The University of Iowa (UI) has contributed significantly to the quantitative imaging network (QIN) goals and mission during its first grant cycle. The overall innovation of the UI QIN site is accelerated deliverable tool generation accomplished through a successfully implemented cloud-like distributed workflow infrastructure. Our diversely talented and experienced interdisciplinary team includes oncologists, nuclear medicine physicians, radiologists, physicists, electrical and computer engineers, bioinformaticists, and statisticians. We propose the following new specific aims that build creatively from previous work in a highly innovative fashion and help accelerate QIN progress during this funding period:
Specific Aim 1 : Develop a novel, robust imaging genomics-based decision support platform using a combination of our successful Phase-I developed and validated highly automated quantitative image analysis methods applied to linked and publically-available well curated image (TCIA) and molecular (TCGA) data warehouses along with an established outcomes database for H&N cancers. This will facilitate new methods necessary for future risk adaptive trials that will certainly include both genomic and quantitative image data.
Specific Aim 2 : Build and innovate based on Phase-I developed and validated image analysis tools: a) Apply highly and fully automated quantitative image analysis methods to a cooperative group data set of H&N cancers, b) Develop unique new tools through creative new image analysis methods for application to FLT/PET in H&N cancer, FLT/PET in pelvis and bone marrow, as well as DOTATOC PET/CT for liver metastases in neuroendocrine cancers. These novel approaches will be made publicly available and will contribute to future clinical trials, decision support, quantitative imaging response assessment and therapy targeting in a variety of cancer sites.
Specific Aim 3 : Create a novel link between our established work in PET quantification and calibration phantoms with our image analysis and decision support tools to create a clinically practical open source automated phantom analysis tool that can be applied to national efforts aimed to improve quantitative imaging quality assurance for clinical trials across multiple modalities including PET, CT, and MRI. This will provide a critical tool for improving the ease, accuracy and harmonization for clinical trials data acquisition.
Specific Aim 4 : Adapt, enhance and extend quantitative image-based response assessment in clinical trial decision-support through relevant active clinical trials. Several clinical trials are highlighted exploring:1) FLT/PET as a predictor of bone marrow activity and toxicity in pelvic malignancies treated with chemoradiotherapy, 2) DOTATOC as an indicator of disease burden and treatment response in neuroendocrine tumors and 3) quantitative MR imaging [T2, T1, T1, quantitative susceptibility mapping (QSM) and MRSI] as effective predictors of response in malignant glial tumors treated with intravenous high dose vitamin C. These trials will facilitate quantitative image analysis tool development, decision support tools and risk adaptive approaches in future clinical trials.
This work can positively impact cancer care by determining the effectiveness of cancer treatment more accurately and earlier using detailed analysis of imaging. This may be used alone or combined with genetic information from tumors to help decision making for cancer treatments. Results will be shared so that work among groups will help apply these new imaging and decision making approaches more effectively.
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