Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions
The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomog...
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| Vydané v: | Magnetic resonance imaging Ročník 100; s. 64 - 72 |
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| Jazyk: | English |
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Elsevier Inc
01.07.2023
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| ISSN: | 0730-725X, 1873-5894, 1873-5894 |
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| Abstract | The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification.
20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (KTRANS), efflux rate constant (Kep), and extracellular volume ratio (Ve) from mpMR images, and SUVmean and SUVmax from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM).
SUVmax yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input.
ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy.
[Display omitted]
•Among 7 quantitative parameters, SUVmax correlated best with Gleason score (GS).•Machine learning yielded higher accuracies than using cut-points of parameters.•Quantitative parameters (QP) and radiomic features (RF) as inputs for GS prediction.•K-Nearest Neighbour yielded highest accuracy of 0.929 with QP and RF.•Combinations of parameters, risk factors and models affect classification accuracy. |
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| AbstractList | The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification.
20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (K
), efflux rate constant (K
), and extracellular volume ratio (V
) from mpMR images, and SUV
and SUV
from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM).
SUV
yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input.
ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy. The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification. 20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (KTRANS), efflux rate constant (Kep), and extracellular volume ratio (Ve) from mpMR images, and SUVmean and SUVmax from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM). SUVmax yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input. ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy. [Display omitted] •Among 7 quantitative parameters, SUVmax correlated best with Gleason score (GS).•Machine learning yielded higher accuracies than using cut-points of parameters.•Quantitative parameters (QP) and radiomic features (RF) as inputs for GS prediction.•K-Nearest Neighbour yielded highest accuracy of 0.929 with QP and RF.•Combinations of parameters, risk factors and models affect classification accuracy. The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification.INTRODUCTIONThe classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification.20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (KTRANS), efflux rate constant (Kep), and extracellular volume ratio (Ve) from mpMR images, and SUVmean and SUVmax from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM).METHODS20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (KTRANS), efflux rate constant (Kep), and extracellular volume ratio (Ve) from mpMR images, and SUVmean and SUVmax from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM).SUVmax yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input.RESULTSSUVmax yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input.ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy.CONCLUSIONSML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy. |
| Author | Conti, Maurizio Roy, Sharmili Singha, Arvind Kumar Eriksson, Lars Chiong, Edmund Ang, Bertrand Wei Leng Totman, John J. Reilhac, Anthonin Cheong, Dennis Lai Hong Robins, Edward G. Kok, Trina Schaefferkoetter, Josh Chua, Wynne Yuru Nai, Ying-Hwey Thamboo, Thomas Paulraj Stephenson, Mary C. Wang, Ziting |
| Author_xml | – sequence: 1 givenname: Ying-Hwey surname: Nai fullname: Nai, Ying-Hwey email: yinghweynai@yahoo.com organization: Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore – sequence: 2 givenname: Dennis Lai Hong surname: Cheong fullname: Cheong, Dennis Lai Hong organization: Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore – sequence: 3 givenname: Sharmili surname: Roy fullname: Roy, Sharmili organization: Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore – sequence: 4 givenname: Trina surname: Kok fullname: Kok, Trina organization: Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore – sequence: 5 givenname: Mary C. surname: Stephenson fullname: Stephenson, Mary C. organization: Centre for Translational MR, Yong Loo Lin School of Medicine, National University of Singapore, Singapore – sequence: 6 givenname: Josh surname: Schaefferkoetter fullname: Schaefferkoetter, Josh organization: Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore – sequence: 7 givenname: John J. surname: Totman fullname: Totman, John J. organization: Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore – sequence: 8 givenname: Maurizio surname: Conti fullname: Conti, Maurizio organization: Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN, USA – sequence: 9 givenname: Lars surname: Eriksson fullname: Eriksson, Lars organization: Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN, USA – sequence: 10 givenname: Edward G. surname: Robins fullname: Robins, Edward G. organization: Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore – sequence: 11 givenname: Ziting surname: Wang fullname: Wang, Ziting organization: Department of Urology, National University Hospital, Singapore – sequence: 12 givenname: Wynne Yuru surname: Chua fullname: Chua, Wynne Yuru organization: Department of Diagnostic Imaging, National University Hospital, Singapore – sequence: 13 givenname: Bertrand Wei Leng surname: Ang fullname: Ang, Bertrand Wei Leng organization: Department of Diagnostic Imaging, National University Hospital, Singapore – sequence: 14 givenname: Arvind Kumar surname: Singha fullname: Singha, Arvind Kumar organization: Department of Diagnostic Imaging, National University Hospital, Singapore – sequence: 15 givenname: Thomas Paulraj surname: Thamboo fullname: Thamboo, Thomas Paulraj organization: Department of Pathology, National University Health System, Singapore – sequence: 16 givenname: Edmund surname: Chiong fullname: Chiong, Edmund organization: Department of Diagnostic Imaging, National University Hospital, Singapore – sequence: 17 givenname: Anthonin surname: Reilhac fullname: Reilhac, Anthonin organization: Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore |
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| Keywords | PRC ADC TZ PZ PSADWP AUC GLDM DT DWI EP2D ML PSA DCE ROC TSE EM Gleason score (GS) ROI GLCM Ktrans SUV GRE GLRLM PSAD SUVmax SVM TOF-PSF GLSZM Radiomics PCa Ve Multiparametric magnetic resonance imaging (mpMRI) Quantitative parameters NPV OP-OSEM PPV kep mRMR T2w TURP WP SUVmean PSADTZ Positron emission tomography (PET) CG NGTDM Machine learning (ML) kNN ADC ratio GS CT PSMA mpMRI csPCa PI-RADS PSADCG PET |
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| SubjectTerms | Gleason score (GS) Humans Machine Learning Machine learning (ML) Magnetic Resonance Imaging - methods Male Multiparametric magnetic resonance imaging (mpMRI) Neoplasm Grading Positron emission tomography (PET) Prostate-Specific Antigen Prostatic Neoplasms - pathology Quantitative parameters Radiomics Retrospective Studies |
| Title | Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions |
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