Abstract #1048
Unsupervised multi-characterstic framework for DW-MRI prostate cancer localization
Raisa Z Freidlin 1 , Harsh K Agarwal 2 , Sandeep Sankineni 3 , Anna M Brown 3 , Marcelino Bernardo 3,4 , Peter A Pinto 3 , Bradford J Wood 3 , Deborah E Citrin 3 , Peter L Choyke 3 , and Baris Turkbey 3
1
NIH/CIT, Bethesda, Maryland, United States,
2
Philips
Research, New York, United States,
3
NIH/NCI,
Maryland, United States,
4
Leidos,
Maryland, United States
Existing studies using diffusion MRI models for prostate
cancer (PCa) detection have not used an unsupervised
approach. Our proof-of-concept study introduces a novel
unsupervised multi-characteristic framework for
localizing PCa. Our framework calculates voxel-based
parameters from the IVIM and kurtosis models and
identifies tumor and tumor suspicious voxels using
patient-specific thresholds. Ten patients with
moderate-high clinicopathological risk for PCa underwent
3T prostate MRI and subsequent biopsy. The index lesion
was identified in all patients (100% patient-based
detection rate). Of the 25 framework-identified lesions,
14 were true positives (56% lesion-based detection
rate). This novel framework shows promise for
identifying index PCa lesions.
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