Characterization of LiF TLD-100 in carbon ion beams for remote audits.

Background: A passive dosimeter framework for the measurement of dose in carbon ion beams has yet to be characterized or implemented for regular use.

Purpose: This work determined the dose calculation correction factors for absorbed dose in thermoluminescent dosimeters (TLDs) in a therapeutic carbon ion beam. TLD could be a useful tool for remote audits, particularly in the context of clinical trials as new protocols are developed for carbon ion radiotherapy.

Plasma DNA Methylation-Based Biomarkers for MPNST Detection in Patients With Neurofibromatosis Type 1.

Malignant peripheral nerve sheath tumor (MPNST) development is characterized by an altered DNA methylation landscape, which presents a promising area for developing MPNST-specific biomarkers for screening patients with NF1. Genome-wide DNA methylation profiling of a cohort of 13 patients with MPNST (29 samples of tumor and adjacent neurofibroma tissues) and of NF1-MPNST cell lines was performed to identify and validate candidate MPNST-specific CpG sites (CpGs). A logistic regression prediction model was constructed to select MPNST-specific CpGs distinct from adjacent neurofibromas and normal tissues.

Constructing phylogenetic trees for microbiome data analysis: A mini-review.

As next-generation sequencing technologies advance rapidly and the cost of metagenomic sequencing continues to decrease, researchers now face an unprecedented volume of microbiome data. This surge has stimulated the development of scalable microbiome data analysis methods and necessitated the incorporation of phylogenetic information into microbiome analysis for improved accuracy. Tools for constructing phylogenetic trees from 16S rRNA sequencing data are well-established, as the highly conserved regions of the 16S gene are limited, simplifying the identification of marker genes.

Bayesian network-guided sparse regression with flexible varying effects.

In this paper, we propose Varying Effects Regression with Graph Estimation (VERGE), a novel Bayesian method for feature selection in regression. Our model has key aspects that allow it to leverage the complex structure of data sets arising from genomics or imaging studies. We distinguish between the predictors, which are the features utilized in the outcome prediction model, and the subject-level covariates, which modulate the effects of the predictors on the outcome.

Is the Imaging Radiation Oncology Core Head and Neck Credentialing Phantom an Effective Surrogate for Different Anatomic Sites?

Purpose: The Imaging Radiation Oncology Core (IROC) head and neck (H&N) phantom is used to credential institutions for intensity modulated radiation therapy delivery for all anatomic sites where delivery of modulated therapy is a primary challenge. This study evaluated how appropriate the use of this phantom is for varied clinical anatomy by evaluating how closely the IROC H&N phantom described clinical dose errors from beam modeling compared with various anatomic sites.

Methods And Materials: The multileaf collimator (MLC) offset, transmission, percent depth dose, and 7 additional beam modeling parameters for a Varian accelerator were modified in RayStation to match community data at the 2.

Bacteroides ovatus alleviates dysbiotic microbiota-induced graft-versus-host disease.

Acute lower gastrointestinal GVHD (aLGI-GVHD) is a serious complication of allogeneic hematopoietic stem cell transplantation. Although the intestinal microbiota is associated with the incidence of aLGI-GVHD, how the intestinal microbiota impacts treatment responses in aLGI-GVHD has not been thoroughly studied. In a cohort of patients with aLGI-GVHD (n = 37), we found that non-response to standard therapy with corticosteroids was associated with prior treatment with carbapenem antibiotics and a disrupted fecal microbiome characterized by reduced abundances of Bacteroides ovatus.

Analyzing the Relationship between Dose and Geometric Agreement Metrics for Auto-Contouring in Head and Neck Normal Tissues.

This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of D (D0.

survivalContour: visualizing predicted survival via colored contour plots.

Summary: Advances in survival analysis have facilitated unprecedented flexibility in data modeling, yet there remains a lack of tools for illustrating the influence of continuous covariates on predicted survival outcomes. We propose the utilization of a colored contour plot to depict the predicted survival probabilities over time. Our approach is capable of supporting conventional models, including the Cox and Fine-Gray models.

Automatic vessel attenuation measurement for quality control of contrast-enhanced CT: Validation on the portal vein.

Background: Adequate image enhancement of organs and blood vessels of interest is an important aspect of image quality in contrast-enhanced computed tomography (CT). There is a need for an objective method for evaluation of vessel contrast that can be automatically and systematically applied to large sets of CT exams.

Purpose: The purpose of this work was to develop a method to automatically segment and measure attenuation Hounsfield Unit (HU) in the portal vein (PV) in contrast-enhanced abdomen CT examinations.

A Visualization and Radiation Treatment Plan Quality Scoring Method for Triage in a Population-Based Context.

Purpose: Our purpose was to develop a clinically intuitive and easily understandable scoring method using statistical metrics to visually determine the quality of a radiation treatment plan.

Methods And Materials: Data from 111 patients with head and neck cancer were used to establish a percentile-based scoring system for treatment plan quality evaluation on both a plan-by-plan and objective-by-objective basis. The percentile scores for each clinical objective and the overall treatment plan score were then visualized using a daisy plot.

Analysis of Microbiome Data.

The microbiome represents a hidden world of tiny organisms populating not only our surroundings but also our own bodies. By enabling comprehensive profiling of these invisible creatures, modern genomic sequencing tools have given us an unprecedented ability to characterize these populations and uncover their outsize impact on our environment and health. Statistical analysis of microbiome data is critical to infer patterns from the observed abundances.

Bayesian varying-effects vector autoregressive models for inference of brain connectivity networks and covariate effects in pediatric traumatic brain injury.

