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BACKGROUND AND PURPOSE: Normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) computed from dynamic susceptibility contrast (DSC) perfusion imaging are useful biomarkers for differential diagnosis and treatment response assessment in brain tumors. However, their measurements are dependent on DSC acquisition factors, and CBV-optimized protocols technically differ from PSR-optimized protocols. This study aimed to generate "synthetic" DSC data with adjustable synthetic acquisition parameters using dual-echo gradient-echo (GE) DSC datasets extracted from dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI). Synthetic DSC was aimed at: 1) simultaneously create nrCBV and PSR maps using optimal sequence parameters, 2) compare DSC datasets with heterogeneous external cohorts, and 3) assess the impact of acquisition factors on DSC metrics. MATERIALS AND METHODS: Thirty-eight patients with contrast-enhancing brain tumors were prospectively imaged with dynamic SAGE-EPI during a non-preloaded single-dose contrast injection and included in this cross-sectional study. Multiple synthetic DSC curves with desired pulse sequence parameters were generated using the Bloch equations applied to the dual-echo GE data extracted from dynamic SAGE-EPI datasets, with or without optional preload simulation. RESULTS: Dynamic SAGE-EPI allowed for simultaneous generation of CBV-optimized and PSR-optimized DSC datasets with a single contrast injection, while PSR computation from guideline-compliant CBV-optimized protocols resulted in rank variations within the cohort (Spearman's ρ=0.83-0.89, i.e. 31%-21% rank variation). Treatment-naïve glioblastoma exhibited lower parameter-matched PSR compared to the external cohorts of treatment-naïve primary CNS lymphomas (PCNSL) (p<0.0001), supporting a role of synthetic DSC for multicenter comparisons. Acquisition factors highly impacted PSR, and nrCBV without leakage correction also showed parameter-dependence, although less pronounced. However, this dependence was remarkably mitigated by post-hoc leakage correction. CONCLUSIONS: Dynamic SAGE-EPI allows for simultaneous generation of CBV-optimized and PSR-optimized DSC data with one acquisition and a single contrast injection, facilitating the use of a single perfusion protocol for all DSC applications. This approach may also be useful for comparisons of perfusion metrics across heterogeneous multicenter datasets, as it facilitates post-hoc harmonization. ABBREVIATIONS: DSC = dynamic susceptibility contrast; FA = flip angle; GBCA = gadolinium-based contrast agent; GBM = glioblastoma; GE = gradient echo; IDH = isocitrate dehydrogenase; IDHm = IDH-mutant; IDHwt = IDH-wild-type; 1p19qcod = 1p19q codeleted; 1p19qint = 1p19q intact; MRI = magnetic resonance imaging; PCNSL = primary CNS lymphoma; PSR = percentage of signal recovery; Rec = recurrent; SAGE-EPI = spin-and-gradient-echo echoplanar imaging; CBV = cerebral blood volume; nrCBV = normalized relative CBV; ROI = region of interest; TE = echo time; TN = treatment-naïve; TR = repetition time.

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Autism spectrum disorder (autism) is a prevalent neurodevelopmental condition characterized by early emerging impairments in social behavior and communication. EEG represents a powerful and non-invasive tool for examining functional brain differences in autism. Recent EEG evidence suggests that greater intra-individual trial-to-trial variability across EEG responses in stimulus-related tasks may characterize brain differences in autism. Traditional analysis of EEG data largely focuses on mean trends of the trial-averaged data, where trial-level analysis is rarely performed due to low neural signal to noise ratio. We propose to use nonlinear (shape-invariant) mixed effects (NLME) models to study intra-individual inter-trial EEG response variability using trial-level EEG data. By providing more precise metrics of response variability, this approach could enrich our understanding of neural disparities in autism and potentially aid the identification of objective markers. The proposed multilevel NLME models quantify variability in the signal's interpretable and widely recognized features (e.g., latency and amplitude) while also regularizing estimation based on noisy trial-level data. Even though NLME models have been studied for more than three decades, existing methods cannot scale up to large data sets. We propose computationally feasible estimation and inference methods via the use of a novel minorization-maximization (MM) algorithm. Extensive simulations are conducted to show the efficacy of the proposed procedures. Applications to data from a large national consortium find that children with autism have larger intra-individual inter-trial variability in P1 latency in a visual evoked potential (VEP) task, compared to their neurotypical peers.

