Comparative performances of machine learning algorithms in radiomics and impacting factors

• Published in: Scientific reports Vol. 13; no. 1; p. 14069
• Main Authors: Decoux, Antoine, Duron, Loic, Habert, Paul, Roblot, Victoire, Arsovic, Emina, Chassagnon, Guillaume, Arnoux, Armelle, Fournier, Laure
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28-08-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/1305; 631/114/1564; Algorithms; Classification; Computer Science; COVID-19; Datasets; Feature selection; Humanities and Social Sciences; Learning algorithms; Machine learning; Medical Imaging; multidisciplinary; Radiomics; Regression analysis; Sarcopenia; Science; Science (multidisciplinary); COVID-19 / diagnostic imaging; Head; Algorithms; Machine Learning; Humans; Random Forest
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-39738-7

There are no current recommendations on which machine learning (ML) algorithms should be used in radiomics. The objective was to compare performances of ML algorithms in radiomics when applied to different clinical questions to determine whether some strategies could give the best and most stable performances regardless of datasets. This study compares the performances of nine feature selection algorithms combined with fourteen binary classification algorithms on ten datasets. These datasets included radiomics features and clinical diagnosis for binary clinical classifications including COVID-19 pneumonia or sarcopenia on CT, head and neck, orbital or uterine lesions on MRI. For each dataset, a train-test split was created. Each of the 126 (9 × 14) combinations of feature selection algorithms and classification algorithms was trained and tuned using a ten-fold cross validation, then AUC was computed. This procedure was repeated three times per dataset. Best overall performances were obtained with JMI and JMIM as feature selection algorithms and random forest and linear regression models as classification algorithms. The choice of the classification algorithm was the factor explaining most of the performance variation (10% of total variance). The choice of the feature selection algorithm explained only 2% of variation, while the train-test split explained 9%.