Oncospace: All Patients on Trial for Analysis of Outcomes, Toxicities, and IMRT Plan Quality

McNutt, T.R.; Evans, K. T.; Wu, Bechien U.; Kahzdan, M.; Simari, Patricio; Sanguineti, Giuseppe; Joseph, Herman; Taylor, Russell H.; Wong, Johnny; DeWeese, T.L.

doi:10.1016/j.ijrobp.2010.07.1139

Cited by 7 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first metric used in the data driven models was total ROI volume. Using Microsoft Visual Studio as a platform, we used SQL direct queries to query patients from the Oncospace Prostate Database based on specific Region of Interest Volume. We then consolidated the patient lists, organizing by ROI, and exported the data into MATLAB and Python, our analysis software.…”

Section: Methodsmentioning

confidence: 99%

Data integrity systems for organ contours in radiation therapy planning

Shah

Lakshminarayanan

Moore

et al. 2018

J Applied Clin Med Phys

View full text Add to dashboard Cite

The purpose of this research is to develop effective data integrity models for contoured anatomy in a radiotherapy workflow for both real‐time and retrospective analysis. Within this study, two classes of contour integrity models were developed: data driven models and contiguousness models. The data driven models aim to highlight contours which deviate from a gross set of contours from similar disease sites and encompass the following regions of interest (ROI): bladder, femoral heads, spinal cord, and rectum. The contiguousness models, which individually analyze the geometry of contours to detect possible errors, are applied across many different ROI's and are divided into two metrics: Extent and Region Growing over volume. After analysis, we found that 70% of detected bladder contours were verified as suspicious. The spinal cord and rectum models verified that 73% and 80% of contours were suspicious respectively. The contiguousness models were the most accurate models and the Region Growing model was the most accurate submodel. 100% of the detected noncontiguous contours were verified as suspicious, but in the cases of spinal cord, femoral heads, bladder, and rectum, the Region Growing model detected additional two to five suspicious contours that the Extent model failed to detect. When conducting a blind review to detect false negatives, it was found that all the data driven models failed to detect all suspicious contours. The Region Growing contiguousness model produced zero false negatives in all regions of interest other than prostate. With regards to runtime, the contiguousness via extent model took an average of 0.2 s per contour. On the other hand, the region growing method had a longer runtime which was dependent on the number of voxels in the contour. Both contiguousness models have potential for real‐time use in clinical radiotherapy while the data driven models are better suited for retrospective use.

show abstract

Section: Methodsmentioning

confidence: 99%

Data integrity systems for organ contours in radiation therapy planning

Shah

Lakshminarayanan

Moore

et al. 2018

J Applied Clin Med Phys

View full text Add to dashboard Cite

show abstract

“…MD Anderson has implemented a system‐wide electronic data capture system that records patients’ treatment information 11 . Johns Hopkins has launched the Oncospace program that captures RT data containing anatomy, dose distributions, and outcomes data in an analytical database 12 . The Mayo Clinic’s Department of Radiation Oncology in Florida has linked its radiation oncology information system with Mayo Clinic’s internal claims data warehouse along with Mayo’s tumor registry which allows for large‐scale studies 13 .…”

Section: Impetus For Automated Radiotherapy Data Abstractionmentioning

confidence: 99%

“…11 Johns Hopkins has launched the Oncospace program that captures RT data containing anatomy, dose distributions, and outcomes data in an analytical database. 12 The Mayo Clinic's Department of Radiation Oncology in Florida has linked its radiation oncology information system with Mayo Clinic's internal claims data warehouse along with Mayo's tumor registry which allows for large-scale studies. 13 Whitaker et al at Mayo Clinic at Rochester developed a patient-reported outcome (PRO) collection and management system to implement a largescale aggregation of patients' treatment data.…”

mentioning

confidence: 99%

Automated data abstraction for quality surveillance and outcome assessment in radiation oncology

Kapoor

Sleeman

Nalluri

et al. 2021

J Applied Clin Med Phys

View full text Add to dashboard Cite

Rigorous radiotherapy quality surveillance and comprehensive outcome assessment require electronic capture and automatic abstraction of clinical, radiation treatment planning, and delivery data. We present the design and implementation framework of an integrated data abstraction, aggregation, and storage, curation, and analytics software: the Health Information Gateway and Exchange (HINGE), which collates data for cancer patients receiving radiotherapy. The HINGE software abstracts structured DICOM-RT data from the treatment planning system (TPS), treatment data from the treatment management system (TMS), and clinical data from the electronic health records (EHRs). HINGE software has disease site-specific "Smart" templates that facilitate the entry of relevant clinical information by physicians and clinical staff in a discrete manner as part of the routine clinical documentation.Radiotherapy data abstracted from these disparate sources and the smart templates are processed for quality and outcome assessment. The predictive data analyses are done on using well-defined clinical and dosimetry quality measures defined by disease site experts in radiation oncology. HINGE application software connects seamlessly to the local IT/medical infrastructure via interfaces and cloud services and performs data extraction and aggregation functions without human intervention. It provides tools to assess variations in radiation oncology practices and outcomes and determines gaps in radiotherapy quality delivered by each provider. K E Y W O R D S big data in radiation oncology, quality surveillance 1 | INTRODUCTION Advanced technologies in health care are bringing a sharper focus on clinical outcome assessment and the assessment of health care quality. Manual abstraction, collation, and subsequent analysis of health care quality from patient treatment and outcome data are onerous, expensive, and impractical. Advances in computer storage, computing power, and the ability to electronically mine data from disparate sources (e.g., demographics, genetics, imaging, treatment, clinical decisions, and outcomes) have enabled big data research in medicine. The evolution of several initiatives in the realm of interconnectivity of health care data sources and the availability of advanced computing frameworks have opened doors for answering a broad array of questions related to quality, safety, and outcomes of

show abstract

“…The PPCR collects information on demographics, diagnosis and staging, baseline health status, chemotherapy and surgery, radiation details, diagnostic imaging, and follow-up. 5 Radiation plans are centrally archived in the universal DICOM-RT format. Due to funding issues and required manual effort, there is limited participation and variable data entry.…”

Section: C Pediatric Proton Registry Consortiummentioning

confidence: 99%

“…The PPCR is a consented registry built upon the NIH supported free web‐based data collection/repository platform, REDCap and is currently open to any U.S. proton center that would like to participate. The PPCR collects information on demographics, diagnosis and staging, baseline health status, chemotherapy and surgery, radiation details, diagnostic imaging, and follow‐up . Radiation plans are centrally archived in the universal DICOM‐RT format.…”

Section: Use Case Examplesmentioning

confidence: 99%

Performance/outcomes data and physician process challenges for practical big data efforts in radiation oncology

et al. 2018

View full text Add to dashboard Cite

It is an exciting time for big data efforts in radiation oncology. The use of big data to help aid both outcomes and decision-making research is becoming a reality. However, there are true challenges that exist in the space of gathering and utilizing performance and outcomes data. Here, we summarize the current state of big data in radiation oncology with respect to outcomes and discuss some of the efforts and challenges in radiation oncology big data.

show abstract

Oncospace: All Patients on Trial for Analysis of Outcomes, Toxicities, and IMRT Plan Quality

Cited by 7 publications

References 0 publications

Data integrity systems for organ contours in radiation therapy planning

Data integrity systems for organ contours in radiation therapy planning

Automated data abstraction for quality surveillance and outcome assessment in radiation oncology

Performance/outcomes data and physician process challenges for practical big data efforts in radiation oncology

Contact Info

Product

Resources

About