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BACKGROUND AND PURPOSE: The T2-FLAIR mismatch sign on MR imaging is a highly specific imaging biomarker of isocitrate dehydrogenase (IDH)-mutant astrocytomas, which lack 1p/19q codeletion. However, most studies using the T2-FLAIR mismatch sign have used visual assessment. This study quantified the degree of T2-FLAIR mismatch using digital subtraction of fluid-nulled T2-weighted FLAIR images from non-fluid-nulled T2-weighted images in human nonenhancing diffuse gliomas and then used this information to assess improvements in diagnostic performance and investigate subregion characteristics within these lesions. MATERIALS AND METHODS: Two cohorts of treatment-naïve, nonenhancing gliomas with known IDH and 1p/19q status were studied (n = 71 from The Cancer Imaging Archive (TCIA) and n = 34 in the institutional cohort). 3D volumes of interest corresponding to the tumor were segmented, and digital subtraction maps of T2-weighted MR imaging minus T2-weighted FLAIR MR imaging were used to partition each volume of interest into a T2-FLAIR mismatched subregion (T2-FLAIR mismatch, corresponding to voxels with positive values on the subtraction maps) and nonmismatched subregion (T2-FLAIR nonmismatch corresponding to voxels with negative values on the subtraction maps). Tumor subregion volumes, percentage of T2-FLAIR mismatch volume, and T2-FLAIR nonmismatch subregion thickness were calculated, and 2 radiologists assessed the T2-FLAIR mismatch sign with and without the aid of T2-FLAIR subtraction maps. RESULTS: Thresholds of ≥42% T2-FLAIR mismatch volume classified IDH-mutant astrocytoma with a specificity/sensitivity of 100%/19.6% (TCIA) and 100%/31.6% (institutional); ≥25% T2-FLAIR mismatch volume showed 92.0%/32.6% and 100%/63.2% specificity/sensitivity, and ≥15% T2-FLAIR mismatch volume showed 88.0%/39.1% and 93.3%/79.0% specificity/sensitivity. In IDH-mutant astrocytomas with ≥15% T2-FLAIR mismatch volume, T2-FLAIR nonmismatch subregion thickness was negatively correlated with the percentage T2-FLAIR mismatch volume (P < .0001) across both cohorts. The percentage T2-FLAIR mismatch volume was higher in grades 3-4 compared with grade 2 IDH-mutant astrocytomas (P < .05), and ≥15% T2-FLAIR mismatch volume IDH-mutant astrocytomas were significantly larger than <15% T2-FLAIR mismatch volume IDH-mutant astrocytoma (P < .05) across both cohorts. When evaluated by 2 radiologists, the additional use of T2-FLAIR subtraction maps did not show a significant difference in interreader agreement, sensitivity, or specificity compared with a separate evaluation of T2-FLAIR and T2-weighted MR imaging alone. CONCLUSIONS: T2-FLAIR digital subtraction maps may be a useful, automated tool to obtain objective segmentations of tumor subregions based on quantitative thresholds for classifying IDH-mutant astrocytomas using the percentage T2 FLAIR mismatch volume with 100% specificity and exploring T2-FLAIR mismatch/T2-FLAIR nonmismatch subregion characteristics. Conversely, the addition of T2-FLAIR subtraction maps did not enhance the sensitivity or specificity of the visual T2-FLAIR mismatch sign assessment by experienced radiologists.

