Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images.

@article{Fehr2015AutomaticCO,
  title={Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images.},
  author={Duc Fehr and Harini Veeraraghavan and Andreas G. Wibmer and Tatsuo Gondo and Kazuhiro Matsumoto and Herbert Alberto Vargas and Evis Sala and Hedvig Hricak and Joseph O. Deasy},
  journal={Proceedings of the National Academy of Sciences of the United States of America},
  year={2015},
  volume={112 46},
  pages={E6265-73}
}
Noninvasive, radiological image-based detection and stratification of Gleason patterns can impact clinical outcomes, treatment selection, and the determination of disease status at diagnosis without subjecting patients to surgical biopsies. We present machine learning-based automatic classification of prostate cancer aggressiveness by combining apparent diffusion coefficient (ADC) and T2-weighted (T2-w) MRI-based texture features. Our approach achieved reasonably accurate classification of… CONTINUE READING
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Haralick texture analysis of prostate MRI: Differentiating normal prostate from clinically significant prostate cancer and associations with cancer aggressiveness

  • A Wibmer
  • Eur Radiol 25(10):2840–2850
  • 2015

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