KEYWORDS: Radiotherapy, Data processing, Technologies and applications, Medical imaging, Decision support systems, Human-machine interfaces, Head, Neck, Tissues, Tumors, Databases, Prototyping, Data modeling, Information science
The primary goal in radiation therapy is to target the tumor with the maximum possible radiation dose while limiting the radiation exposure of the surrounding healthy tissues. However, in order to achieve an optimized treatment plan, many constraints, such as gender, age, tumor type, location, etc. need to be considered. The location of the malignant tumor with respect to the vital organs is another possible important factor for treatment planning process which can be quantified as a feature making it easier to analyze its effects. Incorporation of such features into the patient’s medical history could provide additional knowledge that could lead to better treatment outcomes. To show the value of features such as relative locations of tumors and surrounding organs, the data is first processed in order to calculate the features and formulate a feature matrix. Then these feature are matched with retrospective cases with similar features to provide the clinician with insight on delivered dose in similar cases from past. This process provides a range of doses that can be delivered to the patient while limiting the radiation exposure of surrounding organs based on similar retrospective cases. As the number of patients increase, there will be an increase in computations needed for feature extraction as well as an increase in the workload for the physician to find the perfect dose amount. In order to show how such algorithms can be integrated we designed and developed a system with a streamlined workflow and interface as prototype for the clinician to test and explore. Integration of the tumor location feature with the clinician’s experience and training could play a vital role in designing new treatment algorithms and better outcomes. Last year, we presented how multi-institutional data into a decision support system is incorporated. This year the presentation is focused on the interface and demonstration of the working prototype of informatics system.
We have developed an imaging informatics based decision support system that learns from retrospective treatment plans
to provide recommendations for healthy tissue sparing to prospective incoming patients. This system incorporates a
model of best practices from previous cases, specific to tumor anatomy. Ultimately, our hope is to improve clinical
workflow efficiency, patient outcomes and to increase clinician confidence in decision-making. The success of such a
system depends greatly on the training dataset, which in this case, is the knowledge base that the data-mining algorithm
employs. The size and heterogeneity of the database is essential for good performance. Since most institutions employ
standard protocols and practices for treatment planning, the diversity of this database can be greatly increased by
including data from different institutions. This work presents the results of incorporating cross-country, multi-institutional
data into our decision support system for evaluation and testing.
We have developed a comprehensive DICOM RT specific database of retrospective treatment planning data for radiation therapy of head and neck cancer. Further, we have designed and built an imaging informatics module that utilizes this database to perform data mining. The end-goal of this data mining system is to provide radiation therapy decision support for incoming head and neck cancer patients, by identifying best practices from previous patients who had the most similar tumor geometries. Since the performance of such systems often depends on the size and quality of the retrospective database, we have also placed an emphasis on developing infrastructure and strategies to encourage data sharing and participation from multiple institutions. The infrastructure and decision support algorithm have both been tested and evaluated with 51 sets of retrospective treatment planning data of head and neck cancer patients. We will present the overall design and architecture of our system, an overview of our decision support mechanism as well as the results of our evaluation.
KEYWORDS: Data modeling, Decision support systems, Databases, Computed tomography, Radiotherapy, Data mining, Tumors, Data processing, Imaging informatics, Machine learning
We have built a decision support system that provides recommendations for customizing radiation therapy treatment plans, based on patient models generated from a database of retrospective planning data. This database consists of relevant metadata and information derived from the following DICOM objects - CT images, RT Structure Set, RT Dose and RT Plan. The usefulness and accuracy of such patient models partly depends on the sample size of the learning data set. Our current goal is to increase this sample size by expanding our decision support system into a collaborative framework to include contributions from multiple collaborators. Potential collaborators are often reluctant to upload even anonymized patient files to repositories outside their local organizational network in order to avoid any conflicts with HIPAA Privacy and Security Rules. We have circumvented this problem by developing a tool that can parse DICOM files on the client’s side and extract de-identified numeric and text data from DICOM RT headers for uploading to a centralized system. As a result, the DICOM files containing PHI remain local to the client side. This is a novel workflow that results in adding only relevant yet valuable data from DICOM files to the centralized decision support knowledge base in such a way that the DICOM files never leave the contributor’s local workstation in a cloud-based environment. Such a workflow serves to encourage clinicians to contribute data for research endeavors by ensuring protection of electronic patient data.