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OBJECTIVE: The objective of this study was to identify baseline clinical and radiological characteristics of brain metastases (BMs) associated with a higher probability of lesion-specific progression-free survival (PFS-L) after laser interstitial thermal therapy (LITT). METHODS: A total of 47 lesions in 42 patients with BMs treated with LITT were retrospectively examined, including newly diagnosed BM, suspected recurrent BM, and suspected radiation necrosis. The association of baseline clinical and radiological features with PFS-L was assessed using survival analyses. Radiological features included lesion size measurements, diffusion and perfusion metrics, and sphericity, which is a radiomic feature ranging from 1 (perfect sphere) to 0. RESULTS: The probability of PFS-L for the entire cohort was 88.0% at 3 months, 70.6% at 6 months, 67.4% at 1 and 2 years, and 62.2% at 3 years. For lesions progressing after LITT (n = 13), the median time to progression was 3.9 months, and most lesions (n = 11) progressed within 6 months after LITT. In lesions showing response to LITT (n = 17), the median time to response was 12.1 months. All 3 newly diagnosed BMs showed a long-term response. The mean (± SD) follow-up duration for all censored lesions (n = 34) was 20.7 ± 19.4 months (range 12 days to 6.1 years). The mean pretreatment enhancing volume was 2.68 cm3 and the mean sphericity was 0.70. Pretreatment small enhancing volume (p = 0.003) and high sphericity (p = 0.024) computed from lesion segmentation predicted a longer PFS-L after LITT. Lesions meeting optimal cutoffs of either enhancing volume < 2.5 cm3 (adjusted p = 0.004) or sphericity ≥ 0.705 (adjusted p = 0.019) had longer PFS-L, and their probability of PFS-L was 86.8% at 3 years. Lesions meeting both cutoffs showed a cumulative benefit (p < 0.0001), with a 100% probability of PFS-L at 3 years, which was unchanged at the end of follow-up (4.1 years). Manually computed estimates of lesion size (maximal axial diameter, p = 0.011) and sphericity (p = 0.043) were also predictors of PFS-L. Optimal cutoffs of diameter < 2 cm (adjusted p = 0.035) or manual sphericity ≥ 0.91 (adjusted p = 0.092) identified lesions with longer PFS-L, and lesions meeting both cutoffs showed a cumulative benefit (p = 0.0023). Baseline diffusion imaging did not predict PFS-L. A subset of lesions (n = 7) with highly perfused hotspots had worse PFS-L (adjusted p = 0.010), but perfusion signal contamination from vessels and cortex and underlying size differences were possible confounders. CONCLUSIONS: Small size and high sphericity are ideal baseline features for lesions considered for LITT treatment, with a cumulative PFS-L benefit when both features are present, that could aid patient selection.

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OBJECTIVE: Although seizures are a relatively common phenomenon in the setting of brain metastases (BMs), there are no discrete recommendations regarding the use of antiepileptic drugs (AEDs) in this population, either in general or in the context of treatment. The authors' aim was to better understand the underlying pathological factors as well as the therapeutic techniques that may lead to seizures following the radiosurgical treatment of BMs with the goal of guiding appropriate AED prophylaxis. METHODS: Adult patients with BMs diagnosed from 2013 to 2020 at a single academic institution and treated with radiation therapy were included in this study. The authors evaluated factors associated with the incidence of seizures throughout the disease course, with a focus on seizures in the 90-day period following stereotactic radiosurgery (SRS). RESULTS: Four hundred forty-four patients with newly diagnosed BMs were identified, 10% of whom had seizures at the time of presentation and 28% of whom had a seizure at any point during the study period. Tumor histology was significantly associated with initial seizure risk. AED use was highly variable. In the 90-day post-SRS period, the summed total planning target volume (PTV) was independently predictive of post-SRS seizures, regardless of the fractionation scheme (single fraction vs hypofractionated) and other clinical factors. The number of supratentorial BMs was not predictive of post-SRS seizures. CONCLUSIONS: PTV is a superior predictor of post-SRS seizures relative to the number of supratentorial BMs, as it serves as a volumetric proxy for intracranial disease burden. A larger PTV, alongside tumor histology and prior seizure history, should be considered in the decision-making process for AED use following radiosurgery.

