Imaging has enormous untapped potential to improve cancer research through software to extract and process morphometric and functional biomarkers. In the era of non-cytotoxic treatment agents, multi- modality image-guided ablative therapies and rapidly evolving computational resources, quantitative imaging software can be transformative in enabling minimally invasive, objective and reproducible evaluation of cancer treatment response. Post-processing algorithms are integral to high-throughput analysis and fine- grained differentiation of multiple molecular targets. Software tools used for such analyses must be robust and validated across a range of datasets collected for multiple subjects, timepoints and institutions. Ensuring the validity ofthis software requires unambiguous specification of analysis protocols, documentation ofthe analysis results, and clear guidelines for their interpretation. Yet cancer research data does not exist in formats that facilitate advancement of quantitative analysis and there is lack of an infrastructure to support common data exchange and method sharing. We therefore propose to develop and disseminate interoperable image informatics platform for development of software tools for quantitative imaging biomarker discovery. This platform will enable archival, organization, retrieval, dissemination of the data produced by the novel analysis tools and performance evaluation of quantitative analysis methods. Its functionality will be defined by the needs of the active QIN research projects in quantitative imaging biomarker development for prostate adenocarcinoma, head and neck cancer and glioblastoma multiforme. The infrastructure will be based on 3D Slicer, an NIH funded open source platform for image analysis and visualization, and will be accompanied by sample data and step-by-step documentation. We will (1) develop software tools encapsulating analysis and data organization workflows forthe specific cancer imaging researeh applications;(2) implement support for interoperable open formats accepted in the community to enable disserriination and sharing of the analysis.results;(3) develop interfaces to cbmmunity cancer imaging repositories to enable archival and dissemination ofthe analysis results. '?;: ?:. ?

Public Health Relevance

(Seeinstructions): . . . . . . ,,"""""""" """""""" : , : '. '? - ? '? '? : ? This project will develop the informatics infrastructure for dissemination of image analysis technology and sharing ofthe analysis results and validation data. This will lead to improved traceability ofthe analysis and streamlined multi-site evaluation of imaging biomarkers, ultimately reducing the development time and facilitating the approval process.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
1U24CA180918-01
Application #
8606944
Study Section
Special Emphasis Panel (ZCA1-SRLB-V (O1))
Program Officer
Zhang, Yantian
Project Start
2013-09-04
Project End
2018-08-31
Budget Start
2013-09-04
Budget End
2014-08-31
Support Year
1
Fiscal Year
2013
Total Cost
$724,331
Indirect Cost
$196,922
Name
Brigham and Women's Hospital
Department
Type
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
02115
Fedorov, Andriy; Schwier, Michael; Clunie, David et al. (2018) An annotated test-retest collection of prostate multiparametric MRI. Sci Data 5:180281
Schmainda, K M; Prah, M A; Rand, S D et al. (2018) Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project. AJNR Am J Neuroradiol 39:1008-1016
Malyarenko, Dariya; Fedorov, Andriy; Bell, Laura et al. (2018) Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies. J Med Imaging (Bellingham) 5:011006
Newitt, David C; Malyarenko, Dariya; Chenevert, Thomas L et al. (2018) Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network. J Med Imaging (Bellingham) 5:011003
Peled, Sharon; Vangel, Mark; Kikinis, Ron et al. (2018) Selection of Fitting Model and Arterial Input Function for Repeatability in Dynamic Contrast-Enhanced Prostate MRI. Acad Radiol :
Lasso, Andras; Nam, Hannah H; Dinh, Patrick V et al. (2018) Interaction with Volume-Rendered Three-Dimensional Echocardiographic Images in Virtual Reality. J Am Soc Echocardiogr 31:1158-1160
Black, David; Hahn, Horst K; Kikinis, Ron et al. (2018) Auditory display for fluorescence-guided open brain tumor surgery. Int J Comput Assist Radiol Surg 13:25-35
Chang, Ken; Bai, Harrison X; Zhou, Hao et al. (2018) Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging. Clin Cancer Res 24:1073-1081
Zhou, M; Scott, J; Chaudhury, B et al. (2018) Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. AJNR Am J Neuroradiol 39:208-216
Langkilde, Fredrik; Kobus, Thiele; Fedorov, Andriy et al. (2018) Evaluation of fitting models for prostate tissue characterization using extended-range b-factor diffusion-weighted imaging. Magn Reson Med 79:2346-2358

Showing the most recent 10 out of 34 publications