The treatment process of tumor patients is supported by different stand-alone ePR and clinical decision support (CDS) systems. We developed a concept for the integration of a specialized ePR for head and neck tumor treatment and a DICOM-RT based CDS system for radiation therapy in order to improve the clinical workflow and therapy outcome. A communication interface for the exchange of information that is only available in the respective other system will be realized. This information can then be used for further assistance and clinical decision support functions. In the first specific scenario radiation therapy related information such as radiation dose or tumor size are transmitted from the CDS to the ePR to extend the information base. This information can then be used for the automatic creation of clinical documents or retrospective clinical trial studies. The second specific use case is the transmission of follow-up information from the ePR to the CDS system. The CDS system uses the current patient’s anatomy and planned radiation dose distribution for the selection of other patients that already received radiation therapy. Afterwards, the patients are grouped according to the therapy outcome so that the physician can compare radiation parameters and therapy results for choosing the best possible therapy for the patient. In conclusion this research project shows that centralized information availability in tumor therapy is important for the improvement of the patient treatment process and the development of sophisticated decision support functions.
Cancer registries are information systems that enable easy and efficient collection, organization and utilization of data related to cancer patients for the purpose of epidemiological research, evidence based medicine and planning of public health policies. Our research focuses on developing a web-based system which incorporates aspects of both cancer registry information systems and medical imaging informatics, in order to provide decision support and quality control in external beam radiation therapy. Integrated within this system is a knowledge base composed of retrospective treatment plan data sets of 42 patients, organized in a systematic fashion to aid query, retrieval and data mining. A major cornerstone of our system is the use of DICOM RT data sets as the building blocks of the database. This offers enormous practical advantages since it establishes a framework that can assimilate data from different treatment planning systems and across institutions by making use of a widely used standard – DICOM. Our system will help clinicians to assess their dose volume constraints for prospective patients. This is done by comparing the anatomical configuration of an incoming patient’s tumor and surrounding organs, to that of retrospective patients in the knowledge base. Treatment plans of previous patients with similar anatomical features are retrieved automatically for review by the clinician. The system helps the clinician decide whether his dose/volume constraints for the prospective patient are optimal based on the constraints of the matched retrospective plans. Preliminary results indicate that this small-scale cancer registry could be a powerful decision support tool in radiation therapy treatment planning in IMRT.
At last year’s SPIE, we presented a multiple sclerosis (MS) eFolder as an integrated imaging-informatics based system to provide several functionalities to both clinical and research environments. The eFolder system combines patients’ clinical data, radiological images and computer-aided lesion detection and quantification results to aid in longitudinal tracking, data mining, decision support, and other clinical and research needs. To demonstrate how this system can be integrated in an existing imaging environment such as a large-scale multi-site MS clinical trial, we present a system infrastructure to streamline imaging and clinical data flow with postprocessing (CAD) steps. The system stores clinical and imaging data, provides CAD postprocessing algorithm and data storage, and a web-based graphical user interface (GUI) to view clinical trial data and monitor workflow. To evaluate the system infrastructure, the MS eFolder is set up in a simulated environment with workflow scenarios, including DICOM store, query, and retrieve, automatic CAD steps, and data mining based on CAD results. This project aims to discuss the methodology of setting up eFolder system simulation with a connection to a CAD server component, simulation performance and test results, and discussion of eFolder system deployment results.
Breast cancer is the most common type of non-skin cancer in women. 2D mammography is a screening tool to aid in the
early detection of breast cancer, but has diagnostic limitations of overlapping tissues, especially in dense breasts. 3D
mammography has the potential to improve detection outcomes by increasing specificity, and a new 3D screening tool
with a 3D display for mammography aims to improve performance and efficiency as compared to 2D mammography.
An observer study using human studies collected from was performed to compare traditional 2D mammography with
this new 3D mammography technique. A prior study using a mammography phantom revealed no difference in
calcification detection, but improved mass detection in 2D as compared to 3D. There was a significant decrease in
reading time for masses, calcifications, and normals in 3D compared to 2D, however, as well as more favorable
confidence levels in reading normal cases.
Data for this current study is currently being obtained, and a full report should be available in the next few weeks.
