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Last Updated: 09/21/17
Quantitative Imaging Network (QIN)

University of Iowa

Quantitative Imaging to Assess Response in Clinical Therapy Trials

John M. Buatti, M.D.
Grant Number: U01 CA140206

To fulfil the broad role of improving quantitative imaging for clinical decision making, the Iowa QIN team is pursuing the development of tools for quantitative image analysis for both assessment of response and tumor targeting. Their trajectory is guided by four specific aims:

Specific Aim 1: Develop a novel, robust imaging genomics-based decision support platform using a combination of Phase-I developed and validated highly automated, quantitative image analysis methods applied to linked and publicly-available curated image (TCIA) and molecular (The Cancer Genome Atlas–TCGA) data warehouses along with an established outcomes database for H&N cancers. Progress: The team has refined and enhanced their genomic variant analysis pipeline to identify highly-informative features for prediction and decision support. This informatics pipeline utilizes currently-available TCGA data for H&N cancers for which TCIA data is also available. The team has developed the metrics and data from their Phase I image analysis tool to build predictive models. As patient expansion increases and feature selection improves they will release their feature selection algorithms to the QIN community.

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 for liver metastases in neuroendocrine cancers.

Specific Aim 3: Create a novel link between their established work in PET quantification and calibration phantoms with their 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.

Progress (Specific Aims 2 and 3): The Iowa QIN team has enhanced their publicly released open-source software for quantitative PET image analysis, consisting of three extensions as well as the supporting libraries (see They have developed a fully-automated quantitative PET phantom analysis algorithm, allowing the user to segment ACR/ACRIN-ECOG, SNMMI/CTN, and NEMA NU-2 image quality phantoms and will simplify PET scan image quality assessment.

To augment their H&N PET/CT image collection (already available on TCIA collection: “QIN-HeadNeck”), they encoded a) segmentations and quantitative measurements of lesions derived from Iowa H&N PET/CT image data and b) clinical data related to the Iowa H&N PET/CT image data in standard DICOM format and published it on TCIA. They completed their QIN PET Phantom and Clinical Head and Neck Segmentation challenge with the summary paper providing insight on improving (multi-site) quantitative PET image analysis performance. Development activities for an FLT based tool for head and neck cancer as well as for DOTATOC for tumor burden in liver are also under development but are not yet mature.

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 in neuroendocrine tumors and 3) quantitative MR imaging [T2, T1, T1r, 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. Progress: They are pursuing imaging methods to assess tumor response to pharmacological ascorbate as an adjuvant to standard of care therapy. Peak plasma concentrations of ascorbate are currently measured as part of the trial but do not directly report the concentrations within the tumor. Work is on-going in phantoms and pre-clinical models to further evaluate this relationship.

Iowa QIN Recent Publications

  1. Graham MM, Wahl RL, Hoffman JM, Yap JT, Sunderland JJ, Boellaard R, Perlman ES, Kinahan PE, Christian PE, Hoekstra OS, Dorfman GS. Summary of the UPICT Protocol for 18F-FDG PET/CT Imaging in Oncology Clinical Trials. J Nucl Med. Jun 2015;56(6):955-961. PMID: 25883122. PMCID: 4587663.

  2. Beichel RR, Van Tol M, Ulrich EJ, Bauer C, Chang T, Plichta KA, Smith BJ, Sunderland JJ, Graham MM, Sonka M, Buatti JM. Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach. Med Phys. Jun 2016;43(6):2948. PMID: 27277044. PMCID: 4874930.

  3. Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B, Onken M, Riesmeier J, Pieper S, Kikinis R, Buatti J, Beichel RR. DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ. 2016; 4:e2057. PMID: 27257542. PMCID: 4888317.

  4. Kurland BF, Aggarwal S, Yankeelov TE, Gerstner ER, Mountz JM, Linden HM, Jones EF, Bodeker KL, Buatti JM. Accrual Patterns for Clinical Studies Involving Quantitative Imaging: Results of an NCI Quantitative Imaging Network (QIN) Survey. Tomography. Dec 2016;2(4):276-282. PMID: 28127586. PMCID: 5260812.

  5. McGuire SM, Bhatia SK, Sun W, Jacobson GM, Menda Y, Ponto LL, Smith BJ, Gross BA, Bayouth JE, Sunderland JJ, Graham MM, Buatti JM. Using [(18)F]Fluorothymidine Imaged With Positron Emission Tomography to Quantify and Reduce Hematologic Toxicity Due to Chemoradiation Therapy for Pelvic Cancer Patients. Int J Radiat Oncol Biol Phys. Sep 01 2016;96(1):228-239. PMID: 27319286. PMCID: 4982822.

  6. Pierce Ii LA, Byrd DW, Elston BF, Karp JS, Sunderland JJ, Kinahan PE. An algorithm for automated ROI definition in water or epoxy-filled NEMA NU-2 image quality phantoms. J Appl Clin Med Phys. Jan 08 2016;17(1):5842. PMID: 26894356. PMCID: 4874494.

  7. Yankeelov TE, Mankoff DA, Schwartz LH, Lieberman FS, Buatti JM, Mountz JM, Erickson BJ, Fennessy FM, Huang W, Kalpathy-Cramer J, Wahl RL, Linden HM, Kinahan PE, Zhao B, Hylton NM, Gillies RJ, Clarke L, Nordstrom R, Rubin DL. Quantitative Imaging in Cancer Clinical Trials.Clin Cancer Res. Jan 15 2016;22(2):284-290. PMID: 26773162. PMCID: 4717912.

  8. Anderson CM, Chang T, Graham MM, Marquardt MD, Button A, Smith BJ, Menda Y, Sun W, Pagedar NA, Buatti JM. Change of maximum standardized uptake value slope in dynamic triphasic [18F]-fluorodeoxyglucose positron emission tomography/computed tomography distinguishes malignancy from postradiation inflammation in head-and-neck squamous cell carcinoma: a prospective trial. Int J Radiat Oncol Biol Phys. 2015 Mar 1;91(3):472-9. PubMed PMID: 25680593; PubMed Central PMCID: PMC4335357

  9. Beichel RR, Smith BJ, Bauer C, Ulrich EJ, Ahmadvand P, Budzevich MM, Gillies RJ, Goldgof D, Grkovski M, Hamarneh G, Huang Q, Kinahan PE, Laymon CM, Mountz JM, Muzi JP, Muzi M, Nehmeh S, Oborski MJ, Tan Y, Zhao B, Sunderland JJ, Buatti JM. Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data. Med Phys. 2017 Feb;44(2):479-496. doi: 10.1002/mp.12041. PubMed PMID: 28205306.

  10. Farahani, Keyvan, Jayashree Kalpathy-Cramer, Thomas L. Chenevert, Daniel L. Rubin, John J. Sunderland, Robert J. Nordstrom, John Buatti, and Nola Hylton. "Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network." Tomography 2, no. 4 (2016): 242-249. NIHMSID: 839194 (Approved: PMID pending).

The University of Iowa’s QI group
The University of Iowa’s QI group