In this article, we develop an analytical approach for estimating brain connectivity networks that accounts for subject heterogeneity. More specifically, we consider a novel extension of a multi-subject Bayesian vector autoregressive model that estimates group-specific directed brain connectivity networks and accounts for the effects of covariates on the network edges. We adopt a flexible approach, allowing for (possibly) nonlinear effects of the covariates on edge strength via a novel Bayesian nonparametric prior that employs a weighted mixture of Gaussian processes.

TARO: tree-aggregated factor regression for microbiome data integration.

Motivation: Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data.

OSLD nanoDot characterization for carbon radiotherapy dosimetry.

. This study characterized optically-stimulated luminescent dosimeter (OSLD) nanoDots for use in a therapeutic carbon beam using the Imaging and Radiation Oncology Core (IROC) framework for remote output verification..

Technical note: Radiological clinical equivalence for phantom materials in carbon ion therapy.

Purpose: As carbon ion radiotherapy increases in use, there are limited phantom materials for heterogeneous or anthropomorphic phantom measurements. This work characterized the radiological clinical equivalence of several phantom materials in a therapeutic carbon ion beam.

Methods: Eight materials were tested for radiological material-equivalence in a carbon ion beam.

Randomized Placebo-Controlled, Biomarker-Stratified Phase Ib Microbiome Modulation in Melanoma: Impact of Antibiotic Preconditioning on Microbiome and Immunity.

Gut-microbiota modulation shows promise in improving immune-checkpoint blockade (ICB) response; however, precision biomarker-driven, placebo-controlled trials are lacking. We performed a multicenter, randomized placebo-controlled, biomarker-stratified phase I trial in patients with ICB-naïve metastatic melanoma using SER-401, an orally delivered Firmicutesenriched spore formulation. Fecal microbiota signatures were characterized at baseline; patients were stratified by high versus low Ruminococcaceae abundance prior to randomization to the SER-401 arm (oral vancomycin-preconditioning/SER-401 alone/nivolumab + SER-401), versus the placebo arm [placebo antibiotic/placebo microbiome modulation (PMM)/nivolumab + PMM (NCT03817125)].

A radiotherapy community data-driven approach to determine which complexity metrics best predict the impact of atypical TPS beam modeling on clinical dose calculation accuracy.

Purpose: To quantify the impact of treatment planning system beam model parameters, based on the actual spread in radiotherapy community data, on clinical treatment plans and determine which complexity metrics best describe the impact beam modeling errors have on dose accuracy.

Methods: Ten beam modeling parameters for a Varian accelerator were modified in RayStation to match radiotherapy community data at the 2.5, 25, 50, 75, and 97.

Treatment patterns and outcomes of high-grade immune checkpoint inhibitor-related pneumonitis in an oncology hospitalist service.

Purpose: Immune checkpoint inhibitors (ICI) have become standard of care for some types of lung cancer. Along with expanding usage comes the emergence of immune-related adverse events (irAEs), including ICI-related pneumonitis (ICI-P). Treatment guidelines for managing irAEs have been developed; however, how clinicians manage irAEs in the real-world setting is less well known.

Tsyn-Seq: a T-cell Synapse-Based Antigen Identification Platform.

Tools for genome-wide rapid identification of peptide-major histocompatibility complex targets of T-cell receptors (TCR) are not yet universally available. We present a new antigen screening method, the T-synapse (Tsyn) reporter system, which includes antigen-presenting cells (APC) with a Fas-inducible NF-κB reporter and T cells with a nuclear factor of activated T cells (NFAT) reporter. To functionally screen for target antigens from a cDNA library, productively interacting T cell-APC aggregates were detected by dual-reporter activity and enriched by flow sorting followed by antigen identification quantified by deep sequencing (Tsyn-seq).

Increased Prevalence of Clostridioides difficile Infection Among Pediatric Oncology Patients: Risk Factors for Infection and Complications.

Background: Pediatric oncology patients, who are typically immunosuppressed, exposed to medications associated with increased Clostridioides difficile infection (CDI) risk and hospitalized, are expected to be at substantial risk for infection and complications. Although certain C. difficile ribotypes have been associated with more severe infection in adults, such an association has not been described in children.

Baseline extracellular vesicle miRNA-30c and autophagic CTCs predict chemoradiotherapy resistance and outcomes in patients with lung cancer.

Concurrent chemoradiotherapy (cCRT) is the mainstay of treatment for patients diagnosed with locally advanced non-small cell lung cancer (NSCLC). One significant challenge in the effectiveness of this therapy is the potential development of resistance mechanisms, where autophagy up-regulation has been proposed as a key contributing factor. However, there is a lack of reliable biomarkers to predict outcomes on these patients.

TARO: tree-aggregated factor regression for microbiome data integration.

Motivation: Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data.

Remotely Delivered Cancer Genetic Testing in the Making Genetic Testing Accessible (MAGENTA) Trial: A Randomized Clinical Trial.

Importance: Requiring personalized genetic counseling may introduce barriers to cancer risk assessment, but it is unknown whether omitting counseling could increase distress.

Objective: To assess whether omitting pretest and/or posttest genetic counseling would increase distress during remote testing.

Design, Setting, And Participants: Making Genetic Testing Accessible (MAGENTA) was a 4-arm, randomized noninferiority trial testing the effects of individualized pretest and/or posttest genetic counseling on participant distress 3 and 12 months posttest.

Deep learning-based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers.

Background: In recent years, deep-learning models have been used to predict entire three-dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated.

Purpose: To develop a deep-learning model to predict high-quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements.