Automated image based prominent nucleoli detection

Automated Renal Cancer Grading Using Nuclear Pleomorphic Patterns

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Gland segmentation in prostate histopathological images

Glandular structural features are important for the tumor pathologist in the assessment of cancer malignancy of prostate tissue slides. The varying shapes and sizes of glands combined with the tedious manual observation task can result in inaccurate assessment. There are also discrepancies and low-level agreement among pathologists, especially in cases of Gleason pattern 3 and pattern 4 prostate adenocarcinoma. An automated gland segmentation system can highlight various glandular shapes and structures for further analysis by the pathologist. These objective highlighted patterns can help reduce the assessment variability. We propose an automated gland segmentation system. Forty-three hematoxylin and eosin-stained images were acquired from prostate cancer tissue slides and were manually annotated for gland, lumen, periacinar retraction clefting, and stroma regions. Our automated gland segmentation system was trained using these manual annotations. It identifies these regions using a combination of pixel and object-level classifiers by incorporating local and spatial information for consolidating pixel-level classification results into object-level segmentation. Experimental results show that our method outperforms various texture and gland structure-based gland segmentation algorithms in the literature. Our method has good performance and can be a promising tool to help decrease interobserver variability among pathologists.

Nucleolus-derived mediators in oncogenic stress response and activation of p53-dependent pathways

Rapid growth and division of cells, including tumor ones, is correlated with intensive protein biosynthesis. The output of nucleoli, organelles where translational machineries are formed, depends on a rate of particular stages of ribosome production and on accessibility of elements crucial for their effective functioning, including substrates, enzymes as well as energy resources. Different factors that induce cellular stress also often lead to nucleolar dysfunction which results in ribosome biogenesis impairment. Such nucleolar disorders, called nucleolar or ribosomal stress, usually affect cellular functioning which in fact is a result of p53-dependent pathway activation, elicited as a response to stress. These pathways direct cells to new destinations such as cell cycle arrest, damage repair, differentiation, autophagy, programmed cell death or aging. In the case of impaired nucleolar functioning, nucleolar and ribosomal proteins mediate activation of the p53 pathways. They are also triggered as a response to oncogenic factor overexpression to protect tissues and organs against extensive proliferation of abnormal cells. Intentional impairment of any step of ribosome biosynthesis which would direct the cells to these destinations could be a strategy used in anticancer therapy. This review presents current knowledge on a nucleolus, mainly in relation to cancer biology, which is an important and extremely sensitive element of the mechanism participating in cellular stress reaction mediating activation of the p53 pathways in order to counteract stress effects, especially cancer development.