Radiation Therapy seeks to treat cancers through the dosage of destructive radiation to target volumes. The treatment plans, detailing the application of radiation dosage, are currently created to adhere to formal guidelines and target dose levels based on physician experience and trial-and-error rather than standard quantitative methods. We propose a web-based informatics application to introduce data driven methods and uniformity into radiation therapy treatment plan creation. We use a quantitative comparison of tumor position and structural anatomy between retrospective cases and a current case undergoing treatment planning to identify useful and relevant retrospective treatment plans for use as templates and reference during current treatment plan creation. The system is based on a database of 403 retrospective DICOM RT objects from University of California Los Angeles and State University of New York Buffalo; Roswell Park as well as the quantitative features we extract from each case. The quantitative identifiers we develop and use in our feature extraction process are the Overlap Value Histogram (OVH) and the Spatial Target Similarity (STS) calculated between the tumor volume and each Organ At Risk (OAR) of irradiation. The similarity between each retrospective case and the current case is the gower’s distance sum of all the earth mover’s distance values calculated between the OVHs and STSs for each OAR in common between the two cases. By calculating the similarity between the current case and each retrospective case we construct a similarity index from which clinicians can select cases they deem useful in their current treatment planning process. Case outcomes will be stored in our database allowing the discovery of correlations between the structural anatomy, tumor position, treatment plans, and outcome, enabling treatment plan benchmarking. These methods allow our informatics system to increase usage of data driven methodologies and standardized practices in radiation therapy treatment planning.
While there are formal guidelines and target dose levels used in treatment planning for radiation therapy, currently plans are created to adhere to these goals based on physician experience and trial-and-error rather than standard quantitative methods. To introduce uniformity and data driven methods into the radiation therapy treatment planning process we create a web based informatics application which uses algorithmic analysis of historical cases to identify and provide treatment plan templates and treatment benchmarking. The system relies on a database of 360 historical DICOM RT objects from University of California Los Angeles and State University of New York Buffalo; Roswell Park as well as the quantitative features we calculate from each case. To quantitatively identify each case we calculated the overlap volume histogram and spatial target similarity in our feature extraction algorithms. A case undergoing treatment planning when uploaded to our web application will have it’s quantitative features automatically extracted and then our similarity matching algorithm which matches cases based on the similarity/dissimilarity of their quantitative features is used to generate a list of similar historical cases from our database which a physician can then use for reference. Our database also stores treatment outcomes which we will use to establish relationships between the anatomy of the tumor and surrounding organs, the treatment and outcome. These identified relationships will be used in benchmarking and treatment plan assessment. The system aims to increase uniformity of methods and introduce data driven practices into radiation therapy treatment planning.
With the rapid growth in deep learning research for medical applications, the value of making these techniques accessible to clinics also increases. Many medical technology companies now offer deep learning contouring, but researchers are usually limited to the proprietary pre-trained models. To fully explore the technology, researchers must build deep learning pipelines from scratch. We developed an open-source framework for producing automatic contours for 11 common organs-at-risk (OAR) for head and neck planning CT studies using a convolutional neural network (CNN). The pipeline handles DICOM file ingestion, data pre-processing, CNN utilization, output postprocessing, and DICOM structure set file creation to allow end-to-end use interfacing directly with DICOM files. We trained a standard U-Net model on 210 anonymized head and neck patients from our clinic, validated the model’s performance on a test set of 19 patients, and provide the pre-trained weights as a part of the pipeline offering to allow for immediate use. Scripts for retraining the model are also provided to allow customization and new research efforts. Additionally, we offer a framework of all necessary files to support browser-based, no-code contour generation using the Flask package for Python. These contributions lay the foundation for clinical workflow integration. All files are freely available in a public GitHub repository (https://github.com/jasbach/HN_UNet_Autosegmentation_Tool) and are ready for immediate use. Our work offers a demonstrably successful deep learning tool for automatic contouring with a reduced barrier to entry for novice personnel wishing to expand their efforts into the discipline.
