Aramchol in patients with nonalcoholic steatohepatitis: a randomized, double-blind, placebo-controlled phase 2b trial.

Nonalcoholic steatohepatitis (NASH), a chronic liver disease without an approved therapy, is associated with lipotoxicity and insulin resistance and is a major cause of cirrhosis and hepatocellular carcinoma. Aramchol, a partial inhibitor of hepatic stearoyl-CoA desaturase (SCD1) improved steatohepatitis and fibrosis in rodents and reduced steatosis in an early clinical trial. ARREST, a 52-week, double-blind, placebo-controlled, phase 2b trial randomized 247 patients with NASH (n = 101, n = 98 and n = 48 in the Aramchol 400 mg, 600 mg and placebo arms, respectively; NCT02279524 ). The primary end point was a decrease in hepatic triglycerides by magnetic resonance spectroscopy at 52 weeks with a dose of 600 mg of Aramchol. Key secondary end points included liver histology and alanine aminotransferase (ALT). Aramchol 600 mg produced a placebo-corrected decrease in liver triglycerides without meeting the prespecified significance (-3.1, 95% confidence interval (CI) -6.4 to 0.2, P = 0.066), precluding further formal statistical analysis. NASH resolution without worsening fibrosis was achieved in 16.7% (13 out of 78) of Aramchol 600 mg versus 5% (2 out of 40) of the placebo arm (odds ratio (OR) = 4.74, 95% CI = 0.99 to 22.7) and fibrosis improvement by ≥1 stage without worsening NASH in 29.5% versus 17.5% (OR = 1.88, 95% CI = 0.7 to 5.0), respectively. The placebo-corrected decrease in ALT for 600 mg was -29.1 IU l-1 (95% CI = -41.6 to -16.5). Early termination due to adverse events (AEs) was <5%, and Aramchol 600 and 400 mg were safe, well tolerated and without imbalance in serious or severe AEs between arms. Although the primary end point of a reduction in liver fat did not meet the prespecified significance level with Aramchol 600 mg, the observed safety and changes in liver histology and enzymes provide a rationale for SCD1 modulation as a promising therapy for NASH and fibrosis and are being evaluated in an ongoing phase 3 program.

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.

Tumor burden evaluation in NF1 patients with plexiform neurofibromas in daily clinical practice.

BACKGROUND: Existing volumetric measurements of plexiform neurofibromas (PNs) are time consuming and error prone, as they require delineation of PN boundaries, a procedure that is not practical in the typical clinical setting. The aim of this study is to assess the Plexiform Neurofibroma Instant Segmentation Tool (PNist), a novel semi-automated segmentation program that we developed for PN delineation in a clinical context. PNist was designed to greatly simplify volumetric assessment of PNs through use of an intuitive user interface while providing objectively consistent results with minimal interobserver and intraobserver variabilities in reasonable time. MATERIALS AND METHODS: PNs were measured in 30 magnetic resonance imaging (MRI) scans from 12 patients with neurofibromatosis 1. Volumetric measurements were performed using PNist and compared to a standard semi-automated volumetric method (Analyze 9.0). RESULTS: High correlation was detected between PNist and the semi-automated method (R(2) = 0.996), with a mean volume overlap error of 9.54 % and low intraobserver and interobserver variabilities. The segmentation time required for PNist was 60 % of the time required for Analyze 9.0 (360 versus 900 s, respectively). PNist was also reliable when assessing changes in tumor size over time, compared to the existing commercial method. CONCLUSIONS: Our study suggests that the new PNist method is accurate, intuitive, and less time consuming for PN segmentation compared to existing commercial volumetric methods. The workflow is simple and user-friendly, making it an important clinical tool to be used by radiologists, neurologists and neurosurgeons on a daily basis, helping them deal with the complex task of evaluating PN burden and progression.

Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI.

This paper presents an automatic method for the segmentation, internal classification and follow-up of optic pathway gliomas (OPGs) from multi-sequence MRI datasets. Our method starts with the automatic localization of the OPG and its core with an anatomical atlas followed by a binary voxel classification with a probabilistic tissue model whose parameters are estimated from the MR images. The method effectively incorporates prior location, tissue characteristics, and intensity information for the delineation of the OPG boundaries in a consistent and repeatable manner. Internal classification of the segmented OPG volume is then obtained with a robust method that overcomes grey-level differences between learning and testing datasets. Experimental results on 25 datasets yield a mean surface distance error of 0.73 mm as compared to manual segmentation by experienced radiologists. Our method exhibits reliable performance in OPG growth follow-up MR studies, which are crucial for monitoring disease progression. To the best of our knowledge, this is the first method that addresses automatic segmentation, internal classification, and follow-up of OPG.

31P NMR methods for the direct determination of ADP in the presence of ATP.

A new method is presented for the direct measurement of the amount of ADP in the presence of ATP by selectively eliminating the alpha- and gamma-ATP signals. The method is compared with other methods, both experimentally and theoretically, using the product-operator formalism. An analysis of the effect of beta 1 inhomogeneity on the efficiency of the methods is also given. Experimental results obtained using adiabatic pulses to compensate for such effects are shown. The accuracy of this new method is demonstrated by measuring various ADP concentrations in a series of solutions containing ADP and ATP.