RESUMO
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. However, its capability shines when coupled with cutting-edge machine learning models such as random survival forests and deep neural networks. Availability and implementation: We provide a Shiny app at https://biostatistics.mdanderson.org/shinyapps/survivalContour/ and an R package available at https://github.com/YushuShi/survivalContour as implementations of this tool.
RESUMO
The research uses aerial imagery to pinpoint structures that could block migratory pronghorn and other wildlife.
Assuntos
Migração Animal , Animais Selvagens , Inteligência Artificial , Conservação dos Recursos Naturais , Ruminantes , AnimaisRESUMO
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 Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was <200 cGy for 95% of OARs in terms of Dmax and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality.
RESUMO
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. To validate our scoring method, 6 physicians were recruited to assess 60 plans, each using a scoring table consisting of a 5-point Likert scale (with scores ≥3 considered passing). Spearman correlation analysis was conducted to assess the association between increasing treatment plan percentile rank and physician rating, with Likert scores of 1 and 2 representing clinically unacceptable plans, scores of 3 and 4 representing plans needing minor edits, and a score of 5 representing clinically acceptable plans. Receiver operating characteristic curve analysis was used to assess the scoring system's ability to quantify plan quality. Results: Of the 60 plans scored by the physicians, 8 were deemed as clinically acceptable; these plans had an 89.0th ± 14.5 percentile value using our scoring system. The plans needing minor edits or deemed unacceptable had more variation, with scores falling in the 62.6nd ± 25.1 percentile and 35.6th ± 25.7 percentile, respectively. The estimated Spearman correlation coefficient between the physician score and treatment plan percentile was 0.53 (P < .001), indicating a moderate but statistically significant correlation. Receiver operating characteristic curve analysis demonstrated discernment between acceptable and unacceptable plan quality, with an area under the curve of 0.76. Conclusions: Our scoring system correlates with physician ratings while providing intuitive visual feedback for identifying good treatment plan quality, thereby indicating its utility in the quality assurance process.
RESUMO
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. METHODS: Input CT images were processed by a vessel enhancing filter to determine candidate PV segmentations. Multiple machine learning (ML) classifiers were evaluated for classifying a segmentation as corresponding to the PV based on segmentation shape, location, and intensity features. A public data set of 82 contrast-enhanced abdomen CT examinations was used to train the method. An optimal ML classifier was selected by training and tuning on 66 out of the 82 exams (80% training split) in the public data set. The method was evaluated in terms of segmentation classification accuracy and PV attenuation measurement accuracy, compared to manually determined ground truth, on a test set of the remaining 16 exams (20% test split) held out from public data set. The method was further evaluated on a separate, independently collected test set of 21 examinations. RESULTS: The best classifier was found to be a random forest, with a precision of 0.892 in the held-out test set to correctly identify the PV from among the input candidate segmentations. The mean absolute error of the measured PV attenuation relative to ground truth manual measurement was 13.4 HU. On the independent test set, the overall precision decreased to 0.684. However, the PV attenuation measurement remained relatively accurate with a mean absolute error of 15.2 HU. CONCLUSIONS: The method was shown to accurately measure PV attenuation over a large range of attenuation values, and was validated in an independently collected dataset. The method did not require time-consuming manual contouring to supervise training. The method may be applied to systematic quality control of contrast-enhanced CT examinations.
RESUMO
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. The application and development of analytical methods in this area require careful consideration of the unique aspects of microbiome profiles. We begin this review with a brief overview of microbiome data collection and processing and describe the resulting data structure. We then provide an overview of statistical methods for key tasks in microbiome data analysis, including data visualization, comparison of microbial abundance across groups, regression modeling, and network inference. We conclude with a discussion and highlight interesting future directions.
RESUMO
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. For posterior inference, we achieve computational scalability by implementing a variational Bayes scheme. Our approach enables simultaneous estimation of group-specific networks and selection of relevant covariate effects. We show improved performance over competing two-stage approaches on simulated data. We apply our method on resting-state functional magnetic resonance imaging data from children with a history of traumatic brain injury (TBI) and healthy controls to estimate the effects of age and sex on the group-level connectivities. Our results highlight differences in the distribution of parent nodes. They also suggest alteration in the relation of age, with peak edge strength in children with TBI, and differences in effective connectivity strength between males and females.
