|
The need for comprehensive clinical image data and relevant information in image-guided Radiation Therapy (RT) is becoming steadily apparent. Multiple standalone systems utilizing the most technological advancements in imaging, therapeutic radiation, and computerized treatment planning systems acquire key data during the RT treatment course of a patient. One example are patients treated for brain tumors of greater sizes and irregular shapes that utilize state-of-the-art RT technology to deliver pinpoint accurate radiation doses. Various treatment options are available to the patient from Radiation Therapy to Stereotactic Radiosurgery and utilize different RT modalities. The disparate and complex data generated by the RT modalities along with related data scattered throughout the RT department in RT Information/Management systems, Record & Verify systems, and Treatment Planning Systems (TPS) compromise an efficient 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. However, they are rarely used 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. By combining our past experience in medical imaging informatics research, DICOM-RT expertise, and system integration, our research involves using a brain tumor case model to show proof-of-concept that a DICOM-Standard electronic patient record (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. As an initial first step, we will develop a methodology to perform medical imaging informatics research on a clinical scenario where brain tumor patients undergo treatment planning for either radiosurgery or radiation therapy. Specifically, we will research the "inverse treatment planning" process that is used for those types of treatments and integrate decision-support knowledge and tools designed to assist in the decision-making process, thus introducing an improved "knowledge-enhanced treatment planning" approach.
|
