A comprehensive non-invasive framework for diagnosing prostate cancer.

Early detection of prostate cancer increases chances of patients' survival. Our automated non-invasive system for computer-aided diagnosis (CAD) of prostate cancer segments the prostate on diffusion-weighted magnetic resonance images (DW-MRI) acquired at different b-values, estimates its apparent diffusion coefficients (ADC), and classifies their descriptors - empirical cumulative distribution functions (CDF) - with a trained deep learning network. To segment the prostate, an evolving geometric (level-set-based) deformable model is guided by a speed function depending on intensity attributes extracted from the DW-MRI with nonnegative matrix factorization (NMF). For a more robust evolution, the attributes are fused with a probabilistic shape prior and estimated spatial dependencies between prostate voxels. To preserve continuity, the ADCs of the segmented prostate volume at different b-values are normalized and refined using a generalized Gauss-Markov random field image model. The CDFs of the refined ADCs at different b-values are considered global water diffusion features and used to distinguish between benign and malignant prostates. A deep learning network of stacked non-negativity-constrained auto-encoders (SNCAE) is trained to classify the benign or malignant prostates on the basis of the constructed CDFs. Our experiments on 53 clinical DW-MRI data sets resulted in 92.3% accuracy, 83.3% sensitivity, and 100% specificity, indicating that the proposed CAD system could be used as a reliable non-invasive diagnostic tool.

Computers in biology and medicine. 2016 Dec 23 [Epub ahead of print]

Islam Reda, Ahmed Shalaby, Mohammed Elmogy, Ahmed Abou Elfotouh, Fahmi Khalifa, Mohamed Abou El-Ghar, Ehsan Hosseini-Asl, Georgy Gimel'farb, Naoufel Werghi, Ayman El-Baz

Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt; Bioengineering Department, University of Louisville, Louisville KY 40292, USA., Bioengineering Department, University of Louisville, Louisville KY 40292, USA., Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt., Bioengineering Department, University of Louisville, Louisville KY 40292, USA; Electronics and Communication Engineering Department, Mansoura University, Mansoura, Egypt., Radiology Department, Urology and Nephrology Center, University of Mansoura, Egypt., Electrical and Computer Engineering, University of Louisville, Louisville KY 40292, USA., Department of Computer Science, University of Auckland, Auckland, New Zealand., Khalifa University of Science Technology and Research, Abu Dhabi, UAE., Bioengineering Department, University of Louisville, Louisville KY 40292, USA. Electronic address: .

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