A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset

• Published in: Journal of imaging Vol. 7; no. 10; p. 215
• Main Authors: Donisi, Leandro, Cesarelli, Giuseppe, Castaldo, Anna, De Lucia, Davide Raffaele, Nessuno, Francesca, Spadarella, Gaia, Ricciardi, Carlo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 18.10.2021; MDPI
• Subjects: Algorithms; Classification; Clinical significance; Datasets; Feature extraction; Lesions; Machine learning; Magnetic resonance; Magnetic resonance imaging; MRI; Prostate cancer; Radiomics; Software; Statistical analysis
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging7100215

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.