Assuntos
Teorema de Bayes , Lesões Encefálicas Traumáticas , Conectoma , Imageamento por Ressonância Magnética , Humanos , Lesões Encefálicas Traumáticas/diagnóstico por imagem , Lesões Encefálicas Traumáticas/fisiopatologia , Feminino , Masculino , Criança , Adolescente , Conectoma/métodos , Encéfalo/diagnóstico por imagem , Encéfalo/fisiopatologia , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/fisiopatologia , Modelos NeurológicosRESUMO
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. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns. RESULTS: We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the taxonomic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances. AVAILABILITY AND IMPLEMENTATION: The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package.
Assuntos
Microbiota , Humanos , Software , Metabolômica/métodos , Neoplasias Colorretais/microbiologia , Neoplasias Colorretais/metabolismo , Microbioma Gastrointestinal , AlgoritmosRESUMO
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)]. Analysis of 14 accrued patients demonstrated that treatment with SER-401 + nivolumab was safe, with an overall response rate of 25% in the SER-401 arm and 67% in the placebo arm (though the study was underpowered related to poor accrual during the COVID-19 pandemic). Translational analyses demonstrated that vancomycin preconditioning was associated with the disruption of the gut microbiota and impaired immunity, with incomplete recovery at ICB administration (particularly in patients with high baseline Ruminococcaceae). These results have important implications for future microbiome modulation trials. Significance: This first-of-its-kind, placebo-controlled, randomized biomarker-driven microbiome modulation trial demonstrated that vancomycin + SER-401 and anti-PD-1 are safe in melanoma patients. Although limited by poor accrual during the pandemic, important insights were gained via translational analyses, suggesting that antibiotic preconditioning and interventional drug dosing regimens should be carefully considered when designing such trials.
Assuntos
Antibacterianos , Microbioma Gastrointestinal , Melanoma , Humanos , Melanoma/tratamento farmacológico , Microbioma Gastrointestinal/efeitos dos fármacos , Antibacterianos/uso terapêutico , Antibacterianos/farmacologia , Feminino , Masculino , Pessoa de Meia-Idade , Idoso , Inibidores de Checkpoint Imunológico/uso terapêutico , Nivolumabe/uso terapêutico , Nivolumabe/administração & dosagem , Biomarcadores Tumorais , Vancomicina/uso terapêutico , Adulto , COVID-19/imunologia , Neoplasias Cutâneas/tratamento farmacológico , Neoplasias Cutâneas/imunologiaRESUMO
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. The materials were computed tomography (CT)-scanned to obtain Hounsfield unit (HU) values, then irradiated in a monoenergetic carbon ion beam to determine relative linear stopping power (RLSP). The corresponding HU and RLSP for each phantom material were compared to clinical carbon ion calibration curves. For absorbed dose comparison, ion chamber measurements were made in the center of a carbon ion spread-out Bragg peak (SOBP) in water and in the phantom material, evaluating whether the material perturbed the absorbed dose measurement beyond what was predicted by the HU-RLSP relationship. RESULTS: Polyethylene, solid water (Gammex and Sun Nuclear), Blue Water (Standard Imaging), and Techtron HPV had measured RLSP values that agreed within ±4.2% of RLSP values predicted by the clinical calibration curve. Measured RLSP for acrylic was 7.2% different from predicted. The agreement for balsa wood and cork varied between samples. Ion chamber measurements in the phantom materials were within 0.1% of ion chamber measurements in water for most materials (solid water, Blue Water, polyethylene, and acrylic), and within 1.9% for the rest of the materials (balsa wood, cork, and Techtron HPV). CONCLUSIONS: Several phantom materials (Blue Water, polyethylene, solid water [Gammex and Sun Nuclear], and Techtron HPV) are suitable for heterogeneous phantom measurements for carbon ion therapy. Low density materials should be carefully characterized due to inconsistencies between samples.
