Osher Mini Medical School: 'Prostate Health: Risk, Detection, and Optimizing Personalized Treatments'

Date

October 26, 201710/26/2017 7:00pm 10/26/2017 7:00pm Osher Mini Medical School: 'Prostate Health: Risk, Detection, and Optimizing Personalized Treatments'

Personalized Medicine Powered by Precision Imaging

Meet world-renowned experts from the UCSF Department of Radiology & Biomedical Imaging, who are using and developing new and innovative precision imaging tools to enhance diagnosis, improve disease monitoring, and optimize treatment in the individual patient. In close partnership with subspecialty physicians from other UCSF departments, radiologists are now able to use these tools to pinpoint and better treat disorders such as prostate cancer, breast cancer and degenerative spine and joint disease at earlier stages. This series of lectures will explain how emerging technologies – including specific artificial intelligence platforms – will rely upon imaging to dramatically improve accuracy, safety, and outcomes for patients in the very near future.

Learn more about this Osher Mini Medical School series

Register for this event

Co-Chairs:

William P. Dillon, M.D.
Professor and Executive Vice Chair
Department of Radiology & Biomedical Imaging

Christopher P. Hess, M.D., Ph.D.
Professor and Associate Chair
Department of Radiology & Biomedical Imaging

1391 America/Los_Angeles public

Type

Community

Time Duration

7:00pm - 8:30pm

Location

Enter at Medical Science Building | 513 Parnassus Ave. | School of Nursing Room: N225

Personalized Medicine Powered by Precision Imaging

Meet world-renowned experts from the UCSF Department of Radiology & Biomedical Imaging, who are using and developing new and innovative precision imaging tools to enhance diagnosis, improve disease monitoring, and optimize treatment in the individual patient. In close partnership with subspecialty physicians from other UCSF departments, radiologists are now able to use these tools to pinpoint and better treat disorders such as prostate cancer, breast cancer and degenerative spine and joint disease at earlier stages. This series of lectures will explain how emerging technologies – including specific artificial intelligence platforms – will rely upon imaging to dramatically improve accuracy, safety, and outcomes for patients in the very near future.

Learn more about this Osher Mini Medical School series

Register for this event

Co-Chairs:

William P. Dillon, M.D.
Professor and Executive Vice Chair
Department of Radiology & Biomedical Imaging

Christopher P. Hess, M.D., Ph.D.
Professor and Associate Chair
Department of Radiology & Biomedical Imaging

Speakers

Antionio Westphalen, MD, MAS, PhD
Associate Professor
Department of Radiology and Biomedical Imaging
University of California, San Francisco - School of Medicine
John Kurhanewicz, PhD
Professor
Department of Radiology and Biomedical Imaging
University of California, San Francisco - School of Medicine
Matthew Cooperberg, MD, PhD
Assistant Professor
University of California, San Francisco

Both clinician and patient decisions influence the choices about the type of treatment a patient will receive for localized prostate and kidney (renal) cancer. Matthew Cooperberg, MD, MPH is conducting an ongoing research program to study national prostate cancer management trends, based on data from CaPSURE and other sources. His analyses have looked at changes in cancer risk over time, testing and treatment for prostate cancer, local variation in treatment, and the impact of socio-demographic factors on type of treatment and outcomes. Through the creation of a San Francisco General Hospital (SFGH) prostate cancer patient registry, preliminary analysis show that low socioeconomic status patients are treated for a higher percentage of high-risk disease than patients with a higher socioeconomic status. Using data from CaPSURE, the NCDB, SFGH and in collaboration with the Urologic Diseases in America project he continues to explore these topic in depth.


Prostate Cancer Risk Assessment and Comparative Effectiveness Research

Properly treating prostate cancer requires determining how likely is it that the cancer will progress. Cooperberg led the team that developed the UCSF-CAPRA score, a prostate cancer risk assessment tool that has been validated in several multi-institutional studies in the U.S. and Europe. CAPRA predicts biochemical recurrence-free survival (PSA level does not rise) after radical prostatectomy with an accuracy at least as good as more mathematically complex nomograms that require complex tables or computer software to calculate and other risk assessment instruments. The score is easy to calculate, and can be used to predict an individual's likelihood of metastasis, cancer-specific mortality, and overall mortality after treatment by surgery, radiation therapy, or androgen deprivation therapy.

Cooperberg is currently developing a post-operative extension of the CAPRA score (CAPRA-S). After surgery additional information is available from the pathologist's analysis of the removed prostate. This information can help identify men who will benefit from additional therapy such as radiation and/or hormonal therapy after surgery. CAPRA-S will help in that decision making process. Cooperberg is also collaborating with a group of Japanese scientist to develop a prediction instrument specifically applicable to high-risk patients and patients receiving androgen deprivation therapy (J-CAPRA). As new biomarkers, such as genomic and advanced imaging data, are proven valid by UCSF Urology and collaborating laboratory investigators, Cooperberg plans to integrate the information into the current standard measures of risk and outcomes to improve risk assessment. These findings will help men determine with greater confidence whether active surveillance, surgery, radiation, hormonal therapy, or some combination may be most appropriate for them.

Because accurate risk assessment is essential to compare the effectiveness of different prostate cancer treatments, Cooperberg is currently conducting such comparison studies using CaPSURE data. These data will provide a unique source of insight for future comparative effectiveness research.


Survivorship

Cooperberg is collaborating on a project that will lead to better clinical care for cancer survivors. His efforts have helped to develop UCSF’s Urologic Oncology Database (UODB) into a comprehensive data repository for clinical information about patients treated for prostate, bladder, and renal cancers. With the Urology Department’s information experts Cooperberg is developing an automated process to further augment UODB by automatically transferring data from the UCSF Medical Center’s information system into UODB. In collaboration with UCSF Breast Oncology and a health care web services company Cooperberg is developing an electronic survey for cancer patients. Patients will complete a health history and health-related quality of life (HRQOL) survey prior to their first visit to the clinic and at defined intervals after treatment. This effort is expected to help patients and clinicians track HRQOL outcomes, such as urinary and sexual function, after treatment. The survey will help physicians to identify those patients who may need to be seen in clinic more or less frequently.


Small Renal Masses

In collaboration with the laboratory of John Kurhanewicz, PhD, Cooperberg is conducting a study to see if magnetic resonance spectroscopy (MRS) can be used to non-invasively diagnosis small renal tumors. MRS is a specialized technique associated with magnetic resonance imaging (MRI). MRS equipment can be used to pick up signals from different chemical nuclei within the body. A preliminary laboratory study is using tissue to identify the specific MRS signals associated with a variety of renal tumors. Once renal tumor signals have been identified, Cooperberg plans to test the ability of MRS to accurately analyze renal tumors in patients. This will be done by using MRS imaging technology on patients who are already scheduled for renal cancer surgery prior to their operation. The pre and post surgery information can be analyzed to determine if the information collected non-invasively by the MRS technology matches the histology and grade of the actual tumor.