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

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

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

Morphometric Image Analysis

• Quantify immunohistochemical reactions

• Quantify histochemical stains

• Quantify tissue feature areas

• Identify regions of interest

• Screen for histologic lesions

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

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

Quantification of Metastatic Burden

Quantification of Metastatic Burden

23.57% Tumor

Identification of Regions of Interest: Immunohistochemistry Quantification

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?

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 cell-

derived teratomas

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

Manual Image Segmentation

17.1% Tumor

Genie Training Montage

Genie Lung Metastasis Quantification

13.7% Tumor

Passing and Bablok Regression Analysis Percent Tumor: PRIA vs. Manual Segmentation

slope = 0.9744, y intercept = -0.1508

p > 0.1

Genie Lung Metastasis Image Mark-up

Bland-Altman Limits of Agreement Plot: Percent Tumor Difference vs. Tumor Burden

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

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

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

Genie Training Montage

PRIA Pseudo-color Mark-up

Teratoma Algorithm 1: Well-differentiated Teratoma

Teratoma 1 Algorithm: Less differentiated Teratoma

Teratoma 2 Algorithm: Less Differentiated Teratoma

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

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

Preferential Reliance on Spectral Features

Repetitive Challenge: Necrosis Segmentation Teratoma Algorithm 1

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

Quantification of Melanoma in Tissue Biobank Specimens

68.6% Melanoma Webster et al. J Biomol Tech, 2011

Quantification of Osteosarcoma in Tissue Biobank Specimens

55.6% Osteosarcoma Webster et al. J Biomol Tech, 2011

Distribution of Percent Tumor Stratified by Tumor Type

Webster et al. J Biomol Tech, 2011

Within Patient Variability of Percent Tumor in Tissue Sections

Webster et al. J Biomol Tech, 2011

2-Dimensional Analysis of 3-Dimensional Lesions

• Questions: – 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

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

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

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

Acknowledgments • NCI Molecular Pathology Unit

– Mark Simpson – Jennifer Dwyer – Kara Corps – Shelley Hoover – Bih-Rong Wei – John Hickerson

• NCI Laboratory of Cancer

Biology and Genetics – Lalage Wakefield – Yu-an Yang – Christie Tomlinson – Jeff Green – Jing Huang

• National Institute of Aging – Minoru Ko – Yuhki Nakatake

• Johns Hopkins University

– Tarja Juopperi – Hongjun Song