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Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol.

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Název: Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol.
Autoři: Gibała, Sebastian, Obuchowicz, Rafał, Lasek, Julia, Piórkowski, Adam, Nurzynska, Karolina
Zdroj: Applied Sciences (2076-3417); Sep2023, Vol. 13 Issue 17, p9871, 14p
Témata: TEXTURE analysis (Image processing), EARLY detection of cancer, MAGNETIC resonance imaging, DIAGNOSIS, PROSTATE cancer, PROSTATE
Abstrakt: Prostate cancer is one of the most common cancers in the world. Due to the ageing of society and the extended life of the population, early diagnosis is a great challenge for healthcare. Unfortunately, the currently available diagnostic methods, in which magnetic resonance imaging (MRI) using the PIRADS protocol plays an increasingly important role, are imperfect, mostly in the inability to visualise small cancer foci and misinterpretation of the imagery data. Therefore, there is a great need to improve the methods currently applied and look for even better ones for the early detection of prostate cancer. In the presented research, anonymised MRI scans of 92 patients with evaluation in the PIRADS protocol were selected from the data routinely scanned for prostate cancer. Suspicious tissues were depicted manually under medical supervision. The texture features in the marked regions were calculated using the qMaZda software. The multiple-instance learning approach based on the SVM classifier allowed recognising between healthy and ill prostate tissue. The best F1 score equal to 0.77 with a very high recall equal to 0.70 and precision equal to 0.85 was recorded for the texture features describing the central zone. The research showed that the use of texture analysis in prostate MRI may allow for automation of the assessment of PIRADS scores. [ABSTRACT FROM AUTHOR]
Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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IllustrationInfo
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  Label: Title
  Group: Ti
  Data: Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Gibała%2C+Sebastian%22">Gibała, Sebastian</searchLink><br /><searchLink fieldCode="AR" term="%22Obuchowicz%2C+Rafał%22">Obuchowicz, Rafał</searchLink><br /><searchLink fieldCode="AR" term="%22Lasek%2C+Julia%22">Lasek, Julia</searchLink><br /><searchLink fieldCode="AR" term="%22Piórkowski%2C+Adam%22">Piórkowski, Adam</searchLink><br /><searchLink fieldCode="AR" term="%22Nurzynska%2C+Karolina%22">Nurzynska, Karolina</searchLink>
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  Data: Applied Sciences (2076-3417); Sep2023, Vol. 13 Issue 17, p9871, 14p
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22TEXTURE+analysis+%28Image+processing%29%22">TEXTURE analysis (Image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22EARLY+detection+of+cancer%22">EARLY detection of cancer</searchLink><br /><searchLink fieldCode="DE" term="%22MAGNETIC+resonance+imaging%22">MAGNETIC resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22DIAGNOSIS%22">DIAGNOSIS</searchLink><br /><searchLink fieldCode="DE" term="%22PROSTATE+cancer%22">PROSTATE cancer</searchLink><br /><searchLink fieldCode="DE" term="%22PROSTATE%22">PROSTATE</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Prostate cancer is one of the most common cancers in the world. Due to the ageing of society and the extended life of the population, early diagnosis is a great challenge for healthcare. Unfortunately, the currently available diagnostic methods, in which magnetic resonance imaging (MRI) using the PIRADS protocol plays an increasingly important role, are imperfect, mostly in the inability to visualise small cancer foci and misinterpretation of the imagery data. Therefore, there is a great need to improve the methods currently applied and look for even better ones for the early detection of prostate cancer. In the presented research, anonymised MRI scans of 92 patients with evaluation in the PIRADS protocol were selected from the data routinely scanned for prostate cancer. Suspicious tissues were depicted manually under medical supervision. The texture features in the marked regions were calculated using the qMaZda software. The multiple-instance learning approach based on the SVM classifier allowed recognising between healthy and ill prostate tissue. The best F1 score equal to 0.77 with a very high recall equal to 0.70 and precision equal to 0.85 was recorded for the texture features describing the central zone. The research showed that the use of texture analysis in prostate MRI may allow for automation of the assessment of PIRADS scores. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.3390/app13179871
    Languages:
      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 9871
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      – SubjectFull: TEXTURE analysis (Image processing)
        Type: general
      – SubjectFull: EARLY detection of cancer
        Type: general
      – SubjectFull: MAGNETIC resonance imaging
        Type: general
      – SubjectFull: DIAGNOSIS
        Type: general
      – SubjectFull: PROSTATE cancer
        Type: general
      – SubjectFull: PROSTATE
        Type: general
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      – TitleFull: Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol.
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            NameFull: Gibała, Sebastian
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            – D: 01
              M: 09
              Text: Sep2023
              Type: published
              Y: 2023
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