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Article ; Online: Predictive modeling using shape statistics for interpretable and robust quality assurance of automated contours in radiation treatment planning.

Wooten, Zachary T / Yu, Cenji / Court, Laurence E / Peterson, Christine B

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

2022 Volume 28, Page(s) 395–406

Abstract: Deep learning methods for image segmentation and contouring are gaining prominence as an automated approach for delineating anatomical structures in medical images during radiation treatment planning. These contours are used to guide radiotherapy ... ...

Abstract	Deep learning methods for image segmentation and contouring are gaining prominence as an automated approach for delineating anatomical structures in medical images during radiation treatment planning. These contours are used to guide radiotherapy treatment planning, so it is important that contouring errors are flagged before they are used for planning. This creates a need for effective quality assurance methods to enable the clinical use of automated contours in radiotherapy. We propose a novel method for contour quality assurance that requires only shape features, making it independent of the platform used to obtain the images. Our method uses a random forest classifier to identify low-quality contours. On a dataset of 312 kidney contours, our method achieved a cross-validated area under the curve of 0.937 in identifying unacceptable contours. We applied our method to an unlabeled validation dataset of 36 kidney contours. We flagged 6 contours which were then reviewed by a cervix contour specialist, who found that 4 of the 6 contours contained errors. We used Shapley values to characterize the specific shape features that contributed to each contour being flagged, providing a starting point for characterizing the source of the contouring error. These promising results suggest our method is feasible for quality assurance of automated radiotherapy contours.
MeSH term(s)	Female ; Humans ; Radiotherapy Planning, Computer-Assisted/methods ; Computational Biology
Language	English
Publishing date	2022-12-17
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
ISSN	2335-6936
ISSN (online)	2335-6936
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Bayesian feature selection for radiomics using reliability metrics.

Shoemaker, Katherine / Ger, Rachel / Court, Laurence E / Aerts, Hugo / Vannucci, Marina / Peterson, Christine B

Frontiers in genetics

2023 Volume 14, Page(s) 1112914

Abstract: Introduction: ...

Abstract	Introduction:
Language	English
Publishing date	2023-03-08
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2023.1112914
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Article: Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM.

Osborne, Nathan / Peterson, Christine B / Vannucci, Marina

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America

2021 Volume 31, Issue 1, Page(s) 163–175

Abstract: Network estimation and variable selection have been extensively studied in the statistical literature, but only recently have those two challenges been addressed simultaneously. In this article, we seek to develop a novel method to simultaneously ... ...

Abstract	Network estimation and variable selection have been extensively studied in the statistical literature, but only recently have those two challenges been addressed simultaneously. In this article, we seek to develop a novel method to simultaneously estimate network interactions and associations to relevant covariates for count data, and specifically for compositional data, which have a fixed sum constraint. We use a hierarchical Bayesian model with latent layers and employ spike-and-slab priors for both edge and covariate selection. For posterior inference, we develop a novel variational inference scheme with an expectation-maximization step, to enable efficient estimation. Through simulation studies, we demonstrate that the proposed model outperforms existing methods in its accuracy of network recovery. We show the practical utility of our model via an application to microbiome data. The human microbiome has been shown to contribute too many of the functions of the human body, and also to be linked with a number of diseases. In our application, we seek to better understand the interaction between microbes and relevant covariates, as well as the interaction of microbes with each other. We call our algorithm simultaneous inference for networks and covariates and provide a Python implementation, which is available online.
Language	English
Publishing date	2021-07-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	2014382-5
ISSN	1537-2715 ; 1061-8600
ISSN (online)	1537-2715
ISSN	1061-8600
DOI	10.1080/10618600.2021.1935971
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Article: Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer.

Shi, Yushu / Zhang, Liangliang / Do, Kim-Anh / Jenq, Robert / Peterson, Christine B

Journal of the Royal Statistical Society. Series C, Applied statistics

2023 Volume 72, Issue 1, Page(s) 20–36

Abstract: There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with similar microbiome ... ...

Abstract	There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with similar microbiome profiles. We propose a novel unsupervised clustering approach in the Bayesian framework that innovates over existing model-based clustering approaches, such as the Dirichlet multinomial mixture model, in three key respects: we incorporate feature selection, learn the appropriate number of clusters from the data, and integrate information on the tree structure relating the observed features. We compare the performance of our proposed method to existing methods on simulated data designed to mimic real microbiome data. We then illustrate results obtained for our motivating data set, a clinical study aimed at characterizing the tumor microbiome of pancreatic cancer patients.
Language	English
Publishing date	2023-02-13
Publishing country	England
Document type	Journal Article
ZDB-ID	1482300-7
ISSN	1467-9876 ; 0035-9254
ISSN (online)	1467-9876
ISSN	0035-9254
DOI	10.1093/jrsssc/qlac002
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Article ; Online: Bayesian hierarchical quantile regression with application to characterizing the immune architecture of lung cancer

Das, Priyam / Peterson, Christine B. / Ni, Yang / Reuben, Alexandre / Zhang, Jiexin / Zhang, Jianjun / Do, Kim‐Anh / Baladandayuthapani, Veerabhadran

Biometrics. 2023 Sept., v. 79, no. 3 p.2474-2488

2023

Abstract: The successful development and implementation of precision immuno‐oncology therapies requires a deeper understanding of the immune architecture at a patient level. T‐cell receptor (TCR) repertoire sequencing is a relatively new technology that enables ... ...