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Background: Alterations in tumor growth rate (TGR) in recurrent glioblastoma (rGBM) after treatment may be useful for identifying therapeutic activity. The aim of this study was to assess the impact of volumetric TGR alterations on overall survival (OS) in rGBM treated with chemotherapy with or without radiation therapy (RT). Methods: Sixty-one rGBM patients treated with chemotherapy with or without concomitant radiation therapy (RT) at 1st or 2nd recurrence were retrospectively examined. Pre- and post-treatment contrast enhancing volumes were computed. Patients were considered "responders" if they reached progression-free survival at 6 months (PFS6) and showed a decrease in TGR after treatment and "non-responders" if they didn't reach PFS6 or if TGR increased. Results: Stratification by PFS6 and based on TGR resulted in significant differences in OS both for all patients and for patients without RT (P < 0.05). A decrease of TGR (P = 0.009), smaller baseline tumor volume (P = 0.02), O6-methylguanine-DNA methyltransferase promoter methylation (P = 0.048) and fewer number of recurrences (P = 0.048) were significantly associated with longer OS after controlling for age, sex and concomitant RT. Conclusion: A decrease in TGR in patients with PFS6, along with smaller baseline tumor volume, were associated with a significantly longer OS in rGBM treated with chemotherapy with or without radiation. Importantly, all patients that exhibited PFS6 also showed a measurable decrease in TGR.

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PURPOSE: Antiangiogenic therapies are known to cause high radiographic response rates due to reduction in vascular permeability resulting in a lower degree of contrast extravasation. In this study, we investigate the prognostic ability for model-derived parameters describing enhancing tumor volumetric dynamics to predict survival in recurrent glioblastoma treated with antiangiogenic therapy. EXPERIMENTAL DESIGN: N = 276 patients in two phase II trials were used as training data, including bevacizumab ± irinotecan (NCT00345163) and cabozantinib (NCT00704288), and N = 74 patients in the bevacizumab arm of a phase III trial (NCT02511405) were used for validation. Enhancing volumes were estimated using T1 subtraction maps, and a biexponential model was used to estimate regrowth (g) and regression (d) rates, time to tumor regrowth (TTG), and the depth of response (DpR). Response characteristics were compared to diffusion MR phenotypes previously shown to predict survival. RESULTS: Optimized thresholds occurred at g = 0.07 months-1 (phase II: HR = 0.2579, P = 5 × 10-20; phase III: HR = 0.2197, P = 5 × 10-5); d = 0.11 months-1 (HR = 0.3365, P < 0.0001; HR = 0.3675, P = 0.0113); TTG = 3.8 months (HR = 0.2702, P = 6 × 10-17; HR = 0.2061, P = 2 × 10-5); and DpR = 11.3% (HR = 0.6326, P = 0.0028; HR = 0.4785, P = 0.0206). Multivariable Cox regression controlling for age and baseline tumor volume confirmed these factors as significant predictors of survival. Patients with a favorable pretreatment diffusion MRI phenotype had a significantly longer TTG and slower regrowth. CONCLUSIONS: Recurrent glioblastoma patients with a large, durable radiographic response to antiangiogenic agents have significantly longer survival. This information is useful for interpreting activity of antiangiogenic agents in recurrent glioblastoma.

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Dialysis patients experience frequent hospitalizations and a higher mortality rate compared to other Medicare populations, in whom hospitalizations are a major contributor to morbidity, mortality, and healthcare costs. Patients also typically remain on dialysis for the duration of their lives or until kidney transplantation. Hence, there is growing interest in studying the spatiotemporal trends in the correlated outcomes of hospitalization and mortality among dialysis patients as a function of time starting from transition to dialysis across the United States Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multivariate spatiotemporal functional principal component analysis model to study the joint spatiotemporal patterns of hospitalization and mortality rates among dialysis patients. The proposal is based on a multivariate Karhunen-Loéve expansion that describes leading directions of variation across time and induces spatial correlations among region-specific scores. An efficient estimation procedure is proposed using only univariate principal components decompositions and a Markov Chain Monte Carlo framework for targeting the spatial correlations. The finite sample performance of the proposed method is studied through simulations. Novel applications to the USRDS data highlight hot spots across the United States with higher hospitalization and/or mortality rates and time periods of elevated risk.

