Nuclear pleomorphic patterns are essential for Fuhrman grading of clear cell renal cell carcinoma (ccRCC). Manual observation of renal histopathologic slides may lead to subjective and inconsistent assessment between pathologists. An automated, image-based system that classifies ccRCC slides by quantifying nuclear pleomorphic patterns in an objective and consistent interpretable fashion can aid pathologists in histopathologic assessment.
In the current study, histopathologic tissue slides of 59 patients with ccRCC who underwent surgery at Singapore General Hospital were assembled retrospectively. An automated image classification pipeline detects and analyzes prominent nucleoli in ccRCC images to classify them as either low (Fuhrman grade 1 and 2) or high (Fuhrman grade 3 and 4). The pipeline uses machine learning and image pixel intensity–based feature extraction techniques for nuclear analysis. We trained classification systems that concurrently analyze different permutations of multiple prominent nucleoli image patches.
Given the parameters for feature combination and extraction, we present experimental results across various configurations for the classification of a given ccRCC histopathologic image. We also demonstrate that the image score used by the pipeline, termed fraction value, is correlated (R = 0.59) with an existing multigene assay–based scoring system that has previously been demonstrated to be a strong indicator of prognosis in patients with ccRCC.
The current method provides an objective and fully automated way by which to process pathologic slides. The correlation study with a multigene assay–based scoring system also allows us to provide quantitative interpretation for already established nuclear pleomorphic patterns in ccRCC. This method can be extended to other cancers whose corresponding grading systems use nuclear pattern information.