Prostate Gleason Score Detection by Calibrated Machine Learning Classification through Radiomic Features
The Gleason score was originally formulated to represent the heterogeneity of prostate cancer and helps to stratify the risk of patients affected by this tumor. The Gleason score assigning represents an on H&E stain task performed by pathologists upon histopathological examination of needle biop...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/23/11900 |
| _version_ | 1797463689467527168 |
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| author | Francesco Mercaldo Maria Chiara Brunese Francesco Merolla Aldo Rocca Marcello Zappia Antonella Santone |
| author_facet | Francesco Mercaldo Maria Chiara Brunese Francesco Merolla Aldo Rocca Marcello Zappia Antonella Santone |
| author_sort | Francesco Mercaldo |
| collection | DOAJ |
| description | The Gleason score was originally formulated to represent the heterogeneity of prostate cancer and helps to stratify the risk of patients affected by this tumor. The Gleason score assigning represents an on H&E stain task performed by pathologists upon histopathological examination of needle biopsies or surgical specimens. In this paper, we propose an approach focused on the automatic Gleason score classification. We exploit a set of 18 radiomic features. The radiomic feature set is directly obtainable from segmented magnetic resonance images. We build several models considering supervised machine learning techniques, obtaining with the RandomForest classification algorithm a precision ranging from 0.803 to 0.888 and a recall from to 0.873 to 0.899. Moreover, with the aim to increase the never seen instance detection, we exploit the sigmoid calibration to better tune the built model. |
| first_indexed | 2024-03-09T17:54:18Z |
| format | Article |
| id | doaj.art-872b7a7c49354915bf692a9ebbb73360 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:54:18Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-872b7a7c49354915bf692a9ebbb733602023-11-24T10:27:42ZengMDPI AGApplied Sciences2076-34172022-11-0112231190010.3390/app122311900Prostate Gleason Score Detection by Calibrated Machine Learning Classification through Radiomic FeaturesFrancesco Mercaldo0Maria Chiara Brunese1Francesco Merolla2Aldo Rocca3Marcello Zappia4Antonella Santone5Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyDepartment of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, ItalyThe Gleason score was originally formulated to represent the heterogeneity of prostate cancer and helps to stratify the risk of patients affected by this tumor. The Gleason score assigning represents an on H&E stain task performed by pathologists upon histopathological examination of needle biopsies or surgical specimens. In this paper, we propose an approach focused on the automatic Gleason score classification. We exploit a set of 18 radiomic features. The radiomic feature set is directly obtainable from segmented magnetic resonance images. We build several models considering supervised machine learning techniques, obtaining with the RandomForest classification algorithm a precision ranging from 0.803 to 0.888 and a recall from to 0.873 to 0.899. Moreover, with the aim to increase the never seen instance detection, we exploit the sigmoid calibration to better tune the built model.https://www.mdpi.com/2076-3417/12/23/11900prostatecancerradiomicsmachine learningclassification |
| spellingShingle | Francesco Mercaldo Maria Chiara Brunese Francesco Merolla Aldo Rocca Marcello Zappia Antonella Santone Prostate Gleason Score Detection by Calibrated Machine Learning Classification through Radiomic Features Applied Sciences prostate cancer radiomics machine learning classification |
| title | Prostate Gleason Score Detection by Calibrated Machine Learning Classification through Radiomic Features |
| title_full | Prostate Gleason Score Detection by Calibrated Machine Learning Classification through Radiomic Features |
| title_fullStr | Prostate Gleason Score Detection by Calibrated Machine Learning Classification through Radiomic Features |
| title_full_unstemmed | Prostate Gleason Score Detection by Calibrated Machine Learning Classification through Radiomic Features |
| title_short | Prostate Gleason Score Detection by Calibrated Machine Learning Classification through Radiomic Features |
| title_sort | prostate gleason score detection by calibrated machine learning classification through radiomic features |
| topic | prostate cancer radiomics machine learning classification |
| url | https://www.mdpi.com/2076-3417/12/23/11900 |
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