Abstract	The successful development and implementation of precision immuno‐oncology therapies requires a deeper understanding of the immune architecture at a patient level. T‐cell receptor (TCR) repertoire sequencing is a relatively new technology that enables monitoring of T‐cells, a subset of immune cells that play a central role in modulating immune response. These immunologic relationships are complex and are governed by various distributional aspects of an individual patient's tumor profile. We propose Bayesian QUANTIle regression for hierarchical COvariates (QUANTICO) that allows simultaneous modeling of hierarchical relationships between multilevel covariates, conducts explicit variable selection, estimates quantile and patient‐specific coefficient effects, to induce individualized inference. We show QUANTICO outperforms existing approaches in multiple simulation scenarios. We demonstrate the utility of QUANTICO to investigate the effect of TCR variables on immune response in a cohort of lung cancer patients. At population level, our analyses reveal the mechanistic role of T‐cell proportion on the immune cell abundance, with tumor mutation burden as an important factor modulating this relationship. At a patient level, we find several outlier patients based on their quantile‐specific coefficient functions, who have higher mutational rates and different smoking history.
Keywords	Bayesian theory ; T-lymphocytes ; immune response ; lung neoplasms ; mutation ; patients ; regression analysis ; technology
Language	English
Dates of publication	2023-09
Size	p. 2474-2488.
Publishing place	John Wiley & Sons, Ltd
Document type	Article ; Online
Note	JOURNAL ARTICLE
ZDB-ID	213543-7
ISSN	0099-4987 ; 0006-341X
ISSN	0099-4987 ; 0006-341X
DOI	10.1111/biom.13774
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Article ; Online: Performance determinants of unsupervised clustering methods for microbiome data.

Shi, Yushu / Zhang, Liangliang / Peterson, Christine B / Do, Kim-Anh / Jenq, Robert R

Microbiome

2022 Volume 10, Issue 1, Page(s) 25

Abstract: Background: In microbiome data analysis, unsupervised clustering is often used to identify naturally occurring clusters, which can then be assessed for associations with characteristics of interest. In this work, we systematically compared beta ... ...

Abstract	Background: In microbiome data analysis, unsupervised clustering is often used to identify naturally occurring clusters, which can then be assessed for associations with characteristics of interest. In this work, we systematically compared beta diversity and clustering methods commonly used in microbiome analyses. We applied these to four published datasets where highly distinct microbiome profiles could be seen between sample groups, as well a clinical dataset with less clear separation between groups. Results: Although no single method outperformed the others consistently, we did identify the key scenarios where certain methods can underperform. Specifically, the Bray Curtis (BC) metric resulted in poor clustering in a dataset where high-abundance OTUs were relatively rare. In contrast, the unweighted UniFrac (UU) metric clustered poorly on dataset with a high prevalence of low-abundance OTUs. To explore these hypotheses about BC and UU, we systematically modified the properties of the poorly performing datasets and found that this approach resulted in improved BC and UU performance. Based on these observations, we rationally combined BC and UU to generate a novel metric. We tested its performance while varying the relative contributions of each metric and also compared it with another combined metric, the generalized UniFrac distance. The proposed metric showed high performance across all datasets. Conclusions: Our systematic evaluation of clustering performance in these five datasets demonstrates that there is no existing clustering method that universally performs best across all datasets. We propose a combined metric of BC and UU that capitalizes on the complementary strengths of the two metrics. Video abstract.
MeSH term(s)	Cluster Analysis ; Microbiota/genetics
Language	English
Publishing date	2022-02-05
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Video-Audio Media
ZDB-ID	2697425-3
ISSN	2049-2618 ; 2049-2618
ISSN (online)	2049-2618
ISSN	2049-2618
DOI	10.1186/s40168-021-01199-3
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Article ; Online: Outcomes of breakthrough COVID-19 infections in patients with hematologic malignancies.

Chien, Kelly S / Peterson, Christine B / Young, Elliana / Chihara, Dai / Manasanch, Elizabet E / Ramdial, Jeremy L / Thompson, Philip A

Blood advances

2023 Volume 7, Issue 19, Page(s) 5691–5697

Abstract: Patients with hematologic malignancies have both an increased risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and higher morbidity/mortality. They have lower seroconversion rates after vaccination, potentially leading to ... ...

