Abstract #3340

Retrospectively quantified T2 detects prostate cancer progression in patients undergoing active surveillance

Haoran Sun^1,2, Lixia Wang¹, Timothy Daskivich³, Shihan Qiu^1,2, Fei Han¹, Alessandro D'Agnolo⁴, Rola Saouaf⁵, Eric Lo⁶, Anthony G. Christodoulou², Hyung Kim³, Debiao Li¹, and Yibin Xie¹

¹Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ²Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ³Minimal Invasive Urology, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ⁴Imaging/Nuclear Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ⁵Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ⁶Urology, Cedars-Sinai Medical Center, Los Angeles, CA, United States

Synopsis

Keywords: Prostate, Quantitative Imaging, Active Surveillance, Prostate Cancer, T2 Mapping

Motivation: Multiparametric MRI as a widespread tool for AS management has limitations of diagnostic dilemma and inconsistency in identifying pathologic reclassification.

Goal(s): To further investigate the added value of estimated T2 maps generated by deep learning network on AS.

Approach: Retrospectively estimated T2 maps from T1WI and T2WI using a trained deep learning network. Quantitative analysis was performed on the same lesion ROIs of the estimated T2 maps on baseline and follow-up for progression differentiations.

Results: The estimated T2 is consistent with the intensity level of the prostate tumor. T-test results verified the significant difference of the mean T2 values between processor and non-progressor.

Impact: The estimated T2 information derived from standard clinical MRI has the potential for more accurate PCa progression detection.

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