We create an informatics web application which uses algorithmic analysis of historical cases to introduce uniformity and data driven methods into the radiation therapy treatment process by providing treatment planning templates and treatment benchmarking. The database the system uses consists of historical DICOM RT objects from which we extract spatial quantitative features. These values are used to generate a list of historical cases from our database similar to a current case which a physician can then use as templates for treatment planning. Our system aims to introduce uniformity of methods and data driven methods into radiation therapy treatment planning.
Currently the methods used to develop radiation therapy treatment plans for head and neck cancers rely on clinician experience and a small set of universal guidelines which result in inconsistent and variable methods. Data driven support can provide assistance to clinicians by reducing inconsistency associated with treatment planning and provide empirical estimates to minimize the radiation to healthy organs near the tumor. We created a database of DICOM RT objects which stores historical cases and when a new DICOM object is uploaded it will return a set of similar treatment plans to assist the clinician in creating the treatment plan for the current patient. The database works first by extracting features from DICOM RT object to quantitatively compare and evaluate the similarity of cases enabling the system to mine for cases with defined similarity. The feature extraction methods are based on the spatial relationships between the tumors and organs at risk which allows the generation the overlap volume histogram and spatial target similarity which demonstrate the volumetric and locational similarity between the organ at risk and the tumor. It is useful to find cases with similar tumor anatomy because this similarity translates to similarity in radiation dosage. The developed system was applied to three different RT sites, University of California Los Angeles, Technical University at Munich and State University of New York Buffalo; Roswell Park, with a total of 247 cases to evaluate the system for both inter- and intra- institutional best practices and results. Future roadmap will be discussed for correlating outcomes results to the decision support system which will enhance the overall performance and utilization of the decision support system in the RT workflow. In the future, because this database returns similar historical cases to a current one this could be a worthwhile decision support tool for clinicians as they create new radiation therapy treatment plans.
Peer reviews are needed across all disciplines of medicine to address complex medical challenges in disease care,
medical safety, insurance coverage handling, and public safety. Radiation therapy utilizes technologically advanced
imaging for treatment planning, often with excellent efficacy. Since planning data requirements are substantial, patients
are at risk for repeat diagnostic procedures or suboptimal therapeutic intervention due to a lack of knowledge regarding
previous treatments. The Peer Review System (PRS) will make this critical radiation therapy information readily
available on demand via Web technology. The PRS system has been developed with current Web technology, .NET
framework, and in-house DICOM library. With the advantages of Web server-client architecture, including IIS web
server, SOAP Web Services and Silverlight for the client side, the patient data can be visualized through web browser
and distributed across multiple locations by the local area network and Internet. This PRS will significantly improve the
quality, safety, and accessibility, of treatment plans in cancer therapy. Furthermore, the secure Web-based PRS with
DICOM-RT compliance will provide flexible utilities for organization, sorting, and retrieval of imaging studies and
treatment plans to optimize the patient treatment and ultimately improve patient safety and treatment quality.
Multiple Sclerosis (MS) is a disease which is caused by damaged myelin around axons of the brain and spinal cord.
Currently, MR Imaging is used for diagnosis, but it is very highly variable and time-consuming since the lesion detection
and estimation of lesion volume are performed manually. For this reason, we developed a CAD (Computer Aided
Diagnosis) system which would assist segmentation of MS to facilitate physician's diagnosis. The MS CAD system
utilizes K-NN (k-nearest neighbor) algorithm to detect and segment the lesion volume in an area based on the voxel. The
prototype MS CAD system was developed under the MATLAB environment. Currently, the MS CAD system consumes
a huge amount of time to process data. In this paper we will present the development of a second version of MS CAD
system which has been converted into C/C++ in order to take advantage of the GPU (Graphical Processing Unit) which
will provide parallel computation. With the realization of C/C++ and utilizing the GPU, we expect to cut running time
drastically. The paper investigates the conversion from MATLAB to C/C++ and the utilization of a high-end GPU for
parallel computing of data to improve algorithm performance of MS CAD.
