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Applying computer-aided detection (CAD) generated quantitative image markers has demonstrated significant advantages than using subjectively qualitative assessment in supporting translational clinical research. However, although many advanced CAD schemes have been developed, due to heterogeneity of medical images, achieving high scientific rigor of “black-box” type CAD schemes trained using small datasets remains a big challenge. In order to support and facilitate research effort and progress of physician researchers using quantitative imaging markers, we investigated and tested an interactive approach by developing CAD schemes with interactive functions and visual-aid tools. Thus, unlike fully automated CAD schemes, our interactive CAD tools allow users to visually inspect image segmentation results and provide instruction to correct segmentation errors if needed. Based on users’ instruction, CAD scheme automatically correct segmentation errors, recompute image features and generate machine learning-based prediction scores. We have installed three interactive CAD tools in clinical imaging reading facilities to date, which support and facilitate oncologists to acquire image markers to predict progression-free survival of ovarian cancer patients undergoing angiogenesis chemotherapies, and neurologists to compute image markers and prediction scores to assess prognosis of patients diagnosed with aneurysmal subarachnoid hemorrhage and acute ischemic stroke. Using these ICAD tools, clinical researchers have conducted several translational clinical studies by analyzing several diverse study cohorts, which have resulted in publishing seven peer-reviewed papers in clinical journals in the last three years. Additionally, feedbacks from physician researchers also indicate their increased confidence in using new quantitative image markers and help medical imaging researchers further improve or optimize interactive CAD tools.
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