1. A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset
- Author
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Leandro Donisi, Giuseppe Cesarelli, Anna Castaldo, Davide Raffaele De Lucia, Francesca Nessuno, Gaia Spadarella, and Carlo Ricciardi
- Subjects
radiomics ,machine learning ,MRI ,prostate cancer ,Photography ,TR1-1050 ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Electronic computers. Computer science ,QA75.5-76.95 - 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.
- Published
- 2021
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