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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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