Although prostate cancer is one of the most common causes of mortality and morbidity in advancing-age males, early diagnosis improves prognosis and modifies the therapy of choice. The aim of this study was the evaluation of a combined radiomics and machine learning approach on a publicly available dataset in order to distinguish a clinically significant from a clinically non-significant prostate lesion. A total of 299 prostate lesions were included in the analysis. A univariate statistical analysis was performed to prove the goodness of the 60 extracted radiomic features in distinguishing prostate lesions. Then, a 10-fold cross-validation was used to train and test some models and the evaluation metrics were calculated; finally, a hold-out was performed and a wrapper feature selection was applied. The employed algorithms were Naïve bayes, K nearest neighbour and some tree-based ones. The tree-based algorithms achieved the highest evaluation metrics, with accuracies over 80%, and area-under-the-curve receiver-operating characteristics below 0.80. Combined machine learning algorithms and radiomics based on clinical, routine, multiparametric, magnetic-resonance imaging were demonstrated to be a useful tool in prostate cancer stratification.

A combined radiomics and machine learning approach to distinguish clinically significant prostate lesions on a publicly available mri dataset / Donisi, L.; Cesarelli, G.; Castaldo, A.; De Lucia, D. R.; Nessuno, F.; Spadarella, G.; Ricciardi, C.. - In: JOURNAL OF IMAGING. - ISSN 2313-433X. - 7:10(2021), p. 215. [10.3390/jimaging7100215]

A combined radiomics and machine learning approach to distinguish clinically significant prostate lesions on a publicly available mri dataset

Donisi L.
Co-primo
;
Cesarelli G.
Co-primo
;
Castaldo A.;De Lucia D. R.;Nessuno F.;Spadarella G.
Co-ultimo
;
Ricciardi C.
Co-ultimo
2021

Abstract

Although prostate cancer is one of the most common causes of mortality and morbidity in advancing-age males, early diagnosis improves prognosis and modifies the therapy of choice. The aim of this study was the evaluation of a combined radiomics and machine learning approach on a publicly available dataset in order to distinguish a clinically significant from a clinically non-significant prostate lesion. A total of 299 prostate lesions were included in the analysis. A univariate statistical analysis was performed to prove the goodness of the 60 extracted radiomic features in distinguishing prostate lesions. Then, a 10-fold cross-validation was used to train and test some models and the evaluation metrics were calculated; finally, a hold-out was performed and a wrapper feature selection was applied. The employed algorithms were Naïve bayes, K nearest neighbour and some tree-based ones. The tree-based algorithms achieved the highest evaluation metrics, with accuracies over 80%, and area-under-the-curve receiver-operating characteristics below 0.80. Combined machine learning algorithms and radiomics based on clinical, routine, multiparametric, magnetic-resonance imaging were demonstrated to be a useful tool in prostate cancer stratification.
2021
A combined radiomics and machine learning approach to distinguish clinically significant prostate lesions on a publicly available mri dataset / Donisi, L.; Cesarelli, G.; Castaldo, A.; De Lucia, D. R.; Nessuno, F.; Spadarella, G.; Ricciardi, C.. - In: JOURNAL OF IMAGING. - ISSN 2313-433X. - 7:10(2021), p. 215. [10.3390/jimaging7100215]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/873770
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