shinyOPTIK, a User-Friendly R Shiny Application for Visualizing Cancer Risk Factors and Mortality Across the University of Kansas Cancer Center Catchment Area

PURPOSE The University of Kansas Cancer Center (KU Cancer Center) recently developed a data warehouse to Organize and Prioritize Trends to Inform KU Cancer Center (OPTIK). The OPTIK database aggregates and standardizes data collected across the bistate catchment area served by the KU Cancer Center. To improve the usability of the OPTIK database, we developed shinyOPTIK, a user-friendly, interactive web application for visualizing cancer risk factor and mortality rate data across the KU Cancer Center Catchment area. METHODS Data in the OPTIK database were first consolidated at the county level across the KU Cancer Center catchment area. Next, the shinyOPTIK development team met with the KU Cancer Center leadership to discuss the needs and priorities of the shinyOPTIK web application. shinyOPTIK was developed under the R Shiny framework and consists of a user interface (ui.R) and a web server (server.R). At present, s hinyOPTIK can be used to generate county-level geographical heatmaps; bar plots of demographic, screening, and risk factors; and line plots to visualize temporal trends at different Rural-Urban Continuum Codes (RUCCs), rural-urban status, metropolitan, or county levels across the KU Cancer Center catchment area. RESULTS Two examples, adult obesity prevalence and lung cancer mortality, are presented to illustrate how researchers can use shinyOPTIK. Each example is accompanied by post hoc visualizations to help explain key observations in terms of rural-urban disparities. CONCLUSION Although shinyOPTIK was developed to improve understanding of spatial and temporal trends across the population served by the KU Cancer Center, our hope is that the description of the steps involved in the creation of this tool along with open-source code for our application provided herein will serve as a guide for other research centers in the development of similar tools.

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Accrual Prediction Program: A web-based clinical trials tool for monitoring and predicting accrual for early-phase cancer studies

Clinical Trials ◽

10.1177/1740774519871474 ◽

2019 ◽

Vol 16 (6) ◽

pp. 657-664 ◽

Cited By ~ 2

Author(s):

Junhao Liu ◽

Jo A Wick ◽

Dinesh Pal Mudaranthakam ◽

Yu Jiang ◽

Matthew S Mayo ◽

...

Keyword(s):

Clinical Trials ◽

Real Time ◽

Web Application ◽

Time Trial ◽

Cancer Center ◽

University Of Kansas ◽

Web Based ◽

Institutional Level ◽

Prediction Program ◽

The University

Background Monitoring subject recruitment is key to the success of a clinical trial. Accordingly, researchers have developed accrual-monitoring tools to support the design and conduct of trials. At an institutional level, delays in identifying studies with high risk of accrual failure can lead to inefficient and costly trials with little chances of meeting study objectives. Comprehensive accrual monitoring is necessary to the success of the research enterprise. Methods This article describes the design and implementation of the University of Kansas Cancer Center Accrual Prediction Program, a web-based platform was developed to support comprehensive accrual monitoring and prediction for all active clinical trials. The Accrual Prediction Program provides information on accrual, including the predicted completion date, predicted number of accrued subjects during the pre-specified accrual period, and the probability of achieving accrual targets. It relies on a Bayesian accrual prediction model to combine protocol information with real-time trial enrollment data and disseminates results via web application. Results First released in 2016, the Accrual Prediction Program summarizes enrollment information for active studies categorized by various trial attributes. The web application supports real-time evidence-based decision making for strategic resource allocation and study management of over 120 ongoing clinical trials at the University of Kansas Cancer Center. Conclusion The Accrual Prediction Program makes accessing comprehensive accrual information manageable at an institutional level. Cancer centers or even entire institutions can reproduce the Accrual Prediction Program to achieve real-time comprehensive monitoring and prediction of subject accrual to aid investigators and administrators in the design, conduct, and management of clinical trials.

