Diagnosis of clear cell renal cell carcinoma via a deep learning model with whole-slide images.

BACKGROUND

Traditional pathological diagnosis methods have limitations in terms of interobserver variability and the time consumption of evaluations. In this study, we explored the feasibility of using whole-slide images (WSIs) to establish a deep learning model for the diagnosis of clear cell renal cell carcinoma (ccRCC).

METHODS

We retrospectively collected pathological data from 95 patients with ccRCC from January 2023 to December 2023. All pathological slices conforming to the standards of the model were manually annotated first. The WSIs were preprocessed to extract the region of interest. The WSIs were divided into a training set and a test set, and the ratio of tumor slices to normal tissue slices in the training set to the test set was 3:1. Positive and negative samples were randomly extracted. Model training was based on a convolutional neural network (CNN) and a random forest model. The accuracy of the model was evaluated by generating a receiver operating characteristic (ROC) curve.

RESULTS

A total of 663 pathological slices from 95 patients with ccRCC were collected. The mean number of slices per patient was 7.6 ± 2.7 (range: 3-17), with 506 tumor slices and 157 normal tissue slices. There were 200 tumor slices and 74 normal slices in the training set, and a total of 200,870 small images were extracted. There were 250 tumor slices and 63 normal slices in the test set, and a total of 39,211 small images were extracted. According to the CNN model and random forest model trained with the training set, 11 pathological slices in the test set were identified as false normal slices, and six pathological slices were identified as false tumor slices. The total accuracy was 94.6% (296/313), the precision rate was 97.6% (239/245), and the recall rate was 95.6% (239/250). The generated probabilistic heatmaps were consistent with the manually annotated pathological images. The ROC curve results revealed that the area under curve (AUC) reached 0.9658 (95% confidence interval: 0.9603-0.9713), the specificity was 90.5%, and the sensitivity was 95.6%.

CONCLUSION

The use of a deep learning method for the diagnosis of ccRCC is feasible. The ccRCC model established in this study achieved high accuracy. AI-based diagnostic methods for ccRCC may improve diagnostic efficiency.