Fig. 5From: Clinico-biological-radiomics (CBR) based machine learning for improving the diagnostic accuracy of FDG-PET false-positive lymph nodes in lung cancerThe nomogram was developed using the risk factors of CBR Model in the training set (a). The probability of each predictor could be converted into scores according to the first scale at the top of the nomogram. After adding up the corresponding prediction probability at the bottom of the nomogram was the risk of LNM. The nomogram’s score and probability threshold for predicting LNM were 0.19 and 0.55, respectively. Calibration curves showed the actual probability corresponded closely to the prediction of nomogram in training (b) and test (c) sets, respectivelyBack to article page