Multiple Sclerosis (MS) is a progressive neurological disease affecting myelin pathways in the brain. Multiple lesions in
the white matter can cause paralysis and severe motor disabilities of the affected patient. To solve the issue of
inconsistency and user-dependency in manual lesion measurement of MRI, we have proposed a 3-D automated lesion
quantification algorithm to enable objective and efficient lesion volume tracking. The computer-aided detection (CAD)
of MS, written in MATLAB, utilizes K-Nearest Neighbors (KNN) method to compute the probability of lesions on a
per-voxel basis. Despite the highly optimized algorithm of imaging processing that is used in CAD development, MS
CAD integration and evaluation in clinical workflow is technically challenging due to the requirement of high
computation rates and memory bandwidth in the recursive nature of the algorithm. In this paper, we present the
development and evaluation of using a computing engine in the graphical processing unit (GPU) with MATLAB for
segmentation of MS lesions. The paper investigates the utilization of a high-end GPU for parallel computing of KNN in the MATLAB environment to improve algorithm performance. The integration is accomplished using NVIDIA's CUDA developmental toolkit for MATLAB. The results of this study will validate the practicality and effectiveness of the prototype MS CAD in a clinical setting. The GPU method may allow MS CAD to rapidly integrate in an electronic patient record or any disease-centric health care system.
The electronic patient record (ePR) has been developed for prostate cancer patients treated with proton therapy. The ePR
has functionality to accept digital input from patient data, perform outcome analysis and patient and physician profiling,
provide clinical decision support and suggest courses of treatment, and distribute information across different platforms
and health information systems. In previous years, we have presented the infrastructure of a medical imaging informatics based ePR for PT with functionality to accept digital patient information and distribute this information
across geographical location using Internet protocol. In this paper, we present the ePR decision support tools which
utilize the imaging processing tools and data collected in the ePR. The two decision support tools including the treatment
plan navigator and radiation toxicity tool are presented to evaluate prostate cancer treatment to improve proton therapy
operation and improve treatment outcomes analysis.
KEYWORDS: Surgery, Prototyping, Video, Data acquisition, Data integration, Medical imaging, Data modeling, System integration, Data communications, Databases
Last year we presented a paper that describes the design and clinical implementation of an ePR (Electronic Patient Record) system for Image-Assisted Minimally Invasive Spinal Surgery (IA-MISS). The goal of this ePR is to improve the workflow efficiency by providing all the necessary data of a surgical procedure from the preparation stage until the recovery stage. The mentioned ePR has been implemented and installed clinically and it has been in use for more than 16 months. In this paper, we will describe the migration process from a prototype version of the system to a more stable and easily-to-replicate alpha version.
Recent developments in medical imaging informatics have improved clinical workflow in Radiology enterprise but gaps
remain in the clinical workflow from diagnosis to surgical treatment through post-operative follow-up. One solution to
bridge this gap is the development of an electronic patient record (ePR) that integrates key imaging and informatics data
during the pre, intra, and post-operative phases of clinical workflow. We present an ePR system based on standards and
tailored to the clinical application for image-guided minimally invasive spinal surgery (MISS). The ePR system has
been implemented in a clinical environment for a half-year.
Last year, we presented the infrastructure for a medical imaging informatics DICOM-RT based ePR system for patients
treated with Proton Therapy (PT). The ePR has functionality to integrate patients' imaging and informatics data and
perform outcomes analysis with patient and physician profiling in order to provide clinical decision support and suggest
courses of treatment. In this paper, we present the development of a prototype for the image-guided outcomes analysis
for prostate cancer patient based on DICOM-RT ePR. This ePR system, using DICOM-RT and DICOM-ION objects as
well as clinical and biological parameters, provides tools to evaluate treatment plans and assess the outcomes of the
patient's treatment; hence, it promotes more successful treatment planning for new prostate cancer patients treated with
proton therapy.
