Deep learning model based on CT images to predict Ki-67 expression in renal carcinoma

The aim of the present study was to establish a preoperative prediction model of Ki-67 expression in renal cell carcinoma (RCC) by combining CT images of RCC with deep learning technology, and to evaluate its effect in clinical application. A retrospective analysis was performed on the CT images and pathological data of 137 patients with RCC 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 a clinical validation group. Using 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 area under the curve (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 k coefficient of the model were calculated to assess its clinical efficacy. 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 k coefficient of 0.57. The Mobilenetv3-large model, when applied to venous-phase CT images, demonstrates notable clinical utility. It can proficiently forecast Ki-67 expression levels, thereby offering a precise, expedient and non-invasive aid in the formulation of tailored therapeutic strategies.