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Published on: March 30, 2015
2.5D Deep Learning-Based Prediction of Pathological Grading of Clear Cell Renal Cell Carcinoma Using
Zi Yang1, Haitao Jiang2, Shuai Shan3
1School of Automation, Hangzhou Dianzi University, Hangzhou 310018, P.R. China (Z.Y., Q.K., Y.Z.).
Rationale And Objectives:
To develop and validate a deep learning model based on arterial phase-enhanced CT for predicting the pathological grading of clear cell renal cell carcinoma (ccRCC).
Materials And Methods:
Data from 564 patients diagnosed with ccRCC from five distinct hospitals were retrospectively analyzed. Patients from centers 1 and 2 were randomly divided into a training set (n=283) and an internal test set (n=122). Patients from centers 3, 4, and 5 served as external validation sets 1 (n=60), 2 (n=38), and 3 (n=61), respectively. A 2D model, a 2.5D model (three-slice input), and a radiomics-based multi-layer perceptron (MLP) model were developed. Model performance was evaluated using the area under the curve (AUC), accuracy, and sensitivity.
Results:
The 2.5D model outperformed the 2D and MLP models. Its AUCs were 0.959 (95% CI: 0.9438-0.9738) for the training set, 0.879 (95% CI: 0.8401-0.9180) for the internal test set, and 0.870 (95% CI: 0.8076-0.9334), 0.862 (95% CI: 0.7581-0.9658), and 0.849 (95% CI: 0.7766-0.9216) for the three external validation sets, respectively. The corresponding accuracy values were 0.895, 0.836, 0.827, 0.825, and 0.839. Compared to the MLP model, the 2.5D model achieved significantly higher AUCs (increases of 0.150 [p<0.05], 0.112 [p<0.05], and 0.088 [p<0.05]) and accuracies (increases of 0.077 [p<0.05], 0.075 [p<0.05], and 0.101 [p<0.05]) in the external validation sets.
Conclusion:
The 2.5D model based on 2.5D CT image input demonstrated improved predictive performance for the WHO/ISUP grading of ccRCC.