Assuntos
Radioterapia com Íons Pesados , Imagens de Fantasmas , Radioterapia com Íons Pesados/instrumentação , Calibragem , Tomografia Computadorizada por Raios X , HumanosRESUMO
Objective. 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.Approach. The absorbed dose correction factors for OSLD (fading, linearity, beam quality, angularity, and depletion), as defined by AAPM TG 191, were characterized for carbon beams. For the various correction factors, the effect of linear energy transfer (LET) was examined by characterizing in both a low and high LET setting.Main results. Fading was not statistically different between reference photons and carbon, nor between low and high LET beams; thus, the standard IROC-defined exponential function could be used to characterize fading. Dose linearity was characterized with a linear fit; while low and high LET carbon linearity was different, these differences were small and could be rolled into the uncertainty budget if using a single linearity correction. A linear fit between beam quality and dose-averaged LET was determined. The OSLD response at various angles of incidence was not statistically different, thus a correction factor need not be applied. There was a difference in depletion between low and high LET irradiations in a primary carbon beam, but this difference was small over the standard five readings. The largest uncertainty associated with the use of OSLDs in carbon was because of thekQcorrection factor, with an uncertainty of 6.0%. The overall uncertainty budget was 6.3% for standard irradiation conditions.Significance. OSLD nanoDot response was characterized in a therapeutic carbon beam. The uncertainty was larger than for traditional photon applications. These findings enable the use of OSLDs for carbon absorbed dose measurements, but with less accuracy than conventional OSLD audit programs.
Assuntos
Carbono , Carbono/química , Carbono/uso terapêutico , Radiometria/métodos , Transferência Linear de Energia , Incerteza , Dosimetria por Luminescência Estimulada Opticamente/métodos , Dosagem Radioterapêutica , HumanosRESUMO
Purpose: Immune checkpoint inhibitor-related pneumonitis (ICI-P) is a condition associated with high mortality, necessitating prompt recognition and treatment initiation. This study aimed to assess the impact of implementing a clinical care pathway algorithm on reducing the time to treatment for ICI-P. Methods: Patients with lung cancer and suspected ICI-P were enrolled, and a multi-modal intervention promoting algorithm use was implemented in two phases. Pre- and post-intervention analyses were conducted to evaluate the primary outcome of time from ICI-P diagnosis to treatment initiation. Results: Of the 82 patients admitted with suspected ICI-P, 73.17% were confirmed to have ICI-P, predominantly associated with non-small cell lung cancer (91.67%) and stage IV disease (95%). Pembrolizumab was the most commonly used immune checkpoint inhibitor (55%). The mean times to treatment were 2.37 days in the pre-intervention phase and, 3.07 days (p=0.46), and 1.27 days (p=0.40) in the post-intervention phases 1 and 2, respectively. Utilization of the immunotoxicity order set significantly increased from 0% to 27.27% (p = 0.04) after phase 2. While there were no significant changes in ICU admissions or inpatient mortality, outpatient pulmonology follow-ups increased statistically significantly, demonstrating enhanced continuity of care. The overall mortality for patients with ICI-P was 22%, underscoring the urgency of optimizing management strategies. Notably, all patients discharged on high-dose corticosteroids received appropriate gastrointestinal prophylaxis and prophylaxis against Pneumocystis jirovecii pneumonia infections at the end of phase 2. Conclusion: Implementing a clinical care pathway algorithm for ICI-P management standardizes care practices and enhances patient outcomes, underscoring the importance of structured approaches.
RESUMO
PURPOSE: Increased automation has been identified as one approach to improving global cancer care. The Radiation Planning Assistant (RPA) is a web-based tool offering automated radiotherapy (RT) contouring and planning to low-resource clinics. In this study, the RPA workflow and clinical acceptability were assessed by physicians around the world. METHODS: The RPA output for 75 cases was reviewed by at least three physicians; 31 radiation oncologists at 16 institutions in six countries on five continents reviewed RPA contours and plans for clinical acceptability using a 5-point Likert scale. RESULTS: For cervical cancer, RPA plans using bony landmarks were scored as usable as-is in 81% (with minor edits 93%); using soft tissue contours, plans were scored as usable as-is in 79% (with minor edits 96%). For postmastectomy breast cancer, RPA plans were scored as usable as-is in 44% (with minor edits 91%). For whole-brain treatment, RPA plans were scored as usable as-is in 67% (with minor edits 99%). For head/neck cancer, the normal tissue autocontours were acceptable as-is in 89% (with minor edits 97%). The clinical target volumes (CTVs) were acceptable as-is in 40% (with minor edits 93%). The volumetric-modulated arc therapy (VMAT) plans were acceptable as-is in 87% (with minor edits 96%). For cervical cancer, the normal tissue autocontours were acceptable as-is in 92% (with minor edits 99%). The CTVs for cervical cancer were scored as acceptable as-is in 83% (with minor edits 92%). The VMAT plans for cervical cancer were acceptable as-is in 99% (with minor edits 100%). CONCLUSION: The RPA, a web-based tool designed to improve access to high-quality RT in low-resource settings, has high rates of clinical acceptability by practicing clinicians around the world. It has significant potential for successful implementation in low-resource clinics.
