical analysis (inter-rater and betweensoftware agreement; paired t-test) on fractional anisotropy (FA), axial and radial diffusivity (Daxial, Dradial) were applied for qualitative and quantitative evaluation of DTI software results. RESULTS: Qualitative evaluation of the extracted tracts confirmed anatomical landmarks at least for the core part of each tract, even though differences in the number of fibers extracted and the whole tract were evident, especially for the CST, Fmajor, Fminor and SLF. Descriptive values did not deviate from the expected range of values for healthy adult population. Substantial inter-rater agreement (intraclass correlation coefficient [ICC], Bland-Altman analysis) was found for all tracts (ICC; FA: 0.839-0.989, Daxial: 0.704-0.991, Dradial: 0.972-0.993). Low agreement for FA, Daxial and Dradial (ICC; Bland-Altman analysis) and significant paired t-test differences (p < 0.05) were detected regarding between-software agreement. CONCLUSIONS: Qualitative comparison of four different DTI software in addition to substantial inter-rater but poor between-software agreement highlight the differences on existing fiber tracking methodologies and several particularities of each WM tract, further supporting the need for further study in both clinical and research settings

Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm

Automatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging (MRI) could significantly improve clinical workflow and assist patient management. We have previously developed a novel artificial intelligence framework based on a 2.5D convolutional neural network achieving excellent results equivalent to those achieved by an independent human annotator. Here, we provide the first publicly-available annotated imaging dataset of VS by releasing the data and annotations used in our prior work. This collection contains a labelled dataset of 484 MR images collected on 242 consecutive patients with a VS undergoing Gamma Knife Stereotactic Radiosurgery at a single institution. Data includes all segmentations and contours used in treatment planning and details of the administered dose. Implementation of our automated segmentation algorithm uses MONAI, a freely-available open-source framework for deep learning in healthcare imaging. These data will facilitate the development and validation of automated segmentation frameworks for VS and may also be used to develop other multi-modal algorithmic models