Citations

Abstract Background Non-invasive differentiation between schwannomas and neurofibromas is important for appropriate management, preoperative counseling, and surgical planning, but has proven difficult using conventional imaging. The objective of this study was to develop and evaluate machine learning approaches for differentiating peripheral schwannomas from neurofibromas. Methods We assembled a cohort of schwannomas and neurofibromas from 3 independent institutions and extracted high-dimensional radiomic features from gadolinium-enhanced, T1-weighted MRI using the PyRadiomics package on Quantitative Imaging Feature Pipeline. Age, sex, neurogenetic syndrome, spontaneous pain, and motor deficit were recorded. We evaluated the performance of 6 radiomics-based classifier models with and without clinical features and compared model performance against human expert evaluators. Results One hundred and seven schwannomas and 59 neurofibromas were included. The primary models included both clinical and imaging data. The accuracy of the human evaluators (0.765) did not significantly exceed the no-information rate (NIR), whereas the Support Vector Machine (0.929), Logistic Regression (0.929), and Random Forest (0.905) classifiers exceeded the NIR. Using the method of DeLong, the AUCs for the Logistic Regression (AUC = 0.923) and K Nearest Neighbor (AUC = 0.923) classifiers were significantly greater than the human evaluators (AUC = 0.766; p = 0.041). Conclusions The radiomics-based classifiers developed here proved to be more accurate and had a higher AUC on the ROC curve than expert human evaluators. This demonstrates that radiomics using routine MRI sequences and clinical features can aid in differentiation of peripheral schwannomas and neurofibromas.

4-Aminopyridine is a potential therapeutic option for a subgroup of patients with KCNA2 -encephalopathy and gain-of-function variants.

This prospective phase II clinical trial (Side Out 2) explored the clinical benefits of treatment selection informed by multi‐omic molecular profiling (MoMP) in refractory metastatic breast cancers (MBCs). Core needle biopsies were collected from 32 patients with MBC at trial enrollment. Patients had received an average of 3.94 previous lines of treatment in the metastatic setting before enrollment in this study. Samples underwent MoMP, including exome sequencing, RNA sequencing (RNA‐Seq), immunohistochemistry, and quantitative protein pathway activation mapping by Reverse Phase Protein Microarray (RPPA). Clinical benefit was assessed using the previously published growth modulation index (GMI) under the hypothesis that MoMP‐selected therapy would warrant further investigation for GMI ≥ 1.3 in ≥ 35% of the patients. Of the 32 patients enrolled, 29 received treatment based on their MoMP and 25 met the follow‐up criteria established by the trial protocol. Molecular information was delivered to the tumor board in a median time frame of 14 days (11–22 days), and targetable alterations for commercially available agents were found in 23/25 patients (92%). Of the 25 patients, 14 (56%) reached GMI ≥ 1.3. A high level of DNA topoisomerase I (TOPO1) led to the selection of irinotecan‐based treatments in 48% (12/25) of the patients. A pooled analysis suggested clinical benefit in patients with high TOPO1 expression receiving irinotecan‐based regimens (GMI ≥ 1.3 in 66.7% of cases). These results confirmed previous observations that MoMP increases the frequency of identifiable actionable alterations (92% of patients). The MoMP proposed allows the identification of biomarkers that are frequently expressed in MBCs and the evaluation of their role as predictors of response to commercially available agents. Lastly, this study confirmed the role of MoMP for informing treatment selection in refractory MBC patients: more than half of the enrolled patients reached a GMI ≥ 1.3 even after multiple lines of previous therapies for metastatic disease.

Abstract Background Clinical and molecular subcategories of inflammatory bowel disease (IBD) are needed to discover mechanisms of disease and predictors of response and disease relapse. We aimed to develop a study of a prospective adult research cohort with IBD (SPARC IBD) including longitudinal clinical and patient-reported data and biosamples. Methods We established a cohort of adults with IBD from a geographically diverse sample of patients across the United States with standardized data and biosample collection methods and sample processing techniques. At enrollment and at time of lower endoscopy, patient-reported outcomes (PRO), clinical data, and endoscopy scoring indices are captured. Patient-reported outcomes are collected quarterly. The quality of clinical data entry after the first year of the study was assessed. Results Through January 2020, 3029 patients were enrolled in SPARC, of whom 66.1% have Crohn’s disease (CD), 32.2% have ulcerative colitis (UC), and 1.7% have IBD-unclassified. Among patients enrolled, 990 underwent colonoscopy. Remission rates were 63.9% in the CD group and 80.6% in the UC group. In the quality study of the cohort, there was 96% agreement on year of diagnosis and 97% agreement on IBD subtype. There was 91% overall agreement describing UC extent as left-sided vs extensive or pancolitis. The overall agreement for CD behavior was 83%. Conclusion The SPARC IBD is an ongoing large prospective cohort with longitudinal standardized collection of clinical data, biosamples, and PROs representing a unique resource aimed to drive discovery of clinical and molecular markers that will meet the needs of precision medicine in IBD.

Background White matter abnormalities are a common feature of aging and Alzheimer disease, and tend to be more severe among Black individuals. However, the extent to which white matter abnormalities relate to amyloid deposition, a marker of Alzheimer pathology, remains unclear. This cross‐sectional study examined the association of white matter abnormalities with cortical amyloid in a community sample of older adults without dementia and examined the moderating effect of race. Methods and Results Participants from the ARIC‐PET (Atherosclerosis Risk in Communities‐Positron Emission Tomography) study underwent brain magnetic resonance imaging, which quantified white matter hyperintensity volume and microstructural integrity using diffusion tensor imaging. Participants received florbetapir positron emission tomography imaging to measure brain amyloid. Associations between measures of white matter structure and elevated amyloid status were examined using multivariable logistic regression. Among 322 participants (43% Black), each SD increase in white matter hyperintensity volume was associated with a greater odds of elevated amyloid (odds ratio [OR], 1.37; 95% CI, 1.03–1.83) after adjusting for demographic and cardiovascular risk factors. In race‐stratified analyses, a greater white matter hyperintensity volume was more strongly associated with elevated amyloid among Black participants (OR, 2.00; 95% CI, 1.15–3.50), compared with White participants (OR, 1.29; 95% CI, 0.89–1.89). However, the race interaction was not statistically significant ( P interaction=0.09). We found no association between white matter microstructure and elevated amyloid. Conclusions The results suggest a modest positive relationship between white matter hyperintensity and elevated amyloid in older adults without dementia. Although the results indicate that this association is nonsignificantly stronger among Black participants, these findings will need to be confirmed or refuted using larger multiracial cohorts.

Deep brain stimulation (DBS) is an effective neuromodulatory therapy for Parkinson’s disease (PD). Early studies using globus pallidus internus (GPi) DBS for PD profiled the nucleus as having two functional zones. This concept disseminated throughout the neuromodulation community as the “GPi triangle”. Although our understanding of the pallidum has greatly evolved over the past 20 years, we continue to reference the triangle in our clinical decision-making process. We propose a new direction, termed the spatial boundary hypothesis, to build upon the 2-dimensional outlook on GPi DBS. We believe an updated 3-D GPi model can produce more consistent, positive patient outcomes.