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Eye tracking (ET) experiments commonly record the continuous trajectory of a subject's gaze on a two-dimensional screen throughout repeated presentations of stimuli (referred to as trials). Even though the continuous path of gaze is recorded during each trial, commonly derived outcomes for analysis collapse the data into simple summaries, such as looking times in regions of interest, latency to looking at stimuli, number of stimuli viewed, number of fixations or fixation length. In order to retain information in trial time, we utilize functional data analysis (FDA) for the first time in literature in the analysis of ET data. More specifically, novel functional outcomes for ET data, referred to as viewing profiles, are introduced that capture the common gazing trends across trial time which are lost in traditional data summaries. Mean and variation of the proposed functional outcomes across subjects are then modeled using functional principal components analysis. Applications to data from a visual exploration paradigm conducted by the Autism Biomarkers Consortium for Clinical Trials showcase the novel insights gained from the proposed FDA approach, including significant group differences between children diagnosed with autism and their typically developing peers in their consistency of looking at faces early on in trial time.

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Predictors of genitourinary toxicity after post-prostatectomy radiotherapy remain elusive. A previously defined germline DNA signature (PROSTOX) has shown predictive ability for late grade ≥ 2 GU toxicity after intact prostate stereotactic body radiotherapy. We explore whether PROSTOX would predict toxicity among patients receiving post-prostatectomy SBRT on a phase II clinical trial.

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Bayesian methods provide direct inference in functional data analysis applications without reliance on bootstrap techniques. A major tool in functional data applications is the functional principal component analysis which decomposes the data around a common mean function and identifies leading directions of variation. Bayesian functional principal components analysis (BFPCA) provides uncertainty quantification on the estimated functional model components via the posterior samples obtained. We propose central posterior envelopes (CPEs) for BFPCA based on functional depth as a descriptive visualization tool to summarize variation in the posterior samples of the estimated functional model components, contributing to uncertainty quantification in BFPCA. The proposed BFPCA relies on a latent factor model and targets model parameters within a mixed effects modeling framework using modified multiplicative gamma process shrinkage priors on the variance components. Functional depth provides a center-outward order to a sample of functions. We utilize modified band depth and modified volume depth for ordering of a sample of functions and surfaces, respectively, to derive at CPEs of the mean and eigenfunctions within the BFPCA framework. The proposed CPEs are showcased in extensive simulations. Finally, the proposed CPEs are applied to the analysis of a sample of power spectral densities (PSD) from resting state electroencephalography (EEG) where they lead to novel insights on diagnostic group differences among children diagnosed with autism spectrum disorder and their typically developing peers across age.

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EEG experiments yield high-dimensional event-related potential (ERP) data in response to repeatedly presented stimuli throughout the experiment. Changes in the high-dimensional ERP signal throughout the duration of an experiment (longitudinally) is the main quantity of interest in learning paradigms, where they represent the learning dynamics. Typical analysis, which can be performed in the time or the frequency domain, average the ERP waveform across all trials, leading to the loss of the potentially valuable longitudinal information in the data. Longitudinal time-frequency transformation of ERP (LTFT-ERP) is proposed to retain information from both the time and frequency domains, offering distinct but complementary information on the underlying cognitive processes evoked, while still retaining the longitudinal dynamics in the ERP waveforms. LTFT-ERP begins by time-frequency transformations of the ERP data, collected across subjects, electrodes, conditions and trials throughout the duration of the experiment, followed by a data driven multidimensional principal components analysis (PCA) approach for dimension reduction. Following projection of the data onto leading directions of variation in the time and frequency domains, longitudinal learning dynamics are modeled within a mixed effects modeling framework. Applications to a learning paradigm in autism depict distinct learning patterns throughout the experiment among children diagnosed with Autism Spectrum Disorder and their typically developing peers. LTFT-ERP time-frequency joint transformations are shown to bring an additional level of specificity to interpretations of the longitudinal learning patterns related to underlying cognitive processes, which is lacking in single domain analysis (in the time or the frequency domain only). Simulation studies show the efficacy of the proposed methodology.

