Figure 2 | Scientific Reports

Figure 2

From: Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images

Figure 2The alternative text for this image may have been generated using AI.

Flowchart of some sample quantitative radiomic features used in our study that were extracted from segmented tumor regions of interest (ROI) of mpMRI images. In summary, 55 different features were extracted per image type (i.e., T2WI or ADC) using four different texture extraction methods, yielding 110 radiomic features per patient. The four texture methods included histogram analysis, Gray-Level Co-occurrence and Difference Matrix methods (GLCM and GLDM) and Fast Fourier Transform (FFT). Some of these features are highlighted in green, blue and red respectively. The full list and details of these features are provided in the online Appendix in Supplementary Information. Note that all these features were 2D, as the input imaging data were two-dimensional.

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