| Περίληψη: | Prostate cancer is a disease affecting growing numbers of people, which can form metastases and cause death. The risk of mortality and complications from prostate cancer is assessed by visual inspection of tumour morphology in Haematoxylin & Eosin-stained tissue samples. The morphology of prostate cancer is highly heterogeneous: during tumorigenesis, the normal glands that traverse and branch through the prostate lose their structural organisation, and develop a range of aberrant morphological characteristics, such as smaller size, larger nuclei and loss of the layer of basal epithelial cells found in normal glands. Depending on the level of structural loss, and of dedifferentiation of the glands, tissue samples are assigned a Gleason Score from 4 to 10. A higher Gleason Score is indicative of increased clinical risk for patients, and it guides the decision on what type of treatment patients should receive. The Gleason Score suffers from considerable shortcomings, including a poor reproducibility that is due to its qualitative definition. In this work, we design an algorithm to extract a quantitative representation of the morphology of prostate cancer, based on the segmentation and morphological measurement of prostate glands. We validate the use of the representation by showing it automatically captures clinically relevant morphological classes, it is predictive of the Gleason Score, and it can be used to characterise different tissue specimen types used in prostate cancer diagnosis. The morphology of glands is used to diagnose prostate cancer. In some cases, the morphology may appear abnormal, but not to an extent to make a confident cancer diagnosis possible. Immunohistochemistry is then used to detect the lack of basal cells in glands, which is indicative of prostate cancer. We design an automated tool for advance requesting immunohistochemistry for such cases of ambiguous prostate morphology, and we estimate the temporal and monetary savings of introducing the tool into the clinical workflow.
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