Improving assessment of prostate cancer bone metastases using advanced diffusion imaging

使用先进的扩散成像改进前列腺癌骨转移的评估

• 批准号: 10432040
• 负责人: Tyler Michael Seibert
• 金额: $ 20.13万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10432040
• 关键词: Address; Affect; Aftercare; Agreement; Anatomy; Biomedical Engineering; Caliber; Cancer Control; Cancer Etiology; Cessation of life; Classification; Clinical; Clinical Trials; Custom; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Engineering; Environment; Epithelial; Evaluation; Goals; Guidelines; Human Resources; Hybrids; Image; Individual; Individual Adjustment; Institution; International; Lesion; Location; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Measurement; Measures; Medicine; Mentors; Metastatic Neoplasm to the Bone; Metastatic Prostate Cancer; Modeling; Multimodal Imaging; Neoplasm Metastasis; Outcome; PET/CT scan; Pain; Participant; Patient Care; Patients; Phase; Physicians; Principal Investigator; Process; Prostate Cancer therapy; Protocols documentation; Quality of life; Radiation; Radiation Oncologist; Radiation Oncology; Radiation therapy; Reader; Research; Research Personnel; Research Priority; Resolution; Resources; Restriction Spectrum Imaging; Scientist; Screening for Prostate Cancer; Signal Transduction; System; Techniques; Testing; Tissues; Veterans; Work; anatomic imaging; biomedical imaging; bone; bone imaging; cancer imaging; career; career development; clinical care; clinical pain; clinical practice; clinical predictors; design; experience; image guided radiation therapy; imaging biomarker; imaging detection; imaging genetics; improved; indexing; individual patient; innovation; interest; men; multi-scale modeling; novel therapeutics; outcome prediction; palliation; personalized medicine; predict clinical outcome; professor; prognostic; prognostic value; prostate cancer metastasis; quantitative imaging; reduce symptoms; response; therapeutically effective; tool; treatment planning; treatment response; tumor

Project Summary Metastatic prostate cancer (PCa) is a debilitating and often deadly disease affecting tens of thousands of men in the U.S. and worldwide. Effective therapeutic options exist for symptom relief and control of the cancer, with others in the pipeline for testing. It is even thought that some patients might be cured if we could detect metastatic disease when it is limited to only a small number of locations. However, both treatment and testing of novel therapies require accurate imaging of the metastases. Unfortunately, most PCa metastases are in bones, where imaging quality is poor, and metastases are considered unmeasurable with conventional techniques. The broad goal of this project is to optimize and test innovative approaches to quantitative imaging, radiation targeting, and response assessment of PCa. The proposal centers on distortion correction and advanced diffusion MRI models, which are effective for detection of PCa lesions and have shown promising association with clinical outcomes. The first specific aim of the proposal is to implement distortion-corrected diffusion MRI into a radiotherapy treatment planning system and measure the impact of distortion correction on precise and accurate tumor delineation in bone metastases. The second aim is to evaluate the prognostic value of diffusion MRI metrics as predictors of clinical outcomes after radiotherapy for bone metastases, a treatment with evidence of a survival benefit in some oligometastatic patients and important for quality of life in patients with painful bone lesions. The third aim is to evaluate the reliability of diffusion MRI for quantitative treatment response assessment of individual bone metastases, an unmet need for clinical practice and clinical trials. Dr. Seibert, assistant professor of Radiation Medicine and Bioengineering, seeks to advance patient care with this proposal and to improve the ability of other investigators to more efficiently test experimental treatments for PCa. Simultaneously, Dr. Seibert will gain mentored experience as a principal investigator and build on his engineering, clinical, and imaging backgrounds with specific expertise in the development of quantitative tools for PCa assessment. UC San Diego and the Center for Multimodal Imaging and Genetics— directed by K08 proposal mentor, Dr. Dale—provide the ideal environment for this project and career development, with a veteran team of mentors and an abundance of resources and skilled personnel to facilitate successful completion of the specific aims and to launch Dr. Seibert’s career as a physician-scientist in radiation oncology and biomedical imaging.

