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May Radiomic Data Predict Prostate Cancer Aggressiveness?

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Computer Analysis of Images and Patterns (CAIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1089))

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Abstract

Radiomics can quantify tumor phenotypic characteristics non-invasively by defining a signature correlated with biological information. Thanks to algorithms derived from computer vision to extract features from images, and machine learning methods to mine data, Radiomics is the perfect case study of application of Artificial Intelligence in the context of precision medicine. In this study we investigated the association between radiomic features extracted from multi-parametric magnetic resonance imaging (mp-MRI)of prostate cancer (PCa) and the tumor histologic subtypes (using Gleason Score) using machine learning algorithms, in order to identify which of the mp-MRI derived radiomic features can distinguish high and low risk PCa.

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Notes

  1. 1.

    The Gleason grading system is used to help evaluate the prognosis of men with prostate cancer using samples from a prostate biopsy. The pathologist looks at how the cancer cells are arranged in the prostate and assigns a score on a scale of 3 to 5 from 2 different locations. Please note the notation: the first number is the most common grade in all the samples, while the second number is the highest grade of what’s left. Gleason Score = the most common grade + the highest other grade in the samples.

  2. 2.

    The PI-RADS v2 [25] (Prostate Imaging Reporting & Data System) assessment categories are based on the findings of mp-MRI, combining T2-weighted (T2W), diffusion weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging. The PI-RADS assessment category determines the likelihood of clinically significant prostate cancer. A score, ranging from 1 to 5, is given accordingly to each imaging technique, with 1 being most probably benign (clinically significant cancer is highly unlikely to be present) and 5 being high suspicious for malignancy.

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Authors and Affiliations

  1. Azienda Ospedaliero Universitaria Careggi, Largo G. Brambilla 3, 50134, Florence, Italy

    Simone Agostini, Elena Bertelli, Laura Mercatelli & Vittorio Miele

  2. “Nello Carrara” Institute of Applied Physics, IFAC-CNR, via Madonna del Piano 10, 50019, Florence, Italy

    Andrea Barucci & Nicola Zoppetti

  3. Azienda USL Toscana Centro, Piazza Santa Maria Nuova 1, 50019, Florence, Italy

    Roberto Carpi

  4. ISTI-CNR, Pisa, Italy

    Danila Germanese, Sara Colantonio & Maria Antonietta Pascali

  5. ITB-CNR, Pisa, Italy

    Claudia Caudai

Authors
  1. Danila Germanese
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  2. Sara Colantonio
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  3. Claudia Caudai
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  4. Maria Antonietta Pascali
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  5. Andrea Barucci
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  6. Nicola Zoppetti
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  7. Simone Agostini
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  8. Elena Bertelli
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  9. Laura Mercatelli
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  10. Vittorio Miele
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  11. Roberto Carpi
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Corresponding author

Correspondence to Danila Germanese .

Editor information

Editors and Affiliations

  1. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Salerno, Italy

    Mario Vento

  2. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Salerno, Italy

    Gennaro Percannella

  3. ISTI-CNR, Pisa, Italy

    Sara Colantonio

  4. ISTI-CNR, Pisa, Italy

    Daniela Giorgi

  5. University of Central Lancashire, Preston, UK

    Bogdan J. Matuszewski

  6. Kingston University, London, UK

    Hamideh Kerdegari

  7. Kingston University, London, UK

    Manzoor Razaak

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Germanese, D. et al. (2019). May Radiomic Data Predict Prostate Cancer Aggressiveness?. In: Vento, M., et al. Computer Analysis of Images and Patterns. CAIP 2019. Communications in Computer and Information Science, vol 1089. Springer, Cham. https://doi.org/10.1007/978-3-030-29930-9_7

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  • DOI: https://doi.org/10.1007/978-3-030-29930-9_7

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