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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Vydáno v:	Magnetic resonance imaging Ročník 100; s. 64 - 72
Hlavní autoři:	Nai, Ying-Hwey, Cheong, Dennis Lai Hong, Roy, Sharmili, Kok, Trina, Stephenson, Mary C., Schaefferkoetter, Josh, Totman, John J., Conti, Maurizio, Eriksson, Lars, Robins, Edward G., Wang, Ziting, Chua, Wynne Yuru, Ang, Bertrand Wei Leng, Singha, Arvind Kumar, Thamboo, Thomas Paulraj, Chiong, Edmund, Reilhac, Anthonin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Elsevier Inc 01.07.2023
Témata:	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 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
ISSN:	0730-725X, 1873-5894, 1873-5894
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0730-725X 1873-5894 1873-5894
DOI:	10.1016/j.mri.2023.03.009