Abstract #0704

A Comparison between Radiologists versus Deep Learning for Prostate Cancer Detection in Multi-parameter MRI

Ruiming Cao¹, Xinran Zhong², Sohrab Afshari Mirak³, Ely Felker³, Voraparee Suvannarerg^3,4, Teeravut Tubtawee^3,5, Fabien Scalzo⁶, Steve Raman³, and Kyunghyun Sung³

¹Bioengineering, UC Berkeley, Berkeley, CA, United States, ²UT Southwestern, Dallas, TX, United States, ³Radiology, UCLA, Los Angeles, CA, United States, ⁴Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand, ⁵Radiology, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand, ⁶Computer Science, UCLA, Los Angeles, CA, United States

We evaluated our recently developed deep learning system, FocalNet, for prostate cancer detection in multi-parametric MRI (mpMRI). This study performed a head-to-head comparison between FocalNet and four genitourinary radiologists in an independent evaluation cohort consisting of 126 mpMRI scans untouched during the development. FocalNet demonstrated similar detection performance to radiologists under the high specificity condition or the high sensitivity condition, while radiologists outperformed FocalNet in moderate specificity and sensitivity.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.