Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images
Purpose: This study aimed to investigate the performance of multiparametric magnetic resonance imaging (mpMRI) radiomic feature-based machine learning (ML) models in classifying the Gleason grade group (GG) of prostate cancer. Methods: In this retrospective study, a total of 203 patients with histop...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2024-12-01
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| Series: | Journal of Medical Signals and Sensors |
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| Online Access: | https://journals.lww.com/10.4103/jmss.jmss_47_23 |
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| _version_ | 1841545090770665472 |
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| author | Fatemeh Zandie Mohammad Salehi Asghar Maziar Mohammad Reza Bayatiani Reza Paydar |
| author_facet | Fatemeh Zandie Mohammad Salehi Asghar Maziar Mohammad Reza Bayatiani Reza Paydar |
| author_sort | Fatemeh Zandie |
| collection | DOAJ |
| description | Purpose:
This study aimed to investigate the performance of multiparametric magnetic resonance imaging (mpMRI) radiomic feature-based machine learning (ML) models in classifying the Gleason grade group (GG) of prostate cancer.
Methods:
In this retrospective study, a total of 203 patients with histopathologically confirmed prostate cancer who underwent mpMRI before prostate biopsy were included. After manual segmentation, radiomic features (RFs) were extracted from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted magnetic resonance imaging (DWMRI). Patients were split into training sets and testing sets according to a ratio of 8:2. A pipeline considering combinations of two feature selection (FS) methods and six ML classifiers was developed and evaluated. The performance of models was assessed using the accuracy, sensitivity, precision, F1-measure, and the area under curve (AUC).
Results:
On high b-value DWMRI-derived features, a combination of FS method recursive feature elimination (RFE) and classifier random forest achieved the highest performance for classification of prostate cancer into five GGs, with 97.0% accuracy, 98.0% sensitivity, 98.0% precision, and 97.0% F1-measure. The method also achieved an average AUC for GG of 98%.
Conclusion:
Preoperative mpMRI radiomic analysis based on ML, as a noninvasive approach, showed good performance for classification of prostate cancer into five GGs.
Advances in Knowledge:
Herein, radiomic models based on preoperative mpMRI and ML were developed to classify prostate cancer into 5 GGs. Our study provides evidence that analysis of quantitative RFs extracted from high b-value DWMRI images based on a combination of FS method RFE and classifier random forest can be applied for multiclass grading of prostate cancer with an accuracy of 97.0%. |
| format | Article |
| id | doaj-art-ee185eab53df445aa4d6fb7bdf175000 |
| institution | Kabale University |
| issn | 2228-7477 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Medical Signals and Sensors |
| spelling | doaj-art-ee185eab53df445aa4d6fb7bdf1750002025-01-12T10:51:47ZengWolters Kluwer Medknow PublicationsJournal of Medical Signals and Sensors2228-74772024-12-011412333310.4103/jmss.jmss_47_23Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI ImagesFatemeh ZandieMohammad SalehiAsghar MaziarMohammad Reza BayatianiReza PaydarPurpose: This study aimed to investigate the performance of multiparametric magnetic resonance imaging (mpMRI) radiomic feature-based machine learning (ML) models in classifying the Gleason grade group (GG) of prostate cancer. Methods: In this retrospective study, a total of 203 patients with histopathologically confirmed prostate cancer who underwent mpMRI before prostate biopsy were included. After manual segmentation, radiomic features (RFs) were extracted from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted magnetic resonance imaging (DWMRI). Patients were split into training sets and testing sets according to a ratio of 8:2. A pipeline considering combinations of two feature selection (FS) methods and six ML classifiers was developed and evaluated. The performance of models was assessed using the accuracy, sensitivity, precision, F1-measure, and the area under curve (AUC). Results: On high b-value DWMRI-derived features, a combination of FS method recursive feature elimination (RFE) and classifier random forest achieved the highest performance for classification of prostate cancer into five GGs, with 97.0% accuracy, 98.0% sensitivity, 98.0% precision, and 97.0% F1-measure. The method also achieved an average AUC for GG of 98%. Conclusion: Preoperative mpMRI radiomic analysis based on ML, as a noninvasive approach, showed good performance for classification of prostate cancer into five GGs. Advances in Knowledge: Herein, radiomic models based on preoperative mpMRI and ML were developed to classify prostate cancer into 5 GGs. Our study provides evidence that analysis of quantitative RFs extracted from high b-value DWMRI images based on a combination of FS method RFE and classifier random forest can be applied for multiclass grading of prostate cancer with an accuracy of 97.0%.https://journals.lww.com/10.4103/jmss.jmss_47_23gleason gradingmachine learningmultiparametric magnetic resonance imagingprostate cancerradiomics |
| spellingShingle | Fatemeh Zandie Mohammad Salehi Asghar Maziar Mohammad Reza Bayatiani Reza Paydar Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images Journal of Medical Signals and Sensors gleason grading machine learning multiparametric magnetic resonance imaging prostate cancer radiomics |
| title | Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images |
| title_full | Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images |
| title_fullStr | Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images |
| title_full_unstemmed | Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images |
| title_short | Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images |
| title_sort | radiomics based machine learning models for classification of prostate cancer grade groups from multi parametric mri images |
| topic | gleason grading machine learning multiparametric magnetic resonance imaging prostate cancer radiomics |
| url | https://journals.lww.com/10.4103/jmss.jmss_47_23 |
| work_keys_str_mv | AT fatemehzandie radiomicsbasedmachinelearningmodelsforclassificationofprostatecancergradegroupsfrommultiparametricmriimages AT mohammadsalehi radiomicsbasedmachinelearningmodelsforclassificationofprostatecancergradegroupsfrommultiparametricmriimages AT asgharmaziar radiomicsbasedmachinelearningmodelsforclassificationofprostatecancergradegroupsfrommultiparametricmriimages AT mohammadrezabayatiani radiomicsbasedmachinelearningmodelsforclassificationofprostatecancergradegroupsfrommultiparametricmriimages AT rezapaydar radiomicsbasedmachinelearningmodelsforclassificationofprostatecancergradegroupsfrommultiparametricmriimages |