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Last Updated: 11/19/19
Quantitative Imaging Network (QIN)

Stanford University Department of Radiology (team#1)

Qualification and Deployment of Imaging Biomarkers of Cancer Treatment Response

Daniel Rubin, M.D, M.S.
rubin@stanford.edu

Grant Number: U01CA190214

As cancer treatments being evaluated in clinical trials evolve from chemotherapy to more targeted therapies that may be more effective and cause fewer side effects, there is a pressing need to monitor the response to treatment in patients to ensure these new treatments are working effectively. Imaging is attractive for assessing the response of these new treatments, but the changes in cancers seen in the images may be subtle, and it is critical to apply computerized methods to the images to detect subtle signals (“imaging biomarkers”) that may better detect treatment response than the current approach of simply measuring cancer lesion size. The Quantitative Imaging Network (QIN) is developing such computerized tools, but deploying them into clinical trials is hindered by three major challenges: (1) lack of a flexible tool to compute novel imaging biomarkers as part of the routine clinical trial workflow, (2) lack of decision support tools to assess treatment response in patients or drug effectiveness in clinical trial cohorts using the new imaging biomarkers, and (3) lack of approaches to repurpose the vast collections of image data acquired in clinical trials to acquire evidence for qualifying new imaging biomarkers as surrogate clinical trial endpoints. In this project, we are developing a software platform to enable introducing novel quantitative imaging biomarkers being developed by the QIN and others into clinical trials, and methods to enable qualifying them. We will evaluate the success of our platform by deploying new imaging biomarkers in two clinical trials in individual research sites and in the ECOG-ACRIN cooperative group. To accomplish these goals: (1) We are developing a platform and tools through which to deploy new imaging biomarkers into clinical trials, extending our previously developed Web-based image viewing tool, and we are creating four unique capabilities: a plugin mechanism to execute new quantitative imaging algorithms developed by us or by others in different programming languages, decision support tools for evaluating patient response and treatment effectiveness, and tools that facilitate the workflow of collecting novel imaging biomarkers in clinical trials, that evaluate their benefit over conventional biomarkers, and that collect data which, across clinical trials, will help to qualify them as surrogate endpoints; (2) We are developing methods to repurpose existing imaging data from clinical trials for studying new imaging biomarkers by developing automated image segmentation methods to enable efficient calculation of novel quantitative imaging biomarkers; and (3) We will deploy and evaluate our platform and tools in two cancer centers and the ECOG-ACRIN national cooperative group, and demonstrate their ability to efficiently collect image biomarker data and to facilitate the qualification of new imaging biomarkers. Through the public availability of our platform, its plugin mechanism for introducing new quantitative imaging biomarkers in clinical trials, the intuitive graphical user interfaces for collecting these biomarkers in the image interpretation workflow, de-centralized coordination and oversight of image interpretation in clinical trials, and the tools for decision support, our developments will serve the needs of the QIN and the broader research community, ultimately accelerating clinical trials and the translation of novel image surrogate biomarkers into clinical practice, which will improve the assessment of patient response to new cancer treatments. Our flexible platform and tools will (1) advance cancer research and accelerate clinical trials by enabling novel quantitative imaging biomarkers being developed by QIN researchers and others, which may be more appropriate for newer, targeted anti-cancer agents, to be introduced into the clinical trial workflow, (2) improve both clinical trials and clinical practice by providing decision support about cancer treatment response based on these biomarkers, and (3) accelerate the acquisition of sufficient data needed to qualify new and potentially better imaging biomarkers of cancer treatment response and survival.

Lab URL: http://rubin.web.stanford.edu/

ePAD URL: http://epad.stanford.edu/

Selected Publications:

  1. Rubin DL, Akdogan MU, Altindag C, Alkim E, ePAD: An Image Annotation and Quantitative Analysis Platform for Quantitative Imaging, Tomography 5(1), 170-183, 2019.

  2. Galimzianova A, Siebert SM, Kamaya A, Desser TS, Rubin DL. Toward Automated Pre-Biopsy Thyroid Cancer Risk Estimation in Ultrasound. AMIA Annu Symp Proc. 2018 Apr 16;2017:734-741. eCollection 2017. PubMed PMID: 29854139; PubMed Central PMCID: PMC5977620.

  3. Banerjee I, Madhavan S, Goldman RE, Rubin DL. Intelligent Word Embeddings of Free-Text Radiology Reports. AMIA Annu Symp Proc. 2018 Apr 16;2017:411-420. eCollection 2017. PubMed PMID: 29854105; PubMed Central PMCID: PMC5977573.

  4. Lam C, Yu C, Huang L, Rubin D. Retinal Lesion Detection With Deep Learning Using Image Patches. Invest Ophthalmol Vis Sci. 2018 Jan 1;59(1):590-596. doi: 10.1167/iovs.17-22721. PubMed PMID: 29372258; PubMed Central PMCID: PMC5788045.

  5. Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL. A curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data. 2017 Dec 19;4:170177. doi: 10.1038/sdata.2017.177. PubMed PMID: 29257132; PubMed Central PMCID: PMC5735920.

  6. Banerjee I, Chen MC, Lungren MP, Rubin DL. Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort. J Biomed Inform. 2018 Jan;77:11-20. doi: 10.1016/j.jbi.2017.11.012. Epub 2017 Nov 23. PubMed PMID: 29175548; PubMed Central PMCID: PMC5771955.

  7. Yu KH, Berry GJ, Rubin DL, Ré C, Altman RB, Snyder M. Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma. Cell Syst. 2017 Dec 27;5(6):620-627.e3. doi: 10.1016/j.cels.2017.10.014. Epub 2017 Nov 15. PubMed PMID: 29153840; PubMed Central PMCID: PMC5746468.

  8. Banerjee I, Malladi S, Lee D, Depeursinge A, Telli M, Lipson J, Golden D, Rubin DL. Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging. J Med Imaging (Bellingham). 2018 Jan;5(1):011008. doi: 10.1117/1.JMI.5.1.011008. Epub 2017 Nov 2. PubMed PMID: 29134191; PubMed Central PMCID: PMC5668126.

  9. Echegaray S, Bakr S, Rubin DL, Napel S. Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images. J Digit Imaging. 2018 Aug;31(4):403-414. doi: 10.1007/s10278-017-0019-x. PubMed PMID: 28993897; PubMed Central PMCID: PMC6113159.

  10. Banerjee I, Madhavan S, Goldman RE, Rubin DL. Intelligent Word Embeddings of Free-Text Radiology Reports. AMIA Annu Symp Proc. 2018 Apr 16;2017:411-420. eCollection 2017. PubMed PMID: 29854105; PubMed Central PMCID: PMC5977573.

  11. Lam C, Yu C, Huang L, Rubin D. Retinal Lesion Detection With Deep Learning Using Image Patches. Invest Ophthalmol Vis Sci. 2018 Jan 1;59(1):590-596. doi: 10.1167/iovs.17-22721. PubMed PMID: 29372258; PubMed Central PMCID: PMC5788045.