Larson, Nicholas B.

Abstract Background and Purpose Artifact from cochlear implant electrodes degrades image resolution on CT. Here, we describe the use of coregistered pre‐ and postoperative CT images to reduce metallic artifact from the electrodes to assess its position more accurately within the cochlear lumen. Methods Pre‐ and postoperative CTs were reviewed after coregistration/overlay of both exams. Images were evaluated by two neuroradiologists for scalar location of electrodes tip (± scalar translocation), tip fold over, and angular depth of insertion. Results Thirty‐four patients were included in the final cohort. Transscalar migration was present in three (8.8%) cases (one case demonstrated tip fold over), with initial disagreement regarding transscalar migration in 1 out of 34 patients (2.9%). Agreement regarding depth of insertion was present in 31 (91.1%) cases. Five‐point Likert scales were used to compare the ability to resolve the proximity of electrodes to the lateral/outer cochlear wall without and with overlay, which is a qualitative measure of artifact from the array. Likert scores showed definitive benefit of metal artifact reduction using overlayed images with an average score of 4.34. Conclusion This study demonstrates a novel technique of using fused coregistration of pre‐ and postoperative CTs for the purpose of artifact reduction/electrode localization. It is anticipated that this technique will permit more accurate localization of the electrodes for improvement in surgical technique and electrode array design.

Abstract Context Metformin is the first-line drug for treating diabetes but has a high failure rate. Objective To identify demographic and clinical factors available in the electronic health record (EHR) that predict metformin failure. Methods A cohort of patients with at least 1 abnormal diabetes screening test that initiated metformin was identified at 3 sites (Arizona, Mississippi, and Minnesota). We identified 22 047 metformin initiators (48% female, mean age of 57 ± 14 years) including 2141 African Americans, 440 Asians, 962 Other/Multiracial, 1539 Hispanics, and 16 764 non-Hispanic White people. We defined metformin failure as either the lack of a target glycated hemoglobin (HbA1c) (<7%) within 18 months of index or the start of dual therapy. We used tree-based extreme gradient boosting (XGBoost) models to assess overall risk prediction performance and relative contribution of individual factors when using EHR data for risk of metformin failure. Results In this large diverse population, we observed a high rate of metformin failure (43%). The XGBoost model that included baseline HbA1c, age, sex, and race/ethnicity corresponded to high discrimination performance (C-index of 0.731; 95% CI 0.722, 0.740) for risk of metformin failure. Baseline HbA1c corresponded to the largest feature performance with higher levels associated with metformin failure. The addition of other clinical factors improved model performance (0.745; 95% CI 0.737, 0.754, P < .0001). Conclusion Baseline HbA1c was the strongest predictor of metformin failure and additional factors substantially improved performance suggesting that routinely available clinical data could be used to identify patients at high risk of metformin failure who might benefit from closer monitoring and earlier treatment intensification.

Objective: Investigate the performance of multiparametric MRI radiomic features, alone or combined with current standard-of-care methods, for pulmonary nodule classification. Assess the impact of segmentation variability on feature reproducibility and reliability. Methods: Radiomic features were extracted from 74 pulmonary nodules of 68 patients who underwent nodule resection or biopsy after MRI exam. The MRI features were compared with histopathology and conventional quantitative imaging values (maximum standardized uptake value [SUVmax] and mean Hounsfield unit [HU]) to determine whether MRI radiomic features can differentiate types of nodules and associate with SUVmax and HU using Wilcoxon rank sum test and linear regression. Diagnostic performance of features and four machine learning (ML) models were evaluated with area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs). Concordance correlation coefficient (CCC) assessed the segmentation variation impact on feature reproducibility and reliability. Results: Elevn diffusion-weighted features distinguished malignant from benign nodules (adjusted p < 0.05, AUC: 0.73–0.81). No features differentiated cancer types. Sixty-seven multiparametric features associated with mean CT HU and 14 correlated with SUVmax. All significant MRI features outperformed traditional imaging parameters (SUVmax, mean HU, apparent diffusion coefficient [ADC], T1, T2, dynamic contrast-enhanced imaging values) in distinguishing malignant from benign nodules with some achieving statistical significance (p < 0.05). Adding ADC and smoking history improved feature performance. Machine learning models demonstrated strong performance in nodule classification, with extreme gradient boosting (XGBoost) having the highest discrimination (AUC = 0.83, CI=[0.727, 0.932]). We found good to excellent inter- and intrareader feature reproducibility and reliability (CCC≥0.80). Conclusion: Eleven MRI radiomic features differentiated malignant from benign lung nodules, outperforming traditional quantitative methods. MRI radiomic ML models demonstrated good nodule classification performances with XGBoost superior to three others. There was good to excellent inter- and intrareader feature reproducibility and reliability. Advances in knowledge: Our study identified MRI radiomic features that successfully differentiated malignant from benign lung nodules and demonstrated high performance of our MR radiomic feature-based ML models for nodule classification. These new findings could help further establish thoracic MRI as a non-invasive and radiation-free alternative to standard practice for pulmonary nodule assessment.