KEYWORDS: Computer aided design, Picture Archiving and Communication System, Computer aided diagnosis and therapy, Mammography, System integration, Image processing, Chemical vapor deposition, Brain, Medicine, Medical imaging
Digital Imaging and Communications in Medicine (DICOM) has standardized structure reports (SR) to fully support
conventional free-text reports, images, and structured information, thus enhancing the precision, clarity, and value of
clinical documents. The SR standard provides the capacity to link key images, region of interest within images, and
measurement as result of Computer-Aided Diagnosis (CAD) process. Accordingly, SR bridges the traditional gap
between CAD and PACS. Last year we presented an open and universal CAD-PACS integration toolkit that could
seamlessly integrate standalone Computer-Aided Diagnosis (CAD) workstations with a clinical PACS based on
Structure Report (SR) and IHE Post-Processing. In this presentation, we illustrate the workflow and procedures of CAD-PACS
integration by showing examples from some available CAD applications using the toolkit. This proper integration
will improve usage of the CAD applications for more accurate analysis and faster assessment in the clinical decisionmaking
process.
Last year, we presented methodology to perform knowledge-based medical imaging informatics research on specific
clinical scenarios where brain tumor patients are treated with Proton Beam Therapy (PT). In this presentation, we
demonstrate the design and implementation of quantification and visualization tools to develop the knowledge base for
therapy treatment planning based on DICOM-RT-ION objects. Proton Beam Therapy (PT) is a particular treatment that
utilizes energized charged particles, protons, to deliver dose to the target region. Similar to traditional Radiation Therapy
(RT), complex clinical imaging and informatics data are generated during the treatment process that guide the planning
and the success of the treatment. Therefore, an Electronic Patient Record (ePR) System has been developed to
standardize and centralize clinical imaging and informatics data and properly distribute data throughout the treatment
duration. To further improve treatment planning process, we developed a set of decision support tools to improve the
QA process in treatment planning process. One such example is a tool to assist in the planning of stereotactic PT cases
where CT and MR images need to be analyzed simultaneously during treatment plan assessment. These tools are add-on
features for DICOM standard ePR system of brain cancer patients and improve the clinical efficiency of PT treatment
planning. Additional outcome data collected for PT cases are included in the overall DICOM-RT-ION database design
as knowledge to enhance outcomes analysis for future PT adopters.
During the last 2 years we have been working on developing a DICOM-RT (Radiation Therapy) ePR (Electronic Patient
Record) with decision support that will allow physicists and radiation oncologists during their decision-making process.
This ePR allows offline treatment dose calculations and plan evaluation, while at the same time it compares and
quantifies treatment planning algorithms using DICOM-RT objects. The ePR framework permits the addition of
visualization, processing, and analysis tools, which combined with the core functionality of reporting, importing and
exporting of medical studies, creates a very powerful application that can improve the efficiency while planning cancer
treatments.
Usually a Radiation Oncology department will have disparate and complex data generated by the RT modalities as well
as data scattered in RT Information/Management systems, Record & Verify systems, and Treatment Planning Systems
(TPS) which can compromise the efficiency of the clinical workflow since the data crucial for a clinical decision may be
time-consuming to retrieve, temporarily missing, or even lost. To address these shortcomings, the ACR-NEMA
Standards Committee extended its DICOM (Digital Imaging & Communications in Medicine) standard from Radiology
to RT by ratifying seven DICOM RT objects starting in 1997 [1,2]. However, they are not broadly used yet by the RT
community in daily clinical operations. In the past, the research focus of an RT department has primarily been
developing new protocols and devices to improve treatment process and outcomes of cancer patients with minimal effort
dedicated to integration of imaging and information systems. Our attempt is to show a proof-of-concept that a DICOM-RT
ePR system can be developed as a foundation to perform medical imaging informatics research in developing
decision-support tools and knowledge base for future data mining applications.