Assuntos
Neoplasias da Mama , Neoplasias do Colo do Útero , Feminino , Humanos , Neoplasias da Mama/cirurgia , Inteligência Artificial , Neoplasias do Colo do Útero/radioterapia , Planejamento da Radioterapia Assistida por Computador , MastectomiaRESUMO
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 97.5 percentile levels. These modifications were evaluated on 25 patient cases, including prostate, non-small cell lung, H&N, brain, and mesothelioma, generating 1,000 plan perturbations. Differences in the mean planned dose to clinical target volumes (CTV) and organs at risk (OAR) were evaluated with respect to the planned dose using the reference (50th-percentile) parameter values. Correlation between CTV dose differences, and 18 different complexity metrics were evaluated using linear regression; R-squared values were used to determine the best metric. RESULTS: Perturbations to MLC offset and transmission parameters demonstrated the greatest changes in dose: up to 5.7% in CTVs and 16.7% for OARs. More complex clinical plans showed greater dose perturbation with atypical beam model parameters. The mean MLC Gap and Tongue & Groove index (TGi) complexity metrics best described the impact of TPS beam modeling variations on clinical dose delivery across all anatomical sites; similar, though not identical, trends between complexity and dose perturbation were observed among all sites. CONCLUSION: Extreme values for MLC offset and MLC transmission beam modeling parameters were found to most substantially impact the dose distribution of clinical plans and careful attention should be given to these beam modeling parameters. The mean MLC Gap and TGi complexity metrics were best suited to identifying clinical plans most sensitive to beam modeling errors; this could help provide focus for clinical QA in identifying unacceptable plans.
Assuntos
Neoplasias , Órgãos em Risco , Aceleradores de Partículas , Dosagem Radioterapêutica , Planejamento da Radioterapia Assistida por Computador , Radioterapia de Intensidade Modulada , Humanos , Planejamento da Radioterapia Assistida por Computador/métodos , Radioterapia de Intensidade Modulada/métodos , Órgãos em Risco/efeitos da radiação , Neoplasias/radioterapia , Aceleradores de Partículas/instrumentação , AlgoritmosRESUMO
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). When applied to a previously characterized TCR specific for the E7 antigen derived from human papillomavirus type 16 (HPV16), Tsyn-seq successfully enriched the correct cognate antigen from a cDNA library derived from an HPV16-positive cervical cancer cell line. Tsyn-seq provides a method for rapidly identifying antigens recognized by TCRs of interest from a tumor cDNA library. See related Spotlight by Makani and Joglekar, p. 515.
Assuntos
Sinapses Imunológicas , Receptores de Antígenos de Linfócitos T , Linfócitos T , Humanos , Células Apresentadoras de Antígenos/imunologia , Linhagem Celular Tumoral , Biblioteca Gênica , Sequenciamento de Nucleotídeos em Larga Escala , Papillomavirus Humano 16/imunologia , Papillomavirus Humano 16/genética , Sinapses Imunológicas/imunologia , Fatores de Transcrição NFATC/metabolismo , Fatores de Transcrição NFATC/imunologia , Proteínas E7 de Papillomavirus/imunologia , Proteínas E7 de Papillomavirus/genética , Receptores de Antígenos de Linfócitos T/imunologia , Receptores de Antígenos de Linfócitos T/genética , Linfócitos T/imunologiaRESUMO
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. We aimed to describe the outcomes and care patterns of grade ≥ 3 ICI-P in an onco-hospitalist service. PATIENTS AND METHODS: We included patients with lung cancer treated with ICI who were admitted to an oncology hospitalist service with a suspicion of ICI-P. We described the hospitalization characteristics, treatment patterns, discharge practices, and clinical outcomes of patients with confirmed ICI-P. The primary outcome was time to start treatment for ICI-P. RESULTS: Among 49 patients admitted with a suspicion of ICI-P, 31 patients were confirmed to have ICI-P and subsequently received ICI-P directed treatment. Pulmonology was consulted in 97% of patients. Median time to start treatment for ICI-P was 1 day (IQR 0-3.5 days). All 31 patients received corticosteroids. Inpatient mortality was 32%. Majority of patients discharged with steroids were prescribed prophylaxis for gastritis and opportunistic infections. Thirty-eight percent of patients were seen by pulmonology and 86% were seen by the oncology team post-discharge. CONCLUSION: Our study confirms prior findings of high mortality among patients with high-grade ICI-P. Early diagnosis and treatment are key to improving clinical outcomes. Understanding the care patterns and adherence to treatment guidelines of clinicians caring for this patient population may help identify ways to further standardize management practices and improve patient outcomes.