项目总结

项目成果

The effect of sample size on polygenic hazard models for prostate cancer.

• DOI: 10.1038/s41431-020-0664-2
• 发表时间: 2020-10
• 期刊: European journal of human genetics : EJHG
• 作者: Karunamuni RA;Huynh-Le MP;Fan CC;Eeles RA;Easton DF;Kote-Jarai Z;Amin Al Olama A;Benlloch Garcia S;Muir K;Gronberg H;Wiklund F;Aly M;Schleutker J;Sipeky C;Tammela TLJ;Nordestgaard BG;Key TJ;Travis RC;Neal DE;Donovan JL;Hamdy FC;Pharoah P;Pashayan N;Khaw KT;Thibodeau SN;McDonnell SK;Schaid DJ;Maier C;Vogel W;Luedeke M;Herkommer K;Kibel AS;Cybulski C;Wokolorczyk D;Kluzniak W;Cannon-Albright L;Brenner H;Schöttker B;Holleczek B;Park JY;Sellers TA;Lin HY;Slavov C;Kaneva R;Mitev V;Batra J;Clements JA;Spurdle A;Australian Prostate Cancer BioResource (APCB);Teixeira MR;Paulo P;Maia S;Pandha H;Michael A;Mills IG;Andreassen OA;Dale AM;Seibert TM;PRACTICAL Consortium
• 通讯作者: PRACTICAL Consortium

Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model.

• DOI: 10.1158/1078-0432.ccr-20-2017
• 发表时间: 2021-02-15
• 期刊: Clinical cancer research : an official journal of the American Association for Cancer Research
• 作者: Andreassen MMS;Rodríguez-Soto AE;Conlin CC;Vidić I;Seibert TM;Wallace AM;Zare S;Kuperman J;Abudu B;Ahn GS;Hahn M;Jerome NP;Østlie A;Bathen TF;Ojeda-Fournier H;Goa PE;Rakow-Penner R;Dale AM
• 通讯作者: Dale AM

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models.

• DOI: 10.1002/jmri.27393
• 发表时间: 2021-03
• 期刊: Journal of magnetic resonance imaging : JMRI
• 作者: Conlin CC;Feng CH;Rodriguez-Soto AE;Karunamuni RA;Kuperman JM;Holland D;Rakow-Penner R;Hahn ME;Seibert TM;Dale AM
• 通讯作者: Dale AM

Characterization of the diffusion signal of breast tissues using multi-exponential models.

• DOI: 10.1002/mrm.29090
• 发表时间: 2022-04
• 期刊: MAGNETIC RESONANCE IN MEDICINE
• 影响因子: 3.3
• 作者: Rodriguez-Soto, Ana E.;Andreassen, Maren M. Sjaastad;Fang, Lauren K.;Conlin, Christopher C.;Park, Helen H.;Ahn, Grace S.;Bartsch, Hauke;Kuperman, Joshua;Vidic, Igor;Ojeda-Fournier, Haydee;Wallace, Anne M.;Hahn, Michael;Seibert, Tyler M.;Jerome, Neil Peter;Ostlie, Agnes;Bathen, Tone Frost;Goa, Pal Erik;Rakow-Penner, Rebecca;Dale, Anders M.
• 通讯作者: Dale, Anders M.

Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four-Compartment Restriction Spectrum Imaging.

• DOI: 10.1002/jmri.27623
• 发表时间: 2021-09
• 期刊: Journal of magnetic resonance imaging : JMRI
• 作者: Feng CH;Conlin CC;Batra K;Rodríguez-Soto AE;Karunamuni R;Simon A;Kuperman J;Rakow-Penner R;Hahn ME;Dale AM;Seibert TM
• 通讯作者: Seibert TM