Abstract	Patients with hematologic malignancies have both an increased risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and higher morbidity/mortality. They have lower seroconversion rates after vaccination, potentially leading to inferior coronavirus disease 2019 (COVID-19) outcomes, despite vaccination. We consequently evaluated the clinical outcomes of COVID-19 infections in 243 vaccinated and 175 unvaccinated patients with hematologic malignancies. Hospitalization rates were lower in the vaccinated group when compared with the unvaccinated group (31.3% vs 52.6%). However, the rates of COVID-19-associated death were similar at 7.0% and 8.6% in vaccinated and unvaccinated patients, respectively. By univariate logistic regression, females, older patients, and individuals with higher modified Charlson Comorbidity Index scores were at a higher risk of death from COVID-19 infections. To account for the nonrandomized nature of COVID-19 vaccination status, a propensity score weighting approach was used. In the final propensity-weighted model, vaccination status was not significantly associated with the risk of death from COVID-19 infections but was associated with the risk of hospitalization. The predicted benefit of vaccination was an absolute decrease in the probability of death and hospitalization from COVID-19 infections by 2.3% and 22.9%, respectively. In conclusion, COVID-19 vaccination status in patients with hematologic malignancies was associated with a decreased risk of hospitalization but not associated with a decreased risk of death from COVID-19 infections in the pre-Omicron era. Protective strategies, in addition to immunization, are warranted in this vulnerable patient population.
MeSH term(s)	Female ; Humans ; COVID-19 ; SARS-CoV-2 ; COVID-19 Vaccines ; Hematologic Neoplasms/complications ; Hematologic Neoplasms/therapy
Chemical Substances	COVID-19 Vaccines
Language	English
Publishing date	2023-01-25
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2915908-8
ISSN	2473-9537 ; 2473-9529
ISSN (online)	2473-9537
ISSN	2473-9529
DOI	10.1182/bloodadvances.2022008827
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Article: TARO: tree-aggregated factor regression for microbiome data integration.

Mishra, Aditya K / Mahmud, Iqbal / Lorenzi, Philip L / Jenq, Robert R / Wargo, Jennifer A / Ajami, Nadim J / Peterson, Christine B

bioRxiv : the preprint server for biology

2023

Abstract: 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 ... ...

Abstract	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 phylogenetic 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 .
Language	English
Publishing date	2023-10-19
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.10.17.562792
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Article ; Online: Increased Prevalence of Clostridioides difficile Infection Among Pediatric Oncology Patients: Risk Factors for Infection and Complications.

Murphy, Brianna R / Dailey Garnes, Natalie J / Hwang, Hyunsoo / Peterson, Christine B / Garey, Kevin W / Okhuysen, Pablo

The Pediatric infectious disease journal

2023

Abstract: 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 ... ...

Abstract	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.
Language	English
Publishing date	2023-12-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	392481-6
ISSN	1532-0987 ; 0891-3668
ISSN (online)	1532-0987
ISSN	0891-3668
DOI	10.1097/INF.0000000000004178
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Article ; Online: Low pitch significantly reduces helical artifacts in abdominal CT.

Ahmad, Moiz / Sun, Peng / Peterson, Christine B / Anderson, Marcus R / Liu, Xinming / Morani, Ajaykumar C / Jensen, Corey T

European journal of radiology

2023 Volume 166, Page(s) 110977

Abstract: Purpose: High helical pitch scanning minimizes scan times in CT imaging, and thus also minimizes motion artifact and mis-synchronization with contrast bolus. However, high pitch produces helical artifacts that may adversely affect diagnostic image ... ...

Abstract	Purpose: High helical pitch scanning minimizes scan times in CT imaging, and thus also minimizes motion artifact and mis-synchronization with contrast bolus. However, high pitch produces helical artifacts that may adversely affect diagnostic image quality. This study aims to determine the severity and incidence of helical artifacts in abdominal CT imaging and their relation to the helical pitch scan parameter. Methods: To obtain a dataset with varying pitch values, we used CT exam data both internal and external to our center. A cohort of 59 consecutive adult patients receiving an abdomen CT examination at our center with an accompanying prior examination from an external center was selected for retrospective review. Two expert observers performed a blinded rating of helical artifact in each examination using a five-point Likert scale. The incidence of artifacts with respect to the helical pitch was assessed. A generalized linear mixed-effects regression (GLMER) model, with study arm (Internal or External to our center) and helical pitch as the fixed-effect predictor variables, was fit to the artifact ratings, and significance of the predictor variables was tested. Results: For a pitch of <0.75, the proportion of exams with mild or worse helical artifacts (Likert scores of 1-3) was <1%. The proportion increased to 16% for exams with pitch between 0.75 and 1.2, and further increased to 78% for exams with a pitch greater than 1.2. Pitch was significantly associated with helical artifact in the GLMER model (p = 2.8 × 10 Conclusion: The incidence and severity of helical artifact increased with helical pitch. This difference persisted even after accounting for the potential confounding factor of the center where the study was performed.
MeSH term(s)	Adult ; Humans ; Artifacts ; Tomography, X-Ray Computed/methods ; Motion ; Retrospective Studies ; Abdomen/diagnostic imaging ; Phantoms, Imaging
Language	English
Publishing date	2023-07-13
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	138815-0
ISSN	1872-7727 ; 0720-048X
ISSN (online)	1872-7727
ISSN	0720-048X
DOI	10.1016/j.ejrad.2023.110977
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