The NIH/NCI PAR-03-125 invites applications in system software methods that """"""""could include a variety of image processing and data reduction techniques including temporal analysis of serial studies, close to real-time image processing, novel image display methods, and related imaging informatics for more cost-effective solutions for screening."""""""" The significance of such applications is also due to the fact that diagnostic imaging does not end at images from the imaging devices. A diagnostic report through the physicians viewing and interpreting the images is a much crucial part of the process and the quality of the cancer and lesion marking is the core of that part. There are needs to research, develop, and commercialize efficient software systems to improve the quality and consistency of the lesion/cancer marking process, either in clinical practice, cancer/lesion data base development, or educational training of radiologists. So far, however, there is no single system dedicated to meet the challenge these needs present. We proposed, therefore, a novel system and associated methods that integrate advanced real-time interactive and automatic image analysis technologies in both the temporal and spatial domains for improving the consistency of lesion/cancer marking and characterization. The research will advance the state of the art in computational technology applied cancer diagnostic imaging research, and is expected to have broad applications to cancer early detection and screening as well as quality assurance in cancer informatics applications. This research should also help to understand both the common behavior across and variations between radiologist's decision making. This interdisciplinary research will benefit the radiology community, information science, and CAD technology developers in the health care industry. This research needs reasonable patient data for clinical experiments. The National Lung Image Database Consortium (LIDC) has generally expressed its interests in such R&D activities described above and will make its first data set available in the middle of the next year for our experiments, if the project is funded. The success of this project would be a good showcase for the LIDC and the useful system tools resulted from this project would be helpful for the quality assurance in the LIDC data base development. In this consideration, we are willing to share our clinical experimental results in this project with the community.