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Background: Containing coronavirus disease 2019 (COVID-19) has been difficult, due to both the large number of asymptomatic infected individuals and the long duration of infection. Managing these challenges requires understanding of the differences between asymptomatic vs symptomatic patients and those with a longer duration of infectivity. Methods: Individuals from Los Angeles were tested for COVID-19, and a group positive for COVID-19 chose to have follow-up testing. Associations between symptoms and demographic factors, viral burden measured by cycle threshold (CT) value, and duration of polymerase chain reaction (PCR) positivity were analyzed. Results: Eighteen point eight percent of patients were positive for COVID-19. Asymptomatic COVID-19-positive patients were significantly younger than symptomatic patients (2.6 years; P < .001). There were no differences in average CT between asymptomatic and symptomatic patients. The estimated median duration of COVID-19 PCR positivity was 23 days. Being asymptomatic throughout the course of infection was the only factor associated with a shorter course of COVID-19 PCR positivity (21 vs 28 days; P = .002). Conclusions: We found important differences and similarities between asymptomatic and symptomatic COVID-19-positive patients, the most meaningful being a similar level of virus as measured by PCR, but a shorter duration of PCR positivity for asymptomatic patients. These findings suggest that asymptomatic patients may have more efficient clearance of virus, which may be relevant for management and screening.

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Electroencephalography experiments produce region-referenced functional data representing brain signals in the time or the frequency domain collected across the scalp. The data typically also have a multilevel structure with high-dimensional observations collected across multiple experimental conditions or visits. Common analysis approaches reduce the data complexity by collapsing the functional and regional dimensions, where event-related potential (ERP) features or band power are targeted in a pre-specified scalp region. This practice can fail to portray more comprehensive differences in the entire ERP signal or the power spectral density (PSD) across the scalp. Building on the weak separability of the high-dimensional covariance process, the proposed multilevel hybrid principal components analysis (M-HPCA) utilizes dimension reduction tools from both vector and functional principal components analysis to decompose the total variation into between- and within-subject variance. The resulting model components are estimated in a mixed effects modeling framework via a computationally efficient minorization-maximization algorithm coupled with bootstrap. The diverse array of applications of M-HPCA is showcased with two studies of individuals with autism. While ERP responses to match vs mismatch conditions are compared in an audio odd-ball paradigm in the first study, short-term reliability of the PSD across visits is compared in the second. Finite sample properties of the proposed methodology are studied in extensive simulations.

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BACKGROUND: There is great interest in finding ways to identify patients who will develop toxicity to cancer therapies. This has become especially pressing in the era of immune therapy, where toxicity can be long-lasting and life-altering, and primarily comes in the form of immune-related adverse effects (irAEs). Treatment with the first drugs in this class, anti-programmed death 1 (anti-PD1)/programmed death-ligand 1 (PDL1) checkpoint therapies, results in grade 2 or higher irAEs in up to 25%-30% of patients, which occur most commonly within the first 6 months of treatment and can include arthralgias, rash, pruritus, pneumonitis, diarrhea and/or colitis, hepatitis, and endocrinopathies. We tested the hypothesis that germline microRNA pathway functional variants, known to predict altered systemic stress responses to cancer therapies, would predict irAEs in patients across cancer types. METHODS: MicroRNA pathway variants were evaluated for an association with grade 2 or higher toxicity using four classifiers on 62 patients with melanoma, and then the panel's performance was validated on 99 patients with other cancer types. Trained classifiers included classification trees, LASSO-regularized logistic regression, boosted trees, and random forests. Final performance measures were reported on the training set using leave-one-out cross validation and validated on held-out samples. The predicted probability of toxicity was evaluated for its association, if any, with response categories to anti-PD1/PDL1 therapy in the melanoma cohort. RESULTS: A biomarker panel was identified that predicts toxicity with 80% accuracy (F1=0.76, area under the curve (AUC)=0.82) in the melanoma training cohort and 77.6% accuracy (F1=0.621, AUC=0.778) in the pan-cancer validation cohort. In the melanoma cohort, the predictive probability of toxicity was not associated with response categories to anti-PD1/PDL1 therapy (p=0.70). In the same cohort, the most significant biomarker of toxicity in RAC1, predicting a greater than ninefold increased risk of toxicity (p<0.001), was also not associated with response to anti-PD1/PDL1 therapy (p=0.151). CONCLUSIONS: A germline microRNA-based biomarker signature predicts grade 2 and higher irAEs to anti-PD1/PDL1 therapy, regardless of tumor type, in a pan-cancer manner. These findings represent an important step toward personalizing checkpoint therapy, the use of which is growing rapidly.

