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Fig. 2 | BMC Cancer

Fig. 2

From: Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics

Fig. 2

Radiomics workflow in this study. a Image segmentation was performed in arterial phase, portal venous phase, and delayed phase of Contrast-enhanced MR images. b Features extracted from tumors were classified into shape, first-order statistics, texture, and the features were extracted by least absolute shrinkage and selection operator (LASSO) regression and random survival forest. c clinical nomogram and combined nomogram were established. d Two nomograms were compared by employing time-dependent receiver operating characteristics (ROC) curves, calibration curves, and decision curve analysis. Shape features capture the geometric properties of the tumor, which can be associated with tumor growth and invasiveness. First-order statistics describe basic image characteristics like mean intensity and variance. Texture features reflect the spatial distribution of pixel intensities, indicating tumor heterogeneity and complexity

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BMC Cancer

ISSN: 1471-2407

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