SBIR Phase II: A Minable, Quantitative Imaging Platform for Evidence-Based Medicine within Oncology

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to significantly improve the care of cancer patients by providing an integrated platform for clinical data and image analytics to their care providers for better clinical decision making. Tight integration of clinical data with radiology images will enable evidence-based approaches to be used by care providers in oncology. The tools being developed in this project will enable accelerated transition of comprehensive data-driven cancer research into clinical practice for decision making related to diagnosis and treatment of cancer. Recent trends, such as wide adoption of electronic medical records and radiological images and availability of powerful computing at reasonable prices have made it possible to improve the prognostic and diagnostic power of data, in particular imaging data analysis in healthcare. The integrated analytics platform being developed will streamline treatment monitoring by imaging and reduce diagnostic errors; hence increasing the quality to cancer care. The proposed project aims to develop an integrated, minable, clinical and imaging data analytics platform for oncology. The platform combines recent advances in data mining, context search, image segmentation and deformable registration into an imaging system deployed at the point of care. The diagnostic potential of clinical and image data will be enhanced by the ability to compare lesion characteristics of the current case to a large repository of lesions from other studies with known diagnosis. The proposed search tool will generate patient cohorts with a given set of diagnosis and treatment conditions and present the related images to the diagnostician. Additional technology will locate and track tumors and provide detailed characterization such as size, shape, location and texture with detailed analytics. A large, minable database of segmented tumors and detailed metrics will advance research into identifying ?imaging biomarkers?. The embedded imaging platform will allow clinicians to access these decision support tools across a wide spectrum of devices from powerful personal computers to tablets and other mobile devices.