Computational analysis of variability and uncertainty in the clinical reference on magnetic resonance imaging radiomics: modelling and performance.

ABSTRACT: To conduct a computational investigation to explore the influence of clinical reference uncertainty on magnetic resonance imaging (MRI) radiomics feature selection, modelling, and performance. This study used two sets of publicly available prostate cancer MRI = radiomics data (Dataset 1: n = 260; Dataset 2: n = 100) with Gleason score clinical references. Each dataset was divided into training and holdout testing datasets at a ratio of 7:3 and analysed independently. The clinical references of the training set were permuted at different levels (increments of 5%) and repeated 20 times. Four feature selection algorithms and two classifiers were used to construct the models. Cross-validation was employed for training, while a separate hold-out testing set was used for evaluation. The Jaccard

SUBMITTER: Xue C 

PROVIDER: S-EPMC11573982 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature