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3 January 2020 A Ktrans deep characterization to measure clinical significance regions on prostate cancer
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Proceedings Volume 11330, 15th International Symposium on Medical Information Processing and Analysis; 113300C (2020) https://doi.org/10.1117/12.2542606
Event: 15th International Symposium on Medical Information Processing and Analysis, 2019, Medelin, Colombia
Abstract
Magnetic resonance imaging (MRI) plays a valuable role in many task related with characterization of prostate cancer lesions. Recently, the DCE-MRI (Dynamic contrast Enhanced) has allowed to visualize and localize potential tumor regions. Specifically, Ktrans, from DCR-MRI, has shown to be a powerful pharmacokinetic parameter that allows to characterize tumor biology and to detect treatment responses from reconstructed coefficient maps of capillary permeability. Nevertheless, even expert-based analysis of Ktrans sequences are subject to a large false positive findings (FPF). In much of such cases, the prostate angiogenesis, or benign prostatic hyperplasia (BPH) regions are misclassified as cancer findings. This work introduces a robust deep convolutional strategy that characterizes Ktrans regions and allows an automatic prediction of cancer findings. The proposed strategy was validated over the SPIE-AAPM-NCI PROSTATEx public dataset with 320 multimodal images on peripheral, transitional and anterior fibromuscular stroma regions. The best configuration of proposal strategy achieved an area under the ROC curve (AUC) of 0.74. Additionally, the proposed strategy achieved a proper characterization by using mainly Ktrans information that together with T2-MRI-transaxial overcome baseline strategies that use additional modalities of MRI.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yesid Gutiérrez, John Arevalo, and Fabio Martínez "A Ktrans deep characterization to measure clinical significance regions on prostate cancer", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 113300C (3 January 2020); https://doi.org/10.1117/12.2542606
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