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A comprehensive non-invasive framework for diagnosing prostate cancer.

Islam Reda1, Ahmed Shalaby2, Mohammed Elmogy1

  • 1Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt; Bioengineering Department, University of Louisville, Louisville KY 40292, USA.

Computers in Biology and Medicine
|January 8, 2017
PubMed
Summary

This study presents an automated system for diagnosing prostate cancer using diffusion-weighted MRI. The computer-aided diagnosis (CAD) tool accurately distinguishes between benign and malignant prostates, improving early detection chances.

Keywords:
CADDW-MRIMGRFNMFProstate cancer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Early prostate cancer detection significantly improves patient survival rates.
  • Non-invasive diagnostic tools are crucial for accurate and timely diagnosis.
  • Diffusion-weighted magnetic resonance imaging (DW-MRI) offers valuable insights into prostate tissue characteristics.

Purpose of the Study:

  • To develop and validate an automated, non-invasive computer-aided diagnosis (CAD) system for prostate cancer detection.
  • To segment the prostate gland from DW-MRI, estimate apparent diffusion coefficients (ADC), and classify diffusion features.
  • To utilize a deep learning network for distinguishing between benign and malignant prostate tissues.

Main Methods:

  • Prostate segmentation using a level-set-based deformable model guided by intensity attributes from DW-MRI and nonnegative matrix factorization (NMF).
  • ADC normalization and refinement using a generalized Gauss-Markov random field image model for continuity.
  • Classification of empirical cumulative distribution functions (CDFs) of refined ADCs using a stacked non-negativity-constrained auto-encoder (SNCAE) deep learning network.

Main Results:

  • The proposed CAD system achieved 92.3% accuracy, 83.3% sensitivity, and 100% specificity in experiments on 53 clinical DW-MRI datasets.
  • The system effectively segments the prostate, estimates ADC values, and classifies diffusion features for malignancy detection.
  • The deep learning approach demonstrated high performance in distinguishing benign from malignant prostates based on CDFs of ADC.

Conclusions:

  • The developed automated CAD system is a reliable non-invasive tool for prostate cancer diagnosis.
  • The integration of DW-MRI, advanced image processing, and deep learning shows significant potential for improving prostate cancer diagnostics.
  • The system's high accuracy and specificity suggest its utility in clinical settings for early and reliable detection.