Ruchi Deshpande

Role of an imaging informatics-based DICOM-RT cancer registry in evaluating treatment parameters of IMRT for prostate cancer

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.

A collaborative framework for contributing DICOM RT PHI (Protected Health Information) to augment data mining in clinical decision support

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.

Integration of imaging informatics-based multiple sclerosis eFolder system for multi-site clinical trials utilizing IHE workflow profiles

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.

Comparison of 2D versus 3D mammography with screening cases: an observer study

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.

The development of a decision support system with an interactive clinical user interface for estimating treatment parameters in radiation therapy in order to reduce radiation dose in head and neck patients

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.

Multi-site evaluation of a clinical decision support system for radiation therapy

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.

Design and evaluation of an imaging informatics system for analytics-based decision support in radiation therapy

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.

A concept of a generalized electronic patient record for personalized medicine

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.

Utilization of DICOM multi-frame objects for integrating kinetic and kinematic data with raw videos in movement analysis of wheel-chair users to minimize shoulder pain

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.

A web-based electronic patient record (ePR) system for data integration in movement analysis research on wheel-chair users to minimize shoulder pain

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.