KEYWORDS: Data modeling, Video, Analytical research, Motion analysis, Databases, System integration, Data integration, Data analysis, Injuries, Imaging informatics
Patients confined to manual wheel-chairs are at an added risk of shoulder injury. There is a need for developing optimal
bio-mechanical techniques for wheel-chair propulsion through movement analysis. Data collected is diverse and in need
of normalization and integration. Current databases are ad-hoc and do not provide flexibility, extensibility and ease of
access. The need for an efficient means to retrieve specific trial data, display it and compare data from multiple trials is
unmet through lack of data association and synchronicity. We propose the development of a robust web-based ePR
system that will enhance workflow and facilitate efficient data management.
KEYWORDS: Video, Motion analysis, Visualization, Data conversion, Video processing, Video coding, Kinematics, Data mining, Information fusion, 3D modeling
Wheelchair users are at an increased risk of developing shoulder pain. The key to formulating correct wheelchair
operating practices is to analyze the movement patterns of a sample set of subjects. Data collected for movement
analysis includes videos and force/ motion readings. Our goal is to combine the kinetic/ kinematic data with the trial
video by overlaying force vector graphics on the raw video. Furthermore, conversion of the video to a DICOM multiframe
object annotated with the force vector could provide a standardized way of encoding and analyzing data across
multiple studies and provide a useful tool for data mining.
Acute Intracranial hemorrhage (AIH) requires urgent diagnosis in the emergency setting to mitigate eventual sequelae.
However, experienced radiologists may not always be available to make a timely diagnosis. This is especially true for
small AIH, defined as lesion smaller than 10 mm in size. A computer-aided detection (CAD) system for the detection of
small AIH would facilitate timely diagnosis. A previously developed 2D algorithm shows high false positive rates in the
evaluation based on LAC/USC cases, due to the limitation of setting up correct coordinate system for the
knowledge-based classification system. To achieve a higher sensitivity and specificity, a new 3D algorithm is developed.
The algorithm utilizes a top-hat transformation and dynamic threshold map to detect small AIH lesions. Several key
structures of brain are detected and are used to set up a 3D anatomical coordinate system. A rule-based classification of
the lesion detected is applied based on the anatomical coordinate system. For convenient evaluation in clinical
environment, the CAD module is integrated with a stand-alone system. The CAD is evaluated by small AIH cases and
matched normal collected in LAC/USC. The result of 3D CAD and the previous 2D CAD has been compared.
Our goal in this paper is to data mine the wealth of information contained in the dose-volume objects used in external
beam radiotherapy treatment planning. In addition, by performing computational pattern recognition on these mined
objects, the results may help identify predictors for unsafe dose delivery. This will ultimately enhance current clinical
registries by the inclusion of detailed dose-volume data employed in treatments. The most efficient way of including
dose-volume information in a registry is through DICOM RT objects. With this in mind, we have built a DICOM RT
specific infrastructure, capable of integrating with larger, more general clinical registries, and we will present the results
of data mining these sets.
Acute intra-cranial hemorrhage (AIH) may result from traumatic brain injury (TBI). Successful management of AIH
depends heavily on the speed and accuracy of diagnosis. Timely diagnosis in emergency environments in both civilian
and military settings is difficult primarily due to severe time restraints and lack of resources. Often, diagnosis is
performed by emergency physicians rather than trained radiologists. As a result, added support in the form of computer-aided
detection (CAD) would greatly enhance the decision-making process and help in providing faster and more
accurate diagnosis of AIH. This paper discusses the implementation of a CAD system in an emergency environment, and
its efficacy in aiding in the detection of AIH.
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.
Timely detection of Acute Intra-cranial Hemorrhage (AIH) in an emergency environment is essential for the triage of
patients suffering from Traumatic Brain Injury. Moreover, the small size of lesions and lack of experience on the
reader's part could lead to difficulties in the detection of AIH. A CT based CAD algorithm for the detection of AIH has
been developed in order to improve upon the current standard of identification and treatment of AIH. A retrospective
analysis of the algorithm has already been carried out with 135 AIH CT studies with 135 matched normal head CT
studies from the Los Angeles County General Hospital/ University of Southern California Hospital System (LAC/USC).
In the next step, AIH studies have been collected from Walter Reed Army Medical Center, and are currently being processed using the AIH CAD system as part of implementing a multi-site assessment and evaluation of the performance of the algorithm. The sensitivity and specificity numbers from the Walter Reed study will be compared with the numbers from the LAC/USC study to determine if there are differences in the presentation and detection due to the difference in the nature of trauma between the two sites. Simultaneously, a stand-alone system with a user friendly GUI has been developed to facilitate implementation in a clinical setting.
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