The transition of oncologic imaging from its “industrial era” to it is “information era” demands analytical methods that 1) extract information from this data that is clinically and biologically relevant; 2) integrate imaging, clinical, and genomic data via rigorous statistical and computational methodologies in order to derive models valuable for understanding cancer mechanisms, diagnosis, prognostic assessment, response evaluation, and personalized treatment management; 3) are available to the biomedical community for easy use and application, with the aim of understanding, diagnosing, and treating cancer. We propose to extend the Cancer phenomics Toolkit (CapTk), a software suite integrating advanced oncologic image computing and analytics tools which offer sophisticated quantitative analytics of oncologic images well beyond currently used methods. We will refine, document, and integrate our image analysis algorithms and software into CapTk, develop two well-focused research prototype workstations and disseminate software and knowledge via deploying our software to the clinic at Penn as well as to selected collaborating institutions.
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