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Recurrence of venous thromboembolism among adults acute leukemia patients treated at the University of Texas MD Anderson Cancer Center: Incidence and risk factors

Thrombosis Research ◽

10.1016/j.thromres.2017.05.019 ◽

2017 ◽

Vol 156 ◽

pp. 14-19 ◽

Cited By ~ 4

Author(s):

Nhiem V. Luong ◽

Michael H. Kroll ◽

Khanh Vu

Keyword(s):

Risk Factors ◽

Venous Thromboembolism ◽

Acute Leukemia ◽

Cancer Center ◽

University Of Texas ◽

Md Anderson ◽

The University

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Mining human cancer datasets for kallikrein expression in cancer: the ‘KLK-CANMAP’ Shiny web tool

Biological Chemistry ◽

10.1515/hsz-2017-0322 ◽

2018 ◽

Vol 399 (9) ◽

pp. 983-995

Author(s):

Chenwei Wang ◽

Leire Moya ◽

Judith A. Clements ◽

Colleen C. Nelson ◽

Jyotsna Batra

Keyword(s):

Web Application ◽

Cancer Survival ◽

Human Cancer ◽

Web Tool ◽

Significant Differential Expression ◽

Box Plots ◽

Kaplan Meier ◽

R Shiny ◽

Serine Protease Family ◽

User Friendly

AbstractThe dysregulation of the serine-protease family kallikreins (KLKs), comprising 15 genes, has been reportedly associated with cancer. Their expression in several tissues and physiological fluids makes them potential candidates as biomarkers and therapeutic targets. There are several databases available to mine gene expression in cancer, which often include clinical and pathological data. However, these platforms present some limitations when comparing a specific set of genes and can generate considerable unwanted data. Here, several datasets that showed significant differential expression (p<0.01) in cancer vs. normal (n=118), metastasis vs. primary (n=15) and association with cancer survival (n=21) have been compiled in a user-friendly format from two open and/or publicly available databases Oncomine and OncoLnc for the 15 KLKs. The data have been included in a free web application tool: the KLK-CANMAP https://cancerbioinformatics.shinyapps.io/klk-canmap/. This tool integrates, analyses and visualises data and it was developed with the R Shiny framework. Using KLK-CANMAP box-plots, heatmaps and Kaplan-Meier graphs can be generated for the KLKs of interest. We believe this new cancer KLK focused web tool will benefit the KLK community by narrowing the data visualisation to only the genes of interest.

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MicroScope: ChIP-seq and RNA-seq software analysis suite for gene expression heatmaps

10.1101/034694 ◽

2015 ◽

Cited By ~ 1

Author(s):

Bohdan B. Khomtchouk ◽

James R. Hennessy ◽

Claes Wahlestedt

Keyword(s):

Web Application ◽

Differential Expression Analysis ◽

Dynamic Network ◽

Principal Component ◽

Rna Seq ◽

Software Suite ◽

Software Analysis ◽

Link Type ◽

R Shiny ◽

User Friendly

AbstractWe propose a user-friendly ChIP-seq and RNA-seq software suite for the interactive visualization and analysis of genomic data, including integrated features to support differential expression analysis, interactive heatmap production, principal component analysis, gene ontology analysis, and dynamic network analysis.MicroScope is hosted online as an R Shiny web application based on the D3 JavaScript library: http://microscopebioinformatics.org/. The methods are implemented in R, and are available as part of the MicroScope project at: https://github.com/Bohdan-Khomtchouk/Microscope.

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CONSENSUS: A Shiny application of Dementia evaluation and reporting for the KU ADC Longitudinal Clinical Cohort Database.

10.21203/rs.3.rs-69396/v1 ◽

2020 ◽

Author(s):

Palash Sharma ◽

Robert N. Montgomery ◽

Rasinio S. Graves ◽

Kayla Meyer ◽

Suzanne L Hunt ◽

...

