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Fig. 4 | Respiratory Research

Fig. 4

From: Combination of computed tomography imaging-based radiomics and clinicopathological characteristics for predicting the clinical benefits of immune checkpoint inhibitors in lung cancer

Fig. 4

Development of radiomics nomogram model 1. a Nomogram based on independent predictors (Rad-score, Age, N stage and M stage). b Calibration curves of the nomogram in the training cohort. The horizontal axis is the predicted incidence of the durable clinical benefit (DCB), whereas the vertical axis is the observed incidence of the DCB. The dotted line on the diagonal is the reference line at which the predicted value is equal to the actual value. The orange line is the calibration curve. c Decision curve analysis for each model. The y-axis measures the net benefit, which was calculated using true-positive and false-positive results. Radiomics nomogram model 1 had the highest net benefit among all positive predictions (line labeled “All”), all negative predictions (line labeled “None”), and models (line labeled “radiomics model 1”) at the threshold from 0.1 to 0.9

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