Abstract Background Risk factors for the pathogenesis of chronic rhinosinusitis (CRS) remain largely undetermined, which is likely due to the heterogeneity of the disease. White blood cell counts have been largely unexplored as a risk factor for CRS even though different types of white blood cells are involved in the inflammatory process of CRS. Objective To investigate causal associations between different types of white blood cells on risk of CRS utilizing a Mendelian randomization (MR) analysis. Methods A two-sample MR analysis was performed using respective GWAS summary statistics for the exposure traits (neutrophil count, eosinophil count, basophil count, lymphocyte count, and monocyte count) and outcome trait (CRS). For the exposure traits, the European Bioinformatics Institute database of complete GWAS summary data was used. For the outcome trait, summary statistics for CRS GWAS were obtained from FinnGen. Primary analysis for MR was performed using inverse-variance weighted two-sample MR. Sensitivity analyses included weighted median, MR-Egger, and MR-PRESSO (raw and outlier-corrected). Results Eosinophils were associated with CRS (OR = 1.55 [95%CI 1.38,1.73]; p = 4.3E-14). Eosinophil results were similar across additional MR methods. MR results did not demonstrate significant causal relationships between neutrophils, lymphocytes, monocytes, or basophils with CRS. No significant pleiotropic bias was observed. Conclusions In a two-sample MR analysis, a potential causal link between blood eosinophil counts and CRS has been demonstrated. In addition, causal relationships between blood counts among other white blood cell types and CRS were not found. Further studies involving genetic variation in CRS are needed to corroborate genetic causal effects for CRS.

Abstract The human angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) proteins play key roles in the cellular internalization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus responsible for the coronavirus disease of 2019 (COVID-19) pandemic. We set out to functionally characterize the ACE2 and TMPRSS2 protein abundance for variant alleles encoding these proteins that contained non-synonymous single-nucleotide polymorphisms (nsSNPs) in their open reading frames (ORFs). Specifically, a high-throughput assay, deep mutational scanning (DMS), was employed to test the functional implications of nsSNPs, which are variants of uncertain significance in these two genes. Specifically, we used a ‘landing pad’ system designed to quantify the protein expression for 433 nsSNPs that have been observed in the ACE2 and TMPRSS2 ORFs and found that 8 of 127 ACE2, 19 of 157 TMPRSS2 isoform 1 and 13 of 149 TMPRSS2 isoform 2 variant proteins displayed less than ~25% of the wild-type protein expression, whereas 4 ACE2 variants displayed 25% or greater increases in protein expression. As a result, we concluded that nsSNPs in genes encoding ACE2 and TMPRSS2 might potentially influence SARS-CoV-2 infectivity. These results can now be applied to DNA sequence data for patients infected with SARS-CoV-2 to determine the possible impact of patient-based DNA sequence variation on the clinical course of SARS-CoV-2 infection.