Keyword(s):

Health Care Providers ◽

Care Providers ◽

University Of Kansas ◽

Participant Recruitment ◽

High Data ◽

Large Databases ◽

Visualization Systems ◽

The University ◽

User Friendly ◽

Shiny Application

Abstract Background: The University of Kansas Alzheimer’s Disease Center (KU ADC) maintains several large databases to track participant recruitment, enrollment, and capture various research related activities. It is challenging to manage and coordinate all the research related activities. One of the crucial activities involves capturing the data and maintaining high data quality, which ensures data reusability and reproducibility.Methods: To effectively manage the cohort, the KU ADC utilizes a combination of open source Electronic Data Capture (EDC) (i.e. REDCap), along with other homegrown data management and visualization systems developed using R-studio and Shiny.Results: In this manuscript, we describe the method and utility of the user-friendly dashboard that was developed for the rapid reporting of dementia evaluations along with data visualization, which allows clinical researchers to summarize recruitment metrics, automatically generate letters to both participants and health care providers, and depict other key metrics, which ultimately help optimize workflows. Conclusions: We believe this general framework would be beneficial to any institution for capturing and maintaining similar longitudinal databases for reporting and summarizing key metrics pertaining to their research.

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A Curated Cancer Clinical Outcomes Database (C3OD) for accelerating patient recruitment in cancer clinical trials

JAMIA Open ◽

10.1093/jamiaopen/ooy023 ◽

2018 ◽

Vol 1 (2) ◽

pp. 166-171 ◽

Cited By ~ 3

Author(s):

Dinesh Pal Mudaranthakam ◽

Jeffrey Thompson ◽

Jinxiang Hu ◽

Dong Pei ◽

Shanthan Reddy Chintala ◽

...

Keyword(s):

Clinical Trials ◽

Clinical Outcomes ◽

Cancer Center ◽

Patient Recruitment ◽

Cancer Clinical Trials ◽

University Of Kansas ◽

Tumor Registry ◽

Data Registry ◽

Study Completion ◽

The University

Abstract Data used to determine patient eligibility for cancer clinical trials often come from disparate sources that are typically maintained by different groups within an institution, use differing technologies, and are stored in different formats. Collecting data and resolving inconsistencies across sources increase the time it takes to screen eligible patients, potentially delaying study completion. To address these challenges, the Biostatistics and Informatics Shared Resource at The University of Kansas Cancer Center developed the Curated Cancer Clinical Outcomes Database (C3OD). C3OD merges data from the electronic medical record, tumor registry, bio-specimen and data registry, and allows querying through a single unified platform. By centralizing access and maintaining appropriate controls, C3OD allows researchers to more rapidly obtain detailed information about each patient in order to accelerate eligibility screening. This case report describes the design of this informatics platform as well as initial assessments of its reliability and usability.

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Publications search optimization: Comparison of a homegrown—API approach versus manual publication searches at an NCI designated cancer center

Health Informatics Journal ◽

10.1177/1460458220966816 ◽

2020 ◽

Vol 26 (4) ◽

pp. 3066-3071

Author(s):

Colin Cernik ◽

John Fife ◽

Jeffrey Thompson ◽

Lisa Harlan-Williams ◽

Dinesh Pal Mudaranthakam

Keyword(s):

Cost Savings ◽

Cost Effective ◽

Relevant Information ◽

Automated System ◽

Cancer Center ◽

University Of Kansas ◽

Considerable Effort ◽

Search Optimization ◽

Effective Manner ◽

The University

One measure of research productivity within the University of Kansas Cancer Center (KU Cancer Center) is peer-reviewed publications. Considerable effort goes into searching, capturing, reviewing, storing, and reporting cancer-relevant publications. Traditionally, the method of gathering relevant information to the publications is done manually. This manuscript describes the efforts to transition KU Cancer Center’s publication gathering process from a heavily manual to a more automated and efficient process. To achieve this transition in the most customized and cost-effective manner, a homegrown, automated system was developed using open source API among other software. When comparing the automated and the manual processes over several years of data, publication search and retrieval time dropped from an average of 59 h to 35 min, which would amount to a cost savings of several thousand dollars per year. The development and adoption of an automated publications search process can offer research centers great potential for less-error prone results with a savings in time and cost.

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