Automatic clear cell renal cell carcinoma grading framework using histopathological images via artificial intelligence: a benchmarking study.

BACKGROUND

Renal cell carcinoma (RCC) is a common malignant tumor of the urinary system. Postoperative patients face the risk of recurrence or metastasis, and those who experience disease progression often have a poor prognosis. Therefore, investigating prognostic factors for RCC is crucial for guiding individualized patient treatment. Current studies demonstrate that RCC grading is significantly associated with prognosis, but pathological grading requires high expertise while being time-consuming.

METHODS

Convolutional neural networks (CNNs) have shown significant advantages in tumor tissue recognition. Based on this, our study employed different CNNs (ResNet101, DenseNet121, EfficientNetb6, Wide ResNet101 32 × 8d, ResNeXt101) to automatically distinguish non-tumor tissue from tumor tissue and determine tumor grade.

RESULTS

The results demonstrated that at 100× magnification (actual magnification of the pathological images, likewise for the following), in patch-level, the CNN achieved an accuracy of 0.8565 for identifying non-tumor tissues; at 400× magnification, the accuracy reached 0.8919. For tumor tissues of different grades, in patch-level, the CNNs performed best for Grade 1 tissues, with accuracies of 0.8616 at 100× magnification and 0.8182 at 400× magnification. Besides, at 100× magnification, in patch-level, the accuracy in identifying for Grade 2, Grade 3, Grade 4 tissues was 0.6225, 0.7604, and 0.8361. At 400× magnification, the accuracy was 0.6883, 0.6546 and 0.5127 for Grade 2, Grade 3 and Grade 4 tissues. Compared to pathologists, who can not distinguish between Grade 1 and Grade 2 tissues at 100× magnification, nor between Grade 2 and Grade 3 tissues at 400× magnification, the CNN can distinguish between Grade 1 and Grade 2 tissues at 100×magnification, achieving an accuracy of 0.6225 for Grade 2 tissue identification. They are also capable of differentiating Grade 2 and Grade 3 tissues at 400x magnification, with an accuracy of 0.6546 for Grade 3 tissue, which demonstrated superior performance. An integrated model constructed from patch-level predictions was used to determine the grade at the whole-slide level. The results revealed a clear tendency towards left-skewed or right-skewed distributions in the grading predictions causing by the manner constructed the integrated model emphasizing whether the highest-grade tissue is present in the tumor tissue. This approach requires further refinement.

CONCLUSION

These findings indicate that CNNs can identify tumor and non-tumor tissue of clear cell renal cell carcinoma (ccRCC) and determine tumor grade, exhibiting advantages over pathologists. However, the model’s performance at the whole-slide level requires further improvement. Owing to the generalizability of this study, its findings can be considered as a potential benchmark for similar research, providing a reference for subsequent related studies.