myavr.info Personal Growth Pathology The Big Picture 2008.pdf

PATHOLOGY THE BIG PICTURE 2008.PDF

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The material in this eBook also appears in the print version of this title: Copyright © by The McGraw-Hill Companies, Inc. Click here for terms of use. . Pathology: The Big Picture includes several features to facilitate. Pathology: The Big Picture. Walter L. Pathology of the Liver, Gallbladder, and Pancreas Pathology of the Male and Female Reproductive Tract and Breast. Pathology: the big picture. [Walter L Kemp; Dennis K Burns; Travis G Brown] -- Written to give medical students with what they truly must know.


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In fact, the history of pathology has roots in common with all other medical specialties, arising in antiquity when men reasoned about the physical ailments that afflicted them. For obvious reasons, those gross features of disease that were directly visible, either in life, or after death in funereal preparations, came first to notice.

In addition, over the last century, archaeological discoveries increasingly have been linked with palaeopathological investigations, furnishing a wealth of observations of gross external features of disease, from prehistoric peoples to the present time.

As a result, museums around the world contain marble and terra cotta statues expressing processes that can now be interpreted as examples of hernias, breast tumours, varicose veins, ulcers and other diseases. These records contain information on different types of bone injuries, trachoma Nile valley , ulcerating lumps cancer? This generic model allows QuPath to represent and display relationships between very large numbers of image objects in an efficient and intuitive manner across gigapixel images, and support the fast and interactive training of object classifiers using machine learning techniques.

A practical example of this is in the evaluation of the presence, localization and intensity of expression of key diagnostic, prognostic and predictive biomarkers in tissue sections. These biomarkers are typically detected using antibodies and chromogenic based detection systems, and are selectively expressed in tumor cells or in other cellular compartments. QuPath further supports the classification of different cell types according to these features, to generate a comprehensive phenotypic description of each cell within the tissue sample.

This in turn provides a quantitative cellular map of the entire tissue section, which can be subsequently selected, queried and filtered to mine the image data and uncover morphological subtleties not immediately visible during traditional pathological assessment.

Pathology : the Big Picture

All of this can typically be achieved within minutes, without a requirement for specialist hardware. Results and Discussion To demonstrate some of these capabilities, including its biological and clinical validity, we used QuPath to analyze several image sets derived from surgical resection specimens from a population-based cohort of patients with stage II and stage III colon cancer, diagnosed between — stage II, stage III and with high-quality curation of clinicopathological information.

From representative paraffin-embedded tumor blocks, provided via the Northern Ireland Biobank, whole sections were haematoxylin and eosin-stained in the Northern Ireland Molecular Pathology Laboratory.

Tissue microarrays TMAs were generated from representative 1 mm diameter tumor cores from each case, sampled in triplicate from the tumor center after annotation. The aim was to test the performance of QuPath in the context of known and novel biological and clinical data, using cancer immunology and tumor suppressor genes as paradigms. After this step, batch analysis was applied across all TMA slides to identify the tissue within each core and automatically count the number of positive cells per mm2 tissue based upon a fast peak-finding algorithm after stain separation by color deconvolution 17 Supplementary Fig.

Running on a standard Mac Pro 3. After applying a median cutoff to the exported results, a statistically significant association between disease-specific survival and positive cell density scores was demonstrated for both CD3 and CD8 log-rank test, p-values 0. This recapitulates within our cohort the seminal work of Galon et al.

Figure 2 Survival analysis of colon cancer cohort based on QuPath automated image analysis. Representative images showing an original core and QuPath markup image are included below.

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Representative images show the original images and markup for tumors with a high and low stromal percentage respectively. Green indicates regions classified as stroma, dark red indicates tumor epithelium, while yellow represents other classified tissue or whitespace.

Full size image Next, we used QuPath to evaluate immunohistochemistry for p53 in a second set of TMAs from the same cohort. This required a more sophisticated analysis to encompass the biological understanding and staining pattern of the marker.

Despite the well-established role of TP53 in colorectal cancer carcinogenesis, results from prognostic studies assessing p53 IHC expression have been inconsistent However, the extreme negative pattern of aberrant p53 immunoreactivity has only been described relatively recently 21 , 22 and has not been widely assessed in colorectal cancer cohorts.

This example therefore emphasises the flexibility of the QuPath open source platform in measuring common IHC markers with variable tumor expression patterns, and demonstrates how the relationship between quantitative cellular analysis and clinical outcome can be robustly assessed.

PD-L1 immunoexpression is a prognostic marker in a range of cancer types and also a predictor of response to immune checkpoint therapy in some cancers 23 , However, there is a lack of consensus on the epitopes of clinical relevance and, more importantly, the optimal scoring systems for evaluation.

The approach to analysis was similar in principle to that adopted for p53, however further attention was required because of additional challenges posed by PD-L1 immunostaining.

Secondly, PD-L1 can be expressed in tumor epithelium, but is more commonly expressed in other tissue compartments, notably within peritumoral stromal inflammatory cells. Although this level of heterogeneity in staining pattern is increasingly being recognised, the clinical importance of distinguishing tumor epithelial from inflammatory cell staining is yet to be fully understood. Sufficient cell classification is therefore required to identify both positively and negatively staining tumor epithelial and non-tumor cell populations, in addition to distinguishing true protein expression levels from the various staining artefacts that are inherent with IHC-based tissue analysis.

Medical Biochemistry: The Big Picture

Here, applying QuPath, a cell was classified as positive or negative based on maximal DAB staining intensity, as a surrogate marker of protein expression, within a full cell region approximated by expanding detected nuclei Supplementary Fig.

A median cutoff of 1. Additionally, an analysis based on tertiles exhibited a dose-response effect, and separation of the tumor epithelial and non-epithelial components suggested that PD-L1 expression in colon cancer tissue is primarily found in the non-epithelial compartment Supplementary Fig.

These results support the incipient evidence of PD-L1 prognostic value in colorectal cancer reported by ourselves and others in independent cohorts 24 , 25 , 26 , and may be of help when used together with tumor microsatellite instability status for patient stratification in consideration of anti-PD-L1 therapy.

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Similar Items Related Subjects: Pathology -- Examination Questions. Linked Data More info about Linked Data. Primary Entity http: CreativeWork , schema: Book , schema: Intangible ;. Brown " ;. Burns " ;. Kemp " ;.

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Cancel Forgot your password? English View all editions and formats. Pathology -- Examinations, questions, etc. View all subjects.Despite the flaws of this theory, Hippocratic writers left remarkably clear descriptions of many pathological features, such as wound inflammation, tumours, haemorrhoids, malaria and tuberculosis Fig. In the United States, hematopathology is a board certified subspecialty licensed under the American Board of Pathology practiced by those physicians who have completed a general pathology residency anatomic, clinical, or combined and an additional year of fellowship training in hematology.

Slides, including tissue microarrays, can be loaded into trays, racks, or carousels. These biomarkers are typically detected using antibodies and chromogenic based detection systems, and are selectively expressed in tumor cells or in other cellular compartments.

Green indicates regions classified as stroma, dark red indicates tumor epithelium, while yellow represents other classified tissue or whitespace.

Document, Internet resource Document Type: Representative images showing an original core and QuPath markup image are included below. Your rating has been recorded. The American Osteopathic Board of Pathology also recognizes four primary specialties: anatomic pathology, dermatopathology, forensic pathology, and laboratory medicine.

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