Raisa Z Freidlin1, Harsh K Agarwal2, Sandeep Sankineni3, Anna M Brown4, Francesca Mertan3, Marcelino Bernardo5, Dagane Daar5, Maria Merino6, Deborah Citrin7, Bradford J Wood8, Peter A Pinto9, Peter L Choyke3, Baris Turkbey3. 1. Division of Computational Bioscience, CIT, NIH, Bethesda, MD, USA. Electronic address: raisa@mail.nih.gov. 2. Molecular Imaging Program, NCI, NIH, Bethesda, MD, USA; Philips Research North America, Briarcliff Manor NY, USA. 3. Molecular Imaging Program, NCI, NIH, Bethesda, MD, USA. 4. Molecular Imaging Program, NCI, NIH, Bethesda, MD, USA; Duke University School of Medicine, Durham, NC, USA. 5. Molecular Imaging Program, NCI, NIH, Bethesda, MD, USA; Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, MD, USA. 6. Laboratory of Pathology, NCI, NIH, Bethesda, MD, USA. 7. Radiation Oncology Branch, NCI, NIH, Bethesda, MD, USA. 8. Center for Interventional Oncology, NCI and Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA. 9. Urologic Oncology Branch, NCI, NIH, Bethesda, MD, USA.
Abstract
PURPOSE: The aim of this proof-of-concept work is to propose an unsupervised framework that combines multiple parameters, in "positive-if-all-positive" manner, from different models to localize tumors. METHODS: A voxel-by-voxel analysis of the DW-MRI images of whole prostate was performed to obtain parametric maps for D*, D, f, and K using the IVIM and kurtosis models. Ten patients with moderate or high-risk prostate cancer were included in study. The mean age and serum PSA for these 10 patients were 65years (range 54-78) and 21.9ng/mL (range 4.84-44.81), respectively. These patients were scanned using a DW spin-echo sequence with echo-planar readout with 16 equidistantly spaced b-values in the range of 0-2000s/mm2 (TE=58ms; TR=3990ms; spatial resolution 2.19×2.19×2.73mm3, slices =26, FOV=140×140mm, slice gap =0.27mm, NSA=2). RESULTS: The proposed framework detected 24 lesions of which 14 were true positive with 58% tumor detection rate on lesion-based analysis with sensitivity of 100%. The mpMRI evaluation (PIRADSv2) identified 12 of 14 true positive lesions with sensitivity of 86%; positive predictive value of mpMRI was 92%. The index lesions were visible on all framework maps and were coded as the most suspicious in 9 of 10 patients. CONCLUSION: Preliminary results of the proposed framework indicate high patient-based sensitivity with 100% detection rate for identifying moderate-high risk aggressive index lesions. Published by Elsevier Inc.
PURPOSE: The aim of this proof-of-concept work is to propose an unsupervised framework that combines multiple parameters, in "positive-if-all-positive" manner, from different models to localize tumors. METHODS: A voxel-by-voxel analysis of the DW-MRI images of whole prostate was performed to obtain parametric maps for D*, D, f, and K using the IVIM and kurtosis models. Ten patients with moderate or high-risk prostate cancer were included in study. The mean age and serum PSA for these 10 patients were 65years (range 54-78) and 21.9ng/mL (range 4.84-44.81), respectively. These patients were scanned using a DW spin-echo sequence with echo-planar readout with 16 equidistantly spaced b-values in the range of 0-2000s/mm2 (TE=58ms; TR=3990ms; spatial resolution 2.19×2.19×2.73mm3, slices =26, FOV=140×140mm, slice gap =0.27mm, NSA=2). RESULTS: The proposed framework detected 24 lesions of which 14 were true positive with 58% tumor detection rate on lesion-based analysis with sensitivity of 100%. The mpMRI evaluation (PIRADSv2) identified 12 of 14 true positive lesions with sensitivity of 86%; positive predictive value of mpMRI was 92%. The index lesions were visible on all framework maps and were coded as the most suspicious in 9 of 10 patients. CONCLUSION: Preliminary results of the proposed framework indicate high patient-based sensitivity with 100% detection rate for identifying moderate-high risk aggressive index lesions. Published by Elsevier Inc.
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