Histology Pattern Recognition Software in Investigative Pathology
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1 Histology Pattern Recognition Software in Investigative Pathology J. Webster, DVM, PhD, DACVP Laboratory of Cancer Biology and Genetics National Cancer Institute, Bethesda, MD Pathology Visions 2011 November 1, 2011
2 Outline Introduction Pattern recognition image analysis Evaluation of pattern recognition image analysis Tissue feature quantification Segmentation of morphologically complex tissues Observations and personal experiences Applications and Integration Conclusions
3 Whole Slide Digital Imaging: Revolutionary Transformation in Pathology Diagnostic pathology Enhanced telepathology Real-time consultations Education Specimen preservation Uniform lesion display Conferencing Investigative pathology Quantitative morphometric image analysis
4 Morphometric Image Analysis Quantify immunohistochemical reactions Quantify histochemical stains Quantify tissue feature areas Identify regions of interest Screen for histologic lesions
5 Morphometric Image Analysis Advantages Increased data acquisition Decreased subjectivity Decreased observer variability Whole-slide image analysis Limitations Few well-developed protocols for applications Sensitivity to processing and handling Two-dimensional analysis
6 Pattern Recognition Image Analysis Advantages Improved analysis throughput Reproducible, quantitative analysis Decreased intra-/ inter-observer bias Specific Applications Quantifying metastatic lesions Pre-clinical therapeutic models Tumor transplantation models Identifying regions of Interest Immunohistochemistry quantification Diagnostic screening
7 Quantification of Metastatic Burden
8 Quantification of Metastatic Burden 23.57% Tumor
9 Identification of Regions of Interest: Immunohistochemistry Quantification
10 Pattern Recognition Image Analysis: Persistent Questions How accurate are PRIA measurements? How reproducible are PRIA measurements? Can PRIA software be used for diagnostic screening? How can we best employ PRIA software? How well does 2-D PRIA represent 3-D lesions?
11 Evaluation of Pattern Recognition Image Analysis (PRIA) Comparison to established morphometric measurements Quantification of pulmonary metastatic tumor burden PRIA vs. manual segmentation Assessment of performance during segmentation of morphologically complex tissues Identification of 3 ontogenic germ layers in stem cellderived teratomas
12 Lung Tumor Burden Quantification: Comparison to Established Morphometric Techniques 39 butterfly sections of mouse lungs Metastatic mammary carcinoma Formalin insufflated Hematoxylin and eosin Digitally scanned Aperio XT digital slide scanner Aperio Spectrum w/ ImageScope Whole- slide image analysis Comparison of tumor burden area measurements Genie pattern recognition software Manual image segmentation
13 Manual Image Segmentation 17.1% Tumor
14 Genie Training Montage
15 Genie Lung Metastasis Quantification 13.7% Tumor
16 Passing and Bablok Regression Analysis Percent Tumor: PRIA vs. Manual Segmentation slope = , y intercept = p > 0.1
17 Genie Lung Metastasis Image Mark-up
18 Bland-Altman Limits of Agreement Plot: Percent Tumor Difference vs. Tumor Burden
19 Lung Tumor Burden Quantification: Conclusions Commensurate percent tumor measures PRIA tended to be < 9% less than Manual Differences between methods are uniform across samples Consistent inaccuracies Mostly tolerable Tangential bronchioles and atelectasis Algorithms are sensitive to variations in Tissue handling Processing Staining
20 Segmentation of Morphologically Complex Tissues: Stem Cell-derived Teratomas Teratomas: Neoplasm derived from pluripotent cells capable of recapitulating the spectrum of embryonic development Tissues representing 3 ontogenic germ layers In vivo evidence of stem cell pluipotency
21 Stem Cell Derived Teratomas Samples: 26 Teratomas Mouse 10 Embryonic stem cell/ 2 induced pluripotent stem cell Human 8 Embryonic stem cell/ 6 induced pluripotent stem cell 10 more differentiated 16 less differentiated Formalin fixed, hematoxylin and eosin stained
22 Genie Training Montage
23 PRIA Pseudo-color Mark-up
24 Teratoma Algorithm 1: Well-differentiated Teratoma
25 Teratoma 1 Algorithm: Less differentiated Teratoma
26 Teratoma 2 Algorithm: Less Differentiated Teratoma
27 Segmentation of Complex Tissues: Conclusions Challenging to account for all tissue classes in a single montage Montage size limits Broad spectrum of spatial-spectral features Overlapping spatial-spectral features Overcoming some challenges Development of multiple algorithms Consider limitations Ask appropriate questions
28 Perceived Limitations Based on Experience Preferential reliance on spectral features Sensitivity to specimen handling, processing, staining Limited contextual understanding Restriction to a single magnification Repetitive difficulties in segmenting necrosis
29 Preferential Reliance on Spectral Features
30 Repetitive Challenge: Necrosis Segmentation Teratoma Algorithm 1
31 PRIA Application: Tissue Biobank Quality Assurance Tissue biobanking Essential for translational biomedical research Reliant on high-quality, well-annotated specimens Need for quality assurance pathology review Traditional quality assurance pathology review Confirmation of disease Subjective assessment of percent tumor Single or multiple staff pathologists Goal: Utilize PRIA to reproducibly and consistently quantify tumor percentages in biobank specimens
32 Quantification of Melanoma in Tissue Biobank Specimens Webster et al. J Biomol Tech, % Melanoma
33 Quantification of Osteosarcoma in Tissue Biobank Specimens Webster et al. J Biomol Tech, % Osteosarcoma
34 Webster et al. J Biomol Tech, 2011 Distribution of Percent Tumor Stratified by Tumor Type
35 Webster et al. J Biomol Tech, 2011 Within Patient Variability of Percent Tumor in Tissue Sections
36 Questions: 2-Dimensional Analysis of 3-Dimensional Lesions Are single sections representative? How many step sections are representative? What thicknesses? Evaluations: Comparison to in vivo bioluminescence imaging Comparison to advanced imaging modalities MRI CT scan
37 2-Dimensional Analysis of 3-Dimensional Lesions Correlation of tumor area quantified using PRIA vs. in vivo luminescent imaging Day et al. Int J Cancer, 2010
38 Conclusions Histologic PRIA software allows for Reproducible tissue quantification Decreased intra-/ inter-observer variability Decreased subjectivity Increased throughput Limitations must be considered Difficulty segmenting complex tissues Sensitivity to variations in tissue processing Preferential reliance on spectral features
39 Conclusions PRIA is optimally utilized when Tissues are uniformly handled/ processed Algorithms are simplified Application to appropriate questions Quality assurance is uniformly applied Pathologist oversight is included in PRIA applications
40 Acknowledgments NCI Molecular Pathology Unit Mark Simpson Jennifer Dwyer Kara Corps Shelley Hoover Bih-Rong Wei John Hickerson National Institute of Aging Minoru Ko Yuhki Nakatake Johns Hopkins University Tarja Juopperi Hongjun Song NCI Laboratory of Cancer Biology and Genetics Lalage Wakefield Yu-an Yang Christie Tomlinson Jeff Green Jing Huang
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