Cervical Cordotomy for Intractable Pain: Do Postoperative Imaging Features Correlate with Pain Outcomes and Mirror Pain?

BACKGROUND AND PURPOSE: Percutaneous cervical cordotomy offers relief of unilateral intractable oncologic pain. We aimed to find anatomic and postoperative imaging features that may correlate with clinical outcomes, including pain relief and postoperative contralateral pain. MATERIALS AND METHODS: We prospectively followed 15 patients with cancer who underwent cervical cordotomy for intractable pain during 2018 and 2019 and underwent preoperative and up to 1-month postoperative cervical MR imaging. Lesion volume and diameter were measured on T2-weighted imaging and diffusion tensor imaging (DTI). Lesion mean diffusivity and fractional anisotropy values were extracted. Pain improvement up to 1 month after surgery was assessed by the Numeric Rating Scale and Brief Pain Inventory. RESULTS: All patients reported pain relief from 8 (7-10) to 0 (0-4) immediately after surgery (P = .001), and 5 patients (33%) developed contralateral pain. The minimal percentages of the cord lesion volume required for pain relief were 10.0% on T2-weighted imaging and 6.2% on DTI. Smaller lesions on DWI correlated with pain improvement on the Brief Pain Inventory scale (r = 0.705, P = .023). Mean diffusivity and fractional anisotropy were significantly lower in the ablated tissue than contralateral nonlesioned tissue (P = .003 and P = .001, respectively), compatible with acute-phase tissue changes after injury. Minimal postoperative mean diffusivity values correlated with an improvement of Brief Pain Inventory severity scores (r = -0.821, P = .004). The average lesion mean diffusivity was lower among patients with postoperative contralateral pain (P = .037). CONCLUSIONS: Although a minimal ablation size is required during cordotomy, larger lesions do not indicate better outcomes. DWI metrics changes represent tissue damage after ablation and may correlate with pain outcomes.

Classification of High-Grade Glioma into Tumor and Nontumor Components Using Support Vector Machine.

BACKGROUND AND PURPOSE: Current imaging assessment of high-grade brain tumors relies on the Response Assessment in Neuro-Oncology criteria, which measure gross volume of enhancing and nonenhancing lesions from conventional MRI sequences. These assessments may fail to reliably distinguish tumor and nontumor. This study aimed to classify enhancing and nonenhancing lesion areas into tumor-versus-nontumor components. MATERIALS AND METHODS: A total of 140 MRI scans obtained from 32 patients with high-grade gliomas and 6 patients with brain metastases were included. Classification of lesion areas was performed using a support vector machine classifier trained on 4 components: enhancing and nonenhancing, tumor and nontumor, based on T1-weighted, FLAIR, and dynamic-contrast-enhancing MRI parameters. Classification results were evaluated by 2-fold cross-validation analysis of the training set and MR spectroscopy. Longitudinal changes of the component volumes were compared with Response Assessment in Neuro-Oncology criteria. RESULTS: Normalized T1-weighted values, FLAIR, plasma volume, volume transfer constant, and bolus-arrival-time parameters differentiated components. High sensitivity and specificity (100%) were obtained within the enhancing and nonenhancing areas. Longitudinal changes in component volumes correlated with the Response Assessment in Neuro-Oncology criteria in 27 patients; 5 patients (16%) demonstrated an increase in tumor component volumes indicating tumor progression. These changes preceded Response Assessment in Neuro-Oncology assessments by several months. Seven patients treated with bevacizumab showed a shift to an infiltrative pattern of progression. CONCLUSIONS: This study proposes an automatic classification method: segmented Response Assessment in Neuro-Oncology criteria based on advanced imaging that reliably differentiates tumor and nontumor components in high-grade gliomas. The segmented Response Assessment in Neuro-Oncology criteria may improve therapy-response assessment and provide earlier indication of progression.

Fast reference based MRI.

In many clinical MRI scenarios, existing imaging information can be used to significantly shorten acquisition time or improve Signal to Noise Ratio (SNR). In those cases, a previously acquired image can serve as a reference image, that may exhibit similarity in some sense to the image being acquired. Examples include similarity between adjacent slices in high resolution MRI, similarity between various contrasts in the same scans and similarity between different scans of the same patients. In this paper we present a general framework for utilizing reference images for fast MRI. We take into account that the reference image may exhibit low similarity with the acquired image and develop a hybrid adaptive-weighted approach for sampling and reconstruction. Experiments demonstrate the performance of the method in three different clinical MRI scenarios: SNR improvement in high resolution brain MRI, utilizing similarity between T2-weighted and fluid-attenuated inversion recovery (FLAIR) for fast FLAIR scanning and utilizing similarity between baseline and follow-up scans for fast follow-up scanning.

MRI multiparametric hemodynamic characterization of the normal brain.

Characterization of the brain's vascular system is of major clinical importance in the assessment of patients with cerebrovascular disease. The aim of this study was to characterize brain hemodynamics using multiparametric methods and to obtain reference values from the healthy brain. A multimodal magnetic resonance imaging (MRI) study was performed in twenty healthy subjects, including dynamic susceptibility contrast imaging and blood oxygen level dependence (BOLD) during hypercapnia and carbogen challenges. Brain tissues were defined using unsupervised cluster analysis based on these three methods, and several hemodynamic parameters were calculated for gray matter (GM), white matter (WM), blood vessels and dura (BVD); the three main vascular territories within the GM; and arteries and veins defined within the BVD cluster. The carbogen challenge produced a BOLD signal twice as high as the hypercapnia challenge, in all brain tissues. The three brain tissues differed significantly from each other in their hemodynamic characteristics, supporting the graded vascularity of the tissues, with BVD>GM>WM. Within the GM cluster, a significant delay of ∼1.2 s of the bolus arrival time was detected within the posterior cerebral artery territory relative to the middle and anterior cerebral artery territories. No differences were detected between right and left middle cerebral artery territories for all hemodynamic parameters. Within the BVD cluster, a significant delay of ∼1.9 s of the bolus arrival time was detected within the veins relative to the arteries. This parameter enabled to differentiate between the various blood vessels, including arteries, veins and choroid plexus. This study provides reference values for several hemodynamic parameters, obtained from healthy brains, and may be clinically important in the assessment of patients with various vascular pathologies.

Unsupervised multiparametric classification of dynamic susceptibility contrast imaging: study of the healthy brain.

Characterization and quantification of magnetic resonance perfusion images is important for clinical interpretation, though this calls for a reproducible and accurate method of analysis and a robust healthy reference. The few studies which have examined the perfusion of the healthy brain using dynamic susceptibility contrast (DSC) imaging were largely limited to manual definition of the regions of interest (ROI) and results were dependent on the location of the ROI. The current study aimed to develop a methodology for DSC data analysis and to obtain reference values of healthy subjects. Twenty three healthy volunteers underwent DSC. An unsupervised multiparametric clustering method was applied to four perfusion parameters. Three clusters were defined and identified as: dura-blood-vessels, gray matter and white matter and their vascular characteristics were obtained. Additionally, regional perfusion differences were studied and revealed a prolonged mean transient time and a trend for higher vascularity in the posterior compared with the anterior and middle cerebral vascular territories. While additional studies are required to confirm our findings, this result may have important clinical implications. The proposed unsupervised multiparametric method enabled accurate tissue differentiation, is easy replicable and has a wide range of applications in both pathological and healthy brains.