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Abstract #1630

MR Fingerprinting and Diffusion Mapping based Neural Network Classifier for significant prostate cancer characterization in Peripheral Zone and Transition Zone

Kun Yang1, Ananya Panda2, Verena Carola Obmann3,4, Jesse Hamilton1, Katie Wright3, and Vikas Gulani1,3

1BME, Case Western Reserve University, Cleveland, OH, United States, 2Radiology, Mayo Clinic, Rochester, MN, United States, 3Radiology, Case Western Reserve University, Cleveland, OH, United States, 4Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern, Switzerland

This study demonstrates the utility of a neural network classifier in separating significant cancer from low-grade cancer and non-cancerous lesions, based on the quantitative MRF and diffusion mapping. Using targeted biopsy data for training, the neural network classifier outperforms the linear regression model in both peripheral zone (PZ) and transition zone (TZ). The differentiation results showed an AUC of 0.90 in PZ and AUC of 0.89 in the TZ, comparing to AUC of 0.86 and 0.81 using Logistic Regression respectively. After applying the adaptive data oversampling algorithm, the AUC in characterizing TZ lesions can reach 0.96. Further classification utilizing patient clinical information showed statistically better accuracy in PZ while worse in TZ.

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