ASCO GU 2017: Imaging To Guide Active Surveillance - Session Highlights

Orlando, Florida USA ( Multi parametric MRI of the prostate is an imaging modality that defines the anatomy of the prostate and adjacent organs. The sequences usually used are T2 (which allows for anatomic evaluation) with diffusion weighted sequences of MRI. Sometimes contrast enhancement may help define the anatomy and show lesions more clearly. An endorectal coil is not necessary in most cases.

The role of mpMRI in the evaluation of prostate cancer is still unclear. The PROMIS study is a RCT of 570 patients, diagnosed with prostate cancer by 12 core biopsy with or without MRI. The study showed that the addition of mpMRI increased the sensitivity of diagnosing gleason pattern 4 compared with biopsy alone. To date, mpMRI sensitivity and specificity for prostate cancer is 85-90% and 70-90% respectively. Several guidelines include mpMRI for patients starting on active surveillance for low risk prostate cancer. However, these patients may harbor higher grade disease that was not sampled by the biopsy. MRI can help to ascertain the biopsy targets the correct lesion. A recent trial on the use of mpMRI in 60 patients offered AS showed that in 22% the biopsy results were disconcordant with the MRI results. 77% of these patients were upgraded on repeat biopsy. Furthermore it was shown that mpMRI staging can predict adverse pathological results after radical prostatectomy. Lastly, MRI has been shown to reduce anxiety and increase confidence in patients on AS.

MRI can potentially serve as a marker of progression. The main issue is how to define progression on MRI. Repeat MRI can show changes in size or diffusion in suspected lesions. However, due to its high anatomic sensitivity, MRI can identify change that may not be related to progression. There are several ongoing effort to define MRI progression that will be available in the near future.

Future direction in MRI research include defining quantitative metrics such as volume measurements, apparent diffusion coefficient and dynamic contrast enhanced metrics that will predict outcomes and define progression. Machine learning algorithms that assist in identifying patterns in the MRI image may increase sensitivity and specificity. Finally, defining the trigger for clinically significant change is the holy grail of mpMRI research for AS.

Presented By: Shonit Punwani, BSc, PhD, MBBS, MRCP, FRCR, University College London

Written By: Miki Haifler, MD, M.Sc, Fox Chase Cancer Center

at the 2017 Genitourinary Cancers Symposium - February 16 - 18, 2017 – Orlando, Florida USA