The Medical Imaging Informatics (MI2) Data Grid developed at the USC Image Processing and Informatics Laboratory
enables medical images to be shared securely between multiple imaging centers. Current applications include an
imaging-based clinical trial setting where multiple field sites perform image acquisition and a centralized radiology core
performs image analysis, often using computer-aided diagnosis tools (CAD) that generate a DICOM-SR to report their
findings and measurements. As more and more CAD tools are being developed in the radiology field, the generated
DICOM Structure Reports (SR) holding key radiological findings and measurements that are not part of the DICOM
image need to be integrated into the existing Medical Imaging Informatics Data Grid with the corresponding imaging
studies. We will discuss the significance and method involved in adapting DICOM-SR into the Medical Imaging
Informatics Data Grid. The result is a MI2 Data Grid repository from which users can send and receive DICOM-SR
objects based on the imaging-based clinical trial application. The services required to extract and categorize information
from the structured reports will be discussed, and the workflow to store and retrieve a DICOM-SR file into the existing
MI2 Data Grid will be shown.
Computer Aided Diagnosis (CAD) coupled with physician's knowledge can improve accuracy of clinical decision.
However, many developed CAD software have no features to integrate its results with a picture archive and
communication system (PACS). This obstacle hinders the extensive use of independent CAD results within a more
streamlined diagnosis workflow. In this paper, we demonstrate a universal PACS-CAD toolkit that can seamlessly
integrate independent CAD results with a clinical PACS. The PACS-CAD toolkit consisted of two versions, a DICOM
Secondary Capture (DICOM-SC) version and a DICOM-IHE version to accommodate various PACS. The DICOM-SC
version toolkit installed on a CAD workstation converts the screen shot of CAD results to a DICOM image file for
storing in a PACS server and displaying on PACS workstations. The DICOM-IHE version toolkit follows DICOM and
IHE standards using DICOM Structured Report and Post-Processing Workflow Profiles; thus, results from various CAD
software can be integrated into diagnosis workflow of a PACS having DICOM and IHE-compliance and, most
importantly, these quantified CAD results can be directly queried for and retrieved from within PACS for future data
mining applications. The successful implementation of this toolkit can greatly ease the extensive use of various CAD
results in the clinical diagnosis workflow.
Last year we presented work on an imaging informatics approach towards developing quantitative knowledge and tools
based on standardized DICOM-RT objects for Image-Guided Radiation Therapy. In this paper, we have extended this
methodology to perform knowledge-based medical imaging informatics research on specific clinical scenarios where
brain tumor patients are treated with Proton Beam Therapy (PT). PT utilizes energized charged particles, protons, to
deliver dose to the target region. Protons are energized to specific velocities which determine where they will deposit
maximum energy within the body to destroy cancerous cells. Treatment Planning is similar in workflow to traditional
Radiation Therapy methods such as Intensity-Modulated Radiation Therapy (IMRT) which utilizes a priori knowledge
to drive the treatment plan in an inverse manner. In March 2006, two new RT Objects were drafted in a DICOM-RT
Supplement 102 specifically for Ion Therapy which includes Proton Therapy. The standardization of DICOM-RT-ION
objects and the development of a knowledge base as well as decision-support tools that can be add-on features to the ePR
DICOM-RT system were researched. We have developed a methodology to perform knowledge-based medical imaging
informatics research on specific clinical scenarios. This methodology can be used to extend to Proton Therapy and the
development of future clinical decision-making scenarios during the course of the patient's treatment that utilize "inverse
treatment planning". In this paper, we present the initial steps toward extending this methodology for PT and lay the
foundation for development of future decision-support tools tailored to cancer patients treated with PT. By integrating
decision-support knowledge and tools designed to assist in the decision-making process, a new and improved
"knowledge-enhanced treatment planning" approach can be realized.
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