Assuntos
Médicos Hospitalares , Neoplasias Pulmonares , Pneumonia , Humanos , Alta do Paciente , Assistência ao Convalescente , Inibidores de Checkpoint Imunológico/efeitos adversos , Pneumonia/induzido quimicamente , Neoplasias Pulmonares/tratamento farmacológico , Estudos RetrospectivosRESUMO
BACKGROUND: This study investigated the influence of oral microbial features on the trajectory of oral mucositis (OM) in patients with squamous cell carcinoma of the head and neck. METHODS: OM severity was assessed and buccal swabs were collected at baseline, at the initiation of cancer treatment, weekly during cancer treatment, at the termination of cancer treatment, and after cancer treatment termination. The oral microbiome was characterized via the 16S ribosomal RNA V4 region with the Illumina platform. Latent class mixed-model analysis was used to group individuals with similar trajectories of OM severity. Locally estimated scatterplot smoothing was used to fit an average trend within each group and to assess the association between the longitudinal OM scores and longitudinal microbial abundances. RESULTS: Four latent groups (LGs) with differing patterns of OM severity were identified for 142 subjects. LG1 has an early onset of high OM scores. LGs 2 and 3 begin with relatively low OM scores until the eighth and 11th week, respectively. LG4 has generally flat OM scores. These LGs did not vary by treatment or clinical or demographic variables. Correlation analysis showed that the abundances of Bacteroidota, Proteobacteria, Bacteroidia, Gammaproteobacteria, Enterobacterales, Bacteroidales, Aerococcaceae, Prevotellaceae, Abiotrophia, and Prevotella_7 were positively correlated with OM severity across the four LGs. Negative correlation was observed with OM severity for a few microbial features: Abiotrophia and Aerococcaceae for LGs 2 and 3; Gammaproteobacteria and Proteobacteria for LGs 2, 3, and 4; and Enterobacterales for LGs 2 and 4. CONCLUSIONS: These findings suggest the potential to personalize treatment for OM. PLAIN LANGUAGE SUMMARY: Oral mucositis (OM) is a common and debilitating after effect for patients treated for squamous cell carcinoma of the head and neck. Trends in the abundance of specific microbial features may be associated with patterns of OM severity over time. Our findings suggest the potential to personalize treatment plans for OM via tailored microbiome interventions.
Assuntos
Carcinoma de Células Escamosas , Neoplasias de Cabeça e Pescoço , Microbiota , Estomatite , Humanos , Carcinoma de Células Escamosas de Cabeça e Pescoço , Carcinoma de Células Escamosas/tratamento farmacológicoRESUMO
PURPOSE: The aim of this study was to investigate the dosimetric and clinical effects of 4-dimensional computed tomography (4DCT)-based longitudinal dose accumulation in patients with locally advanced non-small cell lung cancer treated with standard-fractionated intensity-modulated radiation therapy (IMRT). METHODS AND MATERIALS: Sixty-seven patients were retrospectively selected from a randomized clinical trial. Their original IMRT plan, planning and verification 4DCTs, and â¼4-month posttreatment follow-up CTs were imported into a commercial treatment planning system. Two deformable image registration algorithms were implemented for dose accumulation, and their accuracies were assessed. The planned and accumulated doses computed using average-intensity images or phase images were compared. At the organ level, mean lung dose and normal-tissue complication probability (NTCP) for grade ≥2 radiation pneumonitis were compared. At the region level, mean dose in lung subsections and the volumetric overlap between isodose intervals were compared. At the voxel level, the accuracy in estimating the delivered dose was compared by evaluating the fit of a dose versus radiographic image density change (IDC) model. The dose-IDC model fit was also compared for subcohorts based on the magnitude of NTCP difference (|ΔNTCP|) between planned and accumulated doses. RESULTS: Deformable image registration accuracy was quantified, and the uncertainty was considered for the voxel-level analysis. Compared with planned doses, accumulated doses on average resulted in <1-Gy lung dose increase and <2% NTCP increase (up to 8.2 Gy and 18.8% for a patient, respectively). Volumetric overlap of isodose intervals between the planned and accumulated dose distributions ranged from 0.01 to 0.93. Voxel-level dose-IDC models demonstrated a fit improvement from planned dose to accumulated dose (pseudo-R2 increased 0.0023) and a further improvement for patients with ≥2% |ΔNTCP| versus for patients with <2% |ΔNTCP|. CONCLUSIONS: With a relatively large cohort, robust image registrations, multilevel metric comparisons, and radiographic image-based evidence, we demonstrated that dose accumulation more accurately represents the delivered dose and can be especially beneficial for patients with greater longitudinal response.