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BACKGROUND AND PURPOSE: The purpose of this study was to determine whether single nucleotide polymorphisms disrupting microRNA targets (mirSNPs) can serve as predictive biomarkers for toxicity after radiotherapy for prostate cancer and whether these may be differentially predictive depending on radiation fractionation. MATERIALS AND METHODS: We identified 201 men treated with two forms of definitive radiotherapy for prostate cancer at two institutions: 108 men received conventionally-fractionated radiotherapy (CF-RT) and 93 received stereotactic body radiotherapy (SBRT). Germline DNA was evaluated for the presence of functional mirSNPs. Random forest, boosted trees and elastic net models were developed to predict late grade ≥2 GU toxicity by the RTOG scale. RESULTS: The crude incidence of late grade ≥2 GU toxicity was 16% after CF-RT and 15% after SBRT. An elastic net model based on 22 mirSNPs differentiated CF-RT patients at high risk (71.5%) versus low risk (7.5%) for toxicity, with an area under the curve (AUC) values of 0.76-0.81. An elastic net model based on 32 mirSNPs differentiated SBRT patients at high risk (64.7%) versus low risk (3.9%) for toxicity, with an area under the curve (AUC) values of 0.81-0.87. These models were specific to treatment type delivered. Prospective studies are warranted to further validate these results. CONCLUSION: Predictive models using germline mirSNPs have high accuracy for predicting late grade ≥2 GU toxicity after either CF-RT or SBRT, and are unique for each treatment, suggesting that germline predictors of late radiation sensitivity are fractionation-dependent. Prospective studies are warranted to further validate these results.

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Multi-dimensional functional data arises in numerous modern scientific experimental and observational studies. In this article, we focus on longitudinal functional data, a structured form of multidimensional functional data. Operating within a longitudinal functional framework we aim to capture low dimensional interpretable features. We propose a computationally efficient nonparametric Bayesian method to simultaneously smooth observed data, estimate conditional functional means and functional covariance surfaces. Statistical inference is based on Monte Carlo samples from the posterior measure through adaptive blocked Gibbs sampling. Several operative characteristics associated with the proposed modeling framework are assessed comparatively in a simulated environment. We illustrate the application of our work in two case studies. The first case study involves age-specific fertility collected over time for various countries. The second case study is an implicit learning experiment in children with autism spectrum disorder.

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OBJECTIVE: It is unclear whether relatively low glycated haemoglobin (HbA1c) levels are beneficial or harmful for the long-term health outcomes among people without diabetes. We aimed to investigate the association between low HbA1c levels and mortality among the US general population. METHODS: This study includes a nationally representative sample of 39 453 US adults from the National Health and Nutrition Examination Surveys 1999-2014, linked to mortality data through 2015. We employed the parametric g-formula with pooled logistic regression models and the ensemble machine learning algorithms to estimate the time-varying risk of all-cause and cardiovascular mortality by HbA1c categories (low, 4.0 to <5.0%; mid-level, 5.0 to <5.7%; prediabetes, 5.7 to <6.5%; and diabetes, ≥6.5% or taking antidiabetic medication), adjusting for 72 potential confounders including demographic characteristics, lifestyle, biomarkers, comorbidities and medications. RESULTS: Over a median follow-up of 7.5 years, 5118 (13%) all-cause deaths, and 1116 (3%) cardiovascular deaths were observed. Logistic regression models and machine learning algorithms showed nearly identical predictive performance of death and risk estimates. Compared with mid-level HbA1c, low HbA1c was associated with a 30% (95% CI, 16 to 48) and a 12% (95% CI, 3 to 22) increased risk of all-cause mortality at 5 years and 10 years of follow-up, respectively. We found no evidence that low HbA1c levels were associated with cardiovascular mortality risk. The diabetes group, but not the prediabetes group, also showed an increased risk of all-cause mortality. CONCLUSIONS: Using the US national database and adjusting for an extensive set of potential confounders with flexible modelling, we found that adults with low HbA1c were at increased risk of all-cause mortality. Further evaluation and careful monitoring of low HbA1c levels need to be considered.