Background The urine metabolites and chemistries that contribute to kidney stone formation are not fully understood. This study examined differences between the urine metabolic and chemistries profiles of first-time stone formers and controls. Methods High-resolution 1 H-nuclear magnetic resonance (NMR) spectroscopy-based metabolomic analysis was performed in 24-hour urine samples from a prospective cohort of 418 first-time symptomatic kidney stone formers and 440 controls. In total, 48 NMR-quantified metabolites in addition to 12 standard urine chemistries were assayed. Analysis of covariance was used to determine the association of stone former status with urine metabolites or chemistries after adjusting for age and sex and correcting for the false discovery rate. Gradient-boosted machine methods with nested cross-validation were applied to predict stone former status. Results Among the standard urine chemistries, stone formers had lower urine oxalate and potassium and higher urine calcium, phosphate, and creatinine. Among NMR urine metabolites, stone formers had lower hippuric acid, trigonelline, 2-furoylglycine, imidazole, and citrate and higher creatine and alanine. A cross-validated model using urine chemistries, age, and sex yielded a mean AUC of 0.76 (95% CI, 0.73 to 0.79). A cross-validated model using urine chemistries, NMR-quantified metabolites, age, and sex did not meaningfully improve the discrimination (mean AUC, 0.78; 95% CI, 0.75 to 0.81). In this combined model, among the top ten discriminating features, four were urine chemistries and six NMR-quantified metabolites. Conclusions Although NMR-quantified metabolites did not improve discrimination, several urine metabolic profiles were identified that may improve understanding of kidney stone pathogenesis.

3553 Background: Higher levels of plasma 25-hydroxyvitamin D [25(OH)D)] are associated with better outcomes in mCRC, but underlying biologic mechanisms are unknown. Key components of the VitD metabolic pathway include CYP27B1 (encodes 1-α-hydroxylase, converts 25(OH)D to active calcitriol), VitD receptor (VDR), and CYP24A1 (encodes 24-hydroxylase, degrades calcitriol and 25(OH)D into excreted metabolites). Since these factors may affect 25(OH)D levels and potentially mediate VitD activity in mCRC, we examined the relationship between tumoral gene expression (GEx) of CYP27B1, VDR, and CYP24A and pt outcome in a study nested in a randomized phase III trial of first-line chemotherapy plus biologics in mCRC pts, CALGB/SWOG 80405. Methods: We determined GEx of CYP27B1, VDR, and CYP24A1 by RNA sequencing (RNA-Seq) of archival tumor samples using the Illumina TruSeq platform. Primary endpoints were overall (OS) and progression-free survival (PFS). Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for prognostic and molecular characteristics. Results: 562 pts with RNA-Seq data were included. Pts with higher CYP27B1 expression (>median) were less likely to have BRAF wild type (WT) (79% vs 90%) compared to pts with lower expression (p=0.0007). Pts with higher VDR expression (>quartile 1 [Q1]) were younger (median age 59 vs 62 years; p=0.03), more likely to have left-sided (63% vs 46%; p=0.0005) and BRAF WT tumors (89% vs 70%; p<0.0001), and less likely to have RAS WT tumors (70% vs 80%; p=0.02) compared to pts with lower VDR. Pts with higher CYP24A1 expression (>median) were more likely to have left-sided tumors compared to pts with lower expression (63% vs 54%; p=0.03). On multivariable analysis, pts with higher CYP27B1 expression had significantly improved OS (HR 0.84; 95% CI, 0.75-0.93; p=0.002) and PFS (HR 0.89; 95% CI, 0.80-0.99; p=0.04). Higher VDR expression (up to Q1) was associated with significantly improved PFS (HR 0.69; 95% CI, 0.53-0.91; p=0.007) but not OS (HR 0.85; 95% CI, 0.66-1.09; p=0.20). Above Q1, this improvement attenuated. Higher CYP24A1 GEx was not associated with improved OS (HR 0.98; 95% CI, 0.88-1.08; p=0.66) or PFS (HR 0.98; 95% CI, 0.89-1.08; p=0.68). We found no significant interactions between GEx of CYP27B1, VDR, or CYP24A with baseline plasma 25(OH)D levels (p for interaction ≥0.10 for all). Conclusions: Our findings suggest an association between GEx of VitD pathway markers, particularly CYP27B1 and VDR, and survival in pts with mCRC, lending biologic plausibility to a role of VitD in CRC pathogenesis. Future studies are needed to confirm these findings and elucidate underlying mechanisms of action. Clinical trial information: NCT00265850 .