Fig. 1

The workflow includes the steps required in a radiomic and artificial intelligence analysis in prostate cancer patients. The first step involves collecting clinical data on patient characteristics, histopathological data on tumor characteristics, and imaging data, with the extraction of radiomic features (such as shape, intensity, and texture features). Radiomic modeling involves three major aspects: feature selection, modeling methodology, and validation. The number of radiomic features that can be extracted from images is virtually unlimited. Once extracted, radiomic features must be selected; redundant or non-robust features against sources of variability must be identified and eliminated through dimensionality reduction techniques, to avoid overfitting problems. The choice of modeling methodology and the identification of optimal machine learning methods for radiomic applications are a crucial step in obtaining robust and clinically relevant results. The choice of a modeling methodology (supervised or unsupervised machine learning method) depends on the setting of the data, the characteristics of the analyzed population, and the experience of the researchers. The model chosen affects prediction and performance in radiomics, and hence, implementations of multiple modeling methodologies are highly desirable. Finally, validation techniques are useful tools for assessing model performance. An externally validated model has more credibility than an internally validated model because data obtained by the first approach are more independent. Validation is essential to verify the repeatability and reproducibility of the model, demonstrating statistical consistency between the training and validation datasets