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We developed a custom-designed liposome carrier for codelivery of a potent immunogenic cell death (ICD) stimulus plus an inhibitor of the indoleamine 2,3-dioxygenase (IDO-1) pathway to establish a chemo-immunotherapy approach for solid tumors in syngeneic mice. The carrier was constructed by remote import of the anthraquinone chemotherapeutic agent, mitoxantrone (MTO), into the liposomes, which were further endowed with a cholesterol-conjugated indoximod (IND) prodrug in the lipid bilayer. For proof-of-principle testing, we used IV injection of the MTO/IND liposome in a CT26 colon cancer model to demonstrate the generation of a robust immune response, characterized by the appearance of ICD markers (CRT and HMGB-1) as well as evidence of cytotoxic cancer cell death, mediated by perforin and granzyme B. Noteworthy, the cytotoxic effects involved natural killer (NK) cell, which suggests a different type of ICD response. The immunotherapy response was significantly augmented by codelivery of the IND prodrug, which induced additional CRT expression, reduced number of Foxp3+ Treg, and increased perforin release, in addition to extending animal survival beyond the effect of an MTO-only liposome. The outcome reflects the improved pharmacokinetics of MTO delivery to the cancer site by the carrier. In light of the success in the CT26 model, we also assessed the platform efficacy in further breast cancer (EMT6 and 4T1) and renal cancer (RENCA) models, which overexpress IDO-1. Encapsulated MTO delivery was highly effective for inducing chemo-immunotherapy responses, with NK participation, in all tumor models. Moreover, the growth inhibitory effect of MTO was enhanced by IND codelivery in EMT6 and 4T1 tumors. All considered, our data support the use of encapsulated MTO delivery for chemo-immunotherapy, with the possibility to boost the immune response by codelivery of an IDO-1 pathway inhibitor.

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PURPOSE: In a single-institution phase II study, we evaluated the safety of a 5-day dose-equivalent neoadjuvant radiotherapy (RT) regimen for high-risk primary soft tissue sarcoma. PATIENTS AND METHODS: Patients received neoadjuvant RT alone (30 Gy in five fractions) to the primary tumor with standard margins. The primary endpoint was grade ≥2 late-radiation toxicity. Major wound complications, local recurrences, and distant metastases were also examined. In exploratory analysis, we evaluated germline biomarkers for wound toxicity and the effects of the study on treatment utilization. RESULTS: Over 2 years, 52 patients were enrolled with median follow-up of 29 months. Seven of 44 evaluable patients (16%) developed grade ≥2 late toxicity. Major wound complications occurred in 16 of 50 patients (32%); a signature defined by 19 germline SNPs in miRNA-binding sites of immune and DNA damage response genes, in addition to lower extremity tumor location, demonstrated strong predictive performance for major wound complications. Compared with the preceding 2-year period, the number of patients treated with neoadjuvant RT alone at our institution increased 3-fold, with a concomitant increase in the catchment area. CONCLUSIONS: A shorter 5-day neoadjuvant RT regimen results in favorable rates of wound complications and grade ≥2 toxicity after 2-year follow-up. Five-day RT significantly increased utilization of neoadjuvant RT at our high-volume sarcoma center. With further validation, a putative germline biomarker for wound complications may guide safer RT utilization.

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