The clinical significance of computed tomography texture features of renal cell carcinoma in predicting pathological T1-3 staging.

BACKGROUND

Precise T staging is an important prerequisite for the treatment decisions of patients with renal cell carcinoma (RCC). We aimed to predict the pathological T1-3 staging of RCC with an automatic multiclass T staging prediction mode.

METHODS

We retrospectively enrolled 100 consecutive patients with pathologically proven RCC that was newly diagnosed and untreated from Sun Yat-sen University Cancer Center and randomly split these patients into a training set (70%) and an internal testing set (30%). We enrolled additional 29 patients with pathologically proven RCC from The Third Affiliated Hospital of Shenzhen University as the external testing set. We used the training set data to establish a prediction model for pathological T1-3 staging of RCC and validated the effect of the training model using the internal and external testing sets. Quantitative decomposition of the prediction model was conducted to explore the contribution of each extracted feature.

RESULTS

The computed tomography (CT) images of 100 patients (37, 29, and 34 patients with T1, T2, and T3 staging, respectively, according to the eighth tumor-node-metastasis staging system) were used to establish the prediction model for T staging using delineation of the target area, image segmentation, and feature extraction. The micro area under the curve (AUC) and macro-AUC of the model were 0.90 [95% confidence interval (CI): 0.84-1.00] and 0.91 (95% CI: 0.86-1.00), respectively. In terms of validation with the external testing set, the micro-AUC and macro-AUC were 0.72 (95% CI: 0.66-0.84) and 0.78 (95% CI: 0.69-0.88), respectively.

CONCLUSIONS

Our prediction model showed good performance in predicting the pathological T1-3 staging of RCC.