Correction of Diffusion Gradient Bias in Quantitative Diffusivity Metrics for MultiPlatform Clinical Oncology Trials

多平台临床肿瘤学试验定量扩散率指标中扩散梯度偏差的校正

• 批准号: 10664979
• 负责人: Dariya I. Malyarenko
• 金额: $ 63.67万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-10 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10664979
• 关键词: Address; Adoption; Agreement; American College of Radiology Imaging Network; Benchmarking; Biological; Bone Marrow; Breast; California; Cancer Detection; Cancer Patient; Clinical; Clinical Management; Clinical Trials; Clinical Trials Cooperative Group; Collaborations; Complex; Data; Detection; Development; Diagnosis; Diagnostic; Diffusion; Early treatment; Engineering; Ensure; Generations; Goals; Head and neck structure; Image; Imaging technology; Industrialization; Institution; Left; Lesion; Magnetic Resonance Imaging; Malignant Neoplasms; Maps; Memorial Sloan-Kettering Cancer Center; Michigan; Modeling; Monitor; Multi-Institutional Clinical Trial; Oncology; Organ; Output; Parents; Pathology; Patients; Performance; Prediction of Response to Therapy; Procedures; Protocols documentation; Radiation; Radiology Specialty; Reproducibility; Research; Research Personnel; Route; San Francisco; Scanning; Scientist; Site; Source; Standardization; System; Systematic Bias; Technology; Testing; Therapeutic; Tissue Model; Tissues; Translations; Universities; Variant; Vendor; Washington; cancer biomarkers; cancer clinical trial; cancer imaging; clinical application; clinical decision-making; clinical imaging; cohort; cost; design; flexibility; image registration; imaging capabilities; imaging facilities; imaging platform; improved; industry partner; malignant breast neoplasm; manufacture; oncology trial; parent project; prognostic; prospective; prototype; public health relevance; quantitative imaging; success; tool; translational cancer research; translational oncology; treatment response; tumor

Abstract Multi-center clinical trials increasingly utilize quantitative diffusion imaging (DWI) to aid in patient management and treatment response assessment for translational oncology applications. A major source of systematic bias in diffusion was discovered originating from platform-dependent gradient hardware. Left uncorrected, these biases confound quantitative diffusion metrics used for characterization of tissue pathology and treatment response leading to inconclusive findings, and increasing the requisite subject numbers and trial cost. Technical remedy was defined by University of Michigan investigators and motivated our parent AIP involving the three dominant MRI manufactures. The current AIP merged expertise among commercial scientists/engineers and academic researchers, and resulted in successful design and development of prototype correction tools to eliminate systematic diffusion weighting bias in quantitative DWI applications across diverse clinical MRI platforms. As a result, two vendors have implemented prototype tools on their respective scanner platforms for correction of mean tissue diffusivity metric widely used in oncology trials. Furthermore, feasibility of retrospective correction across all three vendor platforms was demonstrated for the repeatability cohort of ACRIN 6698 breast cancer imaging trial. Our AIP participation with clinical trial cooperative groups and quantitative imaging consortia revealed that the most efficient route for adoption of developed technology on clinical platforms is by vendor implementation. Additional need was noted for flexible integration with advanced acquisition protocols and analyses using multiple b-values to accurately quantify complex metrics beyond mean diffusivity. To address these needs, the renewal AIP will extend our collaborations to include two additional academic cancer imaging centers to integrate and validate developed practical correction tools in five active clinical oncology trials, as well as enable application to advanced tissue diffusivity models. These tools will eliminate systematic cross-platform, cross-exam variability to facilitate longitudinal and multi-institutional translational cancer research that utilize quantitative diffusivity metrics. Success of this project will further enhance accuracy and precision of cancer detection and monitoring. These goals will be achieved through Aim1: deployment of DWI bias correction tools for application in multiple cancer imaging trials, and through Aim2: correction integration with advanced DWI protocols and tissue models. Academic team of the proposed partnership consists of recognized experts in quantitative diffusion imaging standardization and translation to clinical oncology trials. The PI institution has active research agreements with three dominant clinical MRI manufactures with prior record of successful implementations for the developed technologies. Accomplishment of the project goals will eliminate significant instrumental bias that confounds current multi-center/multi-platform clinical trials that employ quantitative DWI.

摘要

项目成果

Characterization of soft tissue masses: can quantitative diffusion weighted imaging reliably distinguish cysts from solid masses?

• DOI: 10.1007/s00256-013-1703-7
• 发表时间: 2013-11-01
• 期刊: SKELETAL RADIOLOGY
• 影响因子: 2.1
• 作者: Subhawong, Ty K.;Durand, Daniel J.;Fayad, Laura M.
• 通讯作者: Fayad, Laura M.

Distortion correction in diffusion-weighted imaging of the breast: Performance assessment of prospective, retrospective, and combined (prospective + retrospective) approaches.

• DOI: 10.1002/mrm.26328
• 发表时间: 2017-07
• 期刊: Magnetic resonance in medicine
• 影响因子: 3.3
• 作者: Hancu I;Lee SK;Hulsey K;Lenkinski R;Holland D;Sperl JI;Tan ET
• 通讯作者: Tan ET

Multiparametric Whole-body MRI with Diffusion-weighted Imaging and ADC Mapping for the Identification of Visceral and Osseous Metastases From Solid Tumors.

• DOI: 10.1016/j.acra.2018.02.010
• 发表时间: 2018-11
• 期刊: Academic radiology
• 影响因子: 4.8
• 作者: Jacobs MA;Macura KJ;Zaheer A;Antonarakis ES;Stearns V;Wolff AC;Feiweier T;Kamel IR;Wahl RL;Pan L
• 通讯作者: Pan L

Empirical validation of gradient field models for an accurate ADC measured on clinical 3T MR systems in body oncologic applications.

• DOI: 10.1016/j.ejmp.2021.05.030
• 发表时间: 2021-06
• 期刊: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
• 作者: Pang Y;Malyarenko DI;Amouzandeh G;Barberi E;Cole M;Vom Endt A;Peeters J;Tan ET;Chenevert TL
• 通讯作者: Chenevert TL

Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics.

• DOI: 10.1016/j.ijrobp.2018.05.041
• 发表时间: 2018-11-15
• 期刊: International journal of radiation oncology, biology, physics
• 作者: Peng L;Parekh V;Huang P;Lin DD;Sheikh K;Baker B;Kirschbaum T;Silvestri F;Son J;Robinson A;Huang E;Ames H;Grimm J;Chen L;Shen C;Soike M;McTyre E;Redmond K;Lim M;Lee J;Jacobs MA;Kleinberg L
• 通讯作者: Kleinberg L