Quantitative Advanced Diffusion MRI for Reliable Detection and Enhanced Treatment of High-Grade Prostate Cancer
Abstract
Until recently, most prostate biopsies and prostate cancer treatment were mostly blind. Because the tumors were difficult to see on available imaging, if there was suspicion of prostate cancer based on blood tests or physical exam, a prostate biopsy would be performed by inserting 10-12 needles into various parts of the prostate without trying to target the tumor (unfortunately, this technique can miss significant cancers, especially in certain parts of the prostate). Similarly, when prostate cancer was diagnosed, either the whole prostate was removed surgically or the whole prostate was treated uniformly with radiation therapy. Recent large clinical trials have shown that there is a better way, using magnetic resonance imaging (MRI). Many men with a normal MRI can safely avoid biopsy (and the associated risks, such as rare but serious infection and an anxiety-provoking diagnosis of cancer that was never threatening in the first place). When the MRI shows a suspicious lesion, biopsy needles can be inserted into the lesion so that aggressive cancer is not missed. If an aggressive cancer is diagnosed, MRI makes it possible for doctors to target the tumor (instead of the whole gland). In a large clinical trial, when radiation doctors (radiation oncologists) used MRI to focus a higher radiation dose on the tumor, patients were less likely to have their cancer come back. (Another emerging approach is for doctors to offer focal therapy to only the tumor instead of the whole gland). All these benefits depend on high-quality MRI. Unfortunately, the quality of conventional prostate MRI is notoriously dependent on which imaging center the patient visits and which radiologist looks at the images. Targeted therapy too depends on the availability of high-quality MRI and training doctors to know how to use the images. The current reality is that MRI technology greatly improves detection and treatment of prostate cancer—but only for men who get their medical care at elite centers. We have developed a new MRI technique called Restriction Spectrum Imaging (RSI) that is better than conventional MRI at highlighting prostate cancer and distinguishing it from normal prostate or even low-grade prostate cancer that is not a threat to the patient. Dr. Seibert has led recent efforts to make quantitative maps of the prostate with RSI and to assign a score, the RSI restriction score, for each prostate MRI. The RSI restriction score can be computed from only 2-3 minutes of MRI scan time. In data from 151 patients at UC San Diego, we found that the RSI restriction score was as accurate at detecting aggressive prostate cancer as expert radiologists who used conventional prostate MRI images acquired in 30-40 minutes of scan time. When we combined the expert radiologists’ conventional scores and the RSI restriction score, performance was even better than either alone. We propose here to test the RSI restriction score in data from five institutions in the U.S. (UC San Diego, Harvard, UT San Antonio, Rochester) and two in the UK (Oxford and Cambridge) to see whether it is similarly accurate in these broader datasets. We will also measure differences in RSI restriction score across scanner types, hospitals, and racial/ethnic groups. Our team of engineers and physicians will adjust the score as necessary to optimize the score across different imaging facilities and populations. The advanced MRI score tested and further developed in this project will help patients with suspected prostate cancer and those with aggressive prostate cancer requiring treatment. As RSI can be performed on existing scanners—and as we have worked closely with major scanner vendors (GE, Siemens) to implement RSI on their machines—the technique could be rolled out broadly within a couple of years of completion of this study. During this study, we will design a prospective clinical trial to test the clinical impact of the RSI score for prostate cancer detectio
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 04, 2024
- Source ID
- HT94252310085
Entities
People
- Tyler M Seibert
Organizations
- United States Army
- University of California, San Diego