Deep Learning Model Based on CT Images to Predict Ki-67 Expression in Renal Carcinoma

Abstract Objective To establish a preoperative prediction model of Ki-67 expression in renal carcinoma by combining CT images of renal carcinoma with deep learning technology, and to evaluate its effect in clinical application.Methods A retrospective analysis was performed on the CT images and pathological data of 137 patients with renal carcinoma who underwent renal CT plain scan plus enhancement scans and who were diagnosed pathologically from January 2019 to November 2023 at the Affiliated Hospital of Hebei University. All recruited patients were divided into 35 cases with Ki-67 ≥ 10% and 102 cases with Ki-67 < 10% based on Ki-67 expression. Of these, 110 patients were divided into the training group and the test group in a 4:1 ratio, and the remaining 27 cases were used as clinical validation group. Based on the Mobilenetv3-large model, prediction models incorporating plain scan, arterial, venous and excretion phases were constructed using the training set data. The predictive performance of the models, including accuracy, accuracy, sensitivity, specificity, F1 score, and AUC values, were assessed by inputting CT images of the test group. The best-performing model was applied to the clinical validation group to compare the predicted results with the actual results of pathology; the accuracy, sensitivity, specificity and Kappa coefficient of the model were calculated to assess its clinical efficacy.Results The Mobilenetv3-large model in the venous phase showed the best performance in terms of accuracy, sensitivity and F1 score, as well as relatively high precision and AUC value, indicating good robustness. In the clinical validation group, the model predicted Ki-67 expression with an accuracy of 0.814, sensitivity and specificity of 0.889 and 0.667 at low and high levels, respectively, and a Kappa coefficient of 0.57.Conclusion The Mobilenetv3-large model based on venous-phase CT images is potentially clinically valuable as it can effectively predict Ki-67 expression and provide accurate, efficient, and noninvasive support for the development of personalized treatment plans.