Assuntos
Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Radioterapia de Intensidade Modulada , Humanos , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Carcinoma Pulmonar de Células não Pequenas/radioterapia , Radioterapia de Intensidade Modulada/efeitos adversos , Radioterapia de Intensidade Modulada/métodos , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/radioterapia , Estudos Retrospectivos , Dosagem Radioterapêutica , Planejamento da Radioterapia Assistida por Computador/métodos , Tomografia Computadorizada Quadridimensional/métodosRESUMO
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. METHODS: To characterize CDI epidemiology, including risk factors and complications among pediatric oncology patients, we retrospectively reviewed charts of patients 1-18 years old treated at a designated cancer center during 2000-2017. We used fluorescence-based polymerase chain reaction to identify ribotypes causing disease at our institution. RESULTS: In 11,366 total patients, we identified 207 CDI cases during the study period. CDI prevalence in our pediatric oncology population was 18 cases per 1000 patients. CDI was highest among patients with acute myeloid leukemia, neuroblastoma, and desmoplastic small round cell tumor (105, 66 and 111 cases per 1000 patients, respectively; P < 0.01). Fever, leukocytosis, elevated creatinine and abdominal radiation and fluoroquinolone exposure concurrent with treatment of CDI were associated with complications. Patients with severe CDI experienced increased mortality. Ribotypes previously associated with severe infection were observed infrequently and were not associated with mortality. CONCLUSIONS: This is the largest study of CDI in pediatric oncology patients to date. The study identifies specific oncologic diagnoses with increased CDI risk and factors predictive of poor outcomes. As CDI treatment guidelines are developed for this population, these data will be useful for risk stratification of patients in need of early, aggressive treatment.
Assuntos
Clostridioides difficile , Infecções por Clostridium , Neoplasias , Humanos , Infecções por Clostridium/epidemiologia , Infecções por Clostridium/microbiologia , Criança , Fatores de Risco , Pré-Escolar , Adolescente , Estudos Retrospectivos , Feminino , Prevalência , Lactente , Masculino , Neoplasias/complicações , Ribotipagem , Antibacterianos/uso terapêuticoRESUMO
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. Interestingly, for addressing this gap, extracellular vesicles (EVs) and circulating tumor cells (CTCs) have emerged as potential sources of such biomarkers. In this study, we investigated EV-associated miRNAs and presence of autophagic CTCs in prospectively collected serial samples from 38 patients with stage III NSCLC undergoing cCRT. Our findings revealed that non-responders exhibited low levels of baseline EV miR-375, miR-200c, and miR-30c. In particular, EV miR-30c showed high predictive value with an area under the curve of 87.2%. Low EV miR-30c and the presence of autophagic-activated CTCs emerged as independent predictive biomarkers for shorter relapse-free survival and overall survival. Furthermore, in experimental models simulating the effects of chemo- and radiotherapy, the administration of miR-30c, either through direct transfection or encapsulation into human EVs, led to the inhibition of autophagy in these cells. This is the first report demonstrating that EV miR-30c inhibits tumor autophagy and its quantification, together with autophagic-activated CTCs, could be used as biomarkers for the stratification and monitoring of patients with NSCLC undergoing cCRT, and they may hold promising potential for guiding subsequent consolidation treatment with immunotherapy or other novel therapies based on autophagy inhibitors.