Abstract #4890

Proving b1000 DWI has performance advantage to classify low and high risk Gleason groups by using Neural Network classifier

Hongtao Zhang¹, Bo Wang², Zeyu Hu³, Zhenjie Wu³, Jiamu Xiao³, Gang Wang³, Shulong Wang³, and Huiyi Ye¹

¹Department of Radiology, Chinese PLA General Hospital, Beijing, China, ²Tsinghua University, Beijing, China, ³Xidian University, Xi'an, China

The Gleason grading of histological samples is recommended for the assessment of prostate cancer risk. Assessing Gleason grade correctly can improve patient prognosis and implement early diagnosis. The aim of this work was to prove that b1000 DWI has the best effect on Gleason high-risk and low-risk grading in T2WI and DWIs with b=1000，b=2000, and b=3000. We use NN (Neural Network) with Ensemble Method on each sequence. The AUC of b1000 DWI was 0.8734, which is significantly higher than those observed for other DWIs.

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