Objective: This study assessed the relationships between the salience of amplitude modulation (AM) cues encoded at the auditory nerve (AN), perceptual sensitivity to changes in AM rate (i.e., AM rate discrimination threshold, AMRDT), and speech perception scores in postlingually deafened adult cochlear implant (CI) users. Design: Study participants were 18 postlingually deafened adults with Cochlear Nucleus devices, including five bilaterally implanted patients. For each of 23 implanted ears, neural encoding of AM cues at 20 Hz at the AN was evaluated at seven electrode locations across the electrode array using electrophysiological measures of the electrically evoked compound action potential (eCAP). The salience of AM neural encoding was quantified by the Modulated Response Amplitude Ratio (MRAR). Psychophysical measures of AMRDT for 20 Hz modulation were evaluated in 16 ears using a three-alternative, forced-choice procedure, targeting 79.4% correct on the psychometric function. AMRDT was measured at up to five electrode locations for each test ear, including the electrode pair that showed the largest difference in the MRAR. Consonant-Nucleus-Consonant (CNC) word scores presented in quiet and in speech-shaped noise at a signal to noise ratio (SNR) of +10 dB were measured in all 23 implanted ears. Simulation tests were used to assess the variations in correlation results when using the MRAR and AMRDT measured at only one electrode location in each participant to correlate with CNC word scores. Linear Mixed Models (LMMs) were used to evaluate the relationship between MRARs/AMRDTs measured at individual electrode locations and CNC word scores. Spearman Rank correlation tests were used to evaluate the strength of association between CNC word scores measured in quiet and in noise with (1) the variances in MRARs and AMRDTs, and (2) the averaged MRAR or AMRDT across multiple electrodes tested for each participant. Results: There was no association between the MRAR and AMRDT. Using the MRAR and AMRDT measured at only one, randomly selected electrode location to assess their associations with CNC word scores could lead to opposite conclusions. Both the results of LMMs and Spearman Rank correlation tests showed that CNC word scores measured in quiet or at 10 dB SNR were not significantly correlated with the MRAR or AMRDT. In addition, the results of Spearman Rank correlation tests showed that the variances in MRARs and AMRDTs were not significantly correlated with CNC word scores measured in quiet or in noise. Conclusions: The difference in AN sensitivity to AM cues is not the primary factor accounting for the variation in AMRDTs measured at different stimulation sites within individual CI users. The AN sensitivity to AM per se may not be a crucial factor for CNC word perception in quiet or at 10 dB SNR in postlingually deafened adult CI users. Using electrophysiological or psychophysical results measured at only one electrode location to correlate with speech perception scores in CI users can lead to inaccurate,...

Background: Sinonasal malignancy (SNM) is a heterogeneous group of diseases for which induction chemotherapy (IC) may reduce tumor burden.The purpose of this study was to characterize the response to IC in SNM as a prognostic factor through its effect on survival.Methods: Retrospective cohort of patients undergoing IC for SNM between 2010 and 2019 at our quaternary referral center.Results: Forty-two patients with advanced SNM were included in the analysis.Patients with a favorable response to IC had higher survival rates than those who had an unfavorable response (5-year OS: 66.8% vs. 9.7%; p < 0.001; PFS: 56.8% vs. 0%; p < 0.001). Conclusions: Response to IC in our patient cohort was a prognostic indicator of overall response to treatment. Further elucidation of predictors of response is needed for appropriate patient selection.

Tumor Whole Slide Images (WSI) are often heterogeneous, which hinders the discovery of biomarkers in the presence of confounding clinical factors. In this study, we present a pipeline for identifying biomarkers from the Glioblastoma Multiforme (GBM) cohort of WSIs from TCGA archive. The GBM cohort endures many technical artifacts while the discovery of GBM biomarkers is challenged because “age” is the single most confounding factor for predicting outcomes. The proposed approach relies on interpretable features (e.g., nuclear morphometric indices), effective similarity metrics for heterogeneity analysis, and robust statistics for identifying biomarkers. The pipeline first removes artifacts (e.g., pen marks) and partitions each WSI into patches for nuclear segmentation via an extended U-Net for subsequent quantitative representation. Given the variations in fixation and staining that can artificially modulate hematoxylin optical density (HOD), we extended Navab’s Lab method to normalize images and reduce the impact of batch effects. The heterogeneity of each WSI is then represented either as probability density functions (PDF) per patient or as the composition of a dictionary predicted from the entire cohort of WSIs. For PDF- or dictionary-based methods, morphometric subtypes are constructed based on distances computed from optimal transport and linkage analysis or consensus clustering with Euclidean distances, respectively. For each inferred subtype, Kaplan–Meier and/or the Cox regression model are used to regress the survival time. Since age is the single most important confounder for predicting survival in GBM and there is an observed violation of the proportionality assumption in the Cox model, we use both age and age-squared coupled with the Likelihood ratio test and forest plots for evaluating competing statistics. Next, the PDF- and dictionary-based methods are combined to identify biomarkers that are predictive of survival. The combined model has the advantage of integrating global (e.g., cohort scale) and local (e.g., patient scale) attributes of morphometric heterogeneity, coupled with robust statistics, to reveal stable biomarkers. The results indicate that, after normalization of the GBM cohort, mean HOD, eccentricity, and cellularity are predictive of survival. Finally, we also stratified the GBM cohort as a function of EGFR expression and published genomic subtypes to reveal genomic-dependent morphometric biomarkers.