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Fig. 1 | Cancer Imaging

Fig. 1

From: A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy

Fig. 1

Radiomics feature selection using the LASSO Cox regression model. a The partial likelihood deviance was plotted versus log (lambda). The y-axis indicates the partial likelihood deviance, while the lower x-axis indicates the log (lambda) and the upper x-axis represents the average number of predictors. Dotted vertical lines were drawn at the optimal values using the minimum criteria and 1 standard error of the minimum criteria. The tuning parameter (λ) was selected in the LASSO model via 10-fold cross-validation based on minimum criteria. b LASSO coefficient profiles of the 270 radiomics features. The coefficients (y-axis) were plotted against log (lambda) and 7 features with nonzero coefficients were selected to build the radiomics signature

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