Prostate cancer affects 1 in 6 men in USA. Despite the technological advances, prostate radiotherapy still results in poor local control and survival for patients with high-risk prostate cancer ? over 50% of the patients with high-risk disease will experience relapses following prostate radiotherapy. Histopathologic studies showed that dominant cancer foci within the prostate are associated with sites of local recurrence post radiotherapy. It has been proven that the state-of-the-art multiparametric MRI (mp-MRI) combining anatomic imaging with functional MRI techniques can provide the best performance to define dominant intraprostatic lesion (DIL). High-dose-rate (HDR) brachytherapy, with precise radiation-dose delivery, radiobiological advantage and lower cost, has become an increasing popular treatment modality for patients with prostate cancer. A few studies in HDR brachytherapy have shown that radiotherapy boost for a DIL with whole prostate treatment can improve local tumor control without causing excess toxicity. However, due to the anatomic location and size of a DIL, approximate 60% patients could not achieve the expected DIL boost dose coverage without increasing organ- at-risk (OAR) tolerances in current HDR boost practice. We hypothesize that DIL-targeted HDR catheter placement will significantly improve achievable boost dose coverage for DILs in focal boost HDR brachytherapy. The proposed research is to develop a DIL-targeted, US-guided HDR prostate brachytherapy with focal boost. Specifically, we propose to develop a novel MRI-US-CT deformable registration, which can incorporate mp-MRI-defined DIL into real-time US to guide HDR catheters placement to improve the probability of achievable optimal dose boost, and panning CT to guide focal DIL boost dose delivery in HDR treatment. This overall project is built on three integral components: 1) the PI?s award-winning prostate segmentation and registration technologies, 2) the DIL information provided by mp-MRI, and 3) a precise radiation delivery system offered by the HDR brachytherapy technique. The potential benefit of introducing DIL-targeted HDR boost brachytherapy that can irradiate the whole prostate while simultaneously and focally boost a higher dose to mp-MRI-defined DILs is enormous. In this project, we will 1) develop and optimize prostate segmentation and registration algorithms, 2) quantitatively evaluate the consistency and accuracy of these algorithms using phantom and patient data, and 3) evaluate the performance of the proposed DIL-targeted boost HDR with conventional HDR brachytherapy and examine its clinical benefit. Successful completion of this project will overcome a critical barrier to advance personalized HDR brachytherapy for prostate cancer, thus providing a way to focally boost the radiation dose to the DILs while sparing the normal tissue to improve local control, long-term survival and quality of life for prostate-cancer patients, particularly those with advanced prostate cancer.

Public Health Relevance

/Relevance The proposed project will develop a personalized, tumor-targeted HDR prostate brachytherapy with focal DIL boost using novel imaging segmentation and registration algorithms. Successful completion of this project will result in an important improvement in image-guided prostate HDR brachytherapy with DIL boost, and provides a powerful way to irradiate the whole prostate while simultaneously boost higher radiation to the dominant cancerous regions. The ultimate goal is to change the current clinical practice for prostate HDR brachytherapy and improve local control, long-term survival and quality of life of patients, particularly those with locally advanced prostate cancer.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Radiation Therapeutics and Biology Study Section (RTB)
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Obcemea, Ceferino H
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Emory University
Schools of Medicine
United States
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Lei, Yang; Shu, Hui-Kuo; Tian, Sibo et al. (2018) Magnetic resonance imaging-based pseudo computed tomography using anatomic signature and joint dictionary learning. J Med Imaging (Bellingham) 5:034001