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Corvo A, Caballero HSG, Westenberg MA, van Driel MA, van Wijk JJ. Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2021; 27:3851-3866. [PMID: 32340951 DOI: 10.1109/tvcg.2020.2990336] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Recent advances in computational and algorithmic power are evolving the field of medical imaging rapidly. In cancer research, many new directions are sought to characterize patients with additional imaging features derived from radiology and pathology images. The emerging field of Computational Pathology targets the high-throughput extraction and analysis of the spatial distribution of cells from digital histopathology images. The associated morphological and architectural features allow researchers to quantify and characterize new imaging biomarkers for cancer diagnosis, prognosis, and treatment decisions. However, while the image feature space grows, exploration and analysis become more difficult and ineffective. There is a need for dedicated interfaces for interactive data manipulation and visual analysis of computational pathology and clinical data. For this purpose, we present IIComPath, a visual analytics approach that enables clinical researchers to formulate hypotheses and create computational pathology pipelines involving cohort construction, spatial analysis of image-derived features, and cohort analysis. We demonstrate our approach through use cases that investigate the prognostic value of current diagnostic features and new computational pathology biomarkers.
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Cheng J, Zhang J, Han Y, Wang X, Ye X, Meng Y, Parwani A, Han Z, Feng Q, Huang K. Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis. Cancer Res 2017; 77:e91-e100. [PMID: 29092949 DOI: 10.1158/0008-5472.can-17-0313] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Revised: 02/13/2017] [Accepted: 06/29/2017] [Indexed: 12/17/2022]
Abstract
In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers. Cancer Res; 77(21); e91-100. ©2017 AACR.
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Affiliation(s)
- Jun Cheng
- Guangdong Province Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Jie Zhang
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio.,Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana
| | - Yatong Han
- College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China
| | - Xusheng Wang
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio
| | - Xiufen Ye
- College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China
| | - Yuebo Meng
- College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, China
| | - Anil Parwani
- Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Zhi Han
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio.,Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana.,Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Qianjin Feng
- Guangdong Province Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
| | - Kun Huang
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio. .,Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana
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histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat Methods 2017; 14:873-876. [PMID: 28783155 PMCID: PMC5617107 DOI: 10.1038/nmeth.4391] [Citation(s) in RCA: 350] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Accepted: 07/07/2017] [Indexed: 12/11/2022]
Abstract
Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational histology topography cytometry analysis toolbox (histoCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell-cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of histoCAT through analysis of highly multiplexed mass cytometry images of human breast cancer tissues.
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McKinley ET, Sui Y, Al-Kofahi Y, Millis BA, Tyska MJ, Roland JT, Santamaria-Pang A, Ohland CL, Jobin C, Franklin JL, Lau KS, Gerdes MJ, Coffey RJ. Optimized multiplex immunofluorescence single-cell analysis reveals tuft cell heterogeneity. JCI Insight 2017; 2:93487. [PMID: 28570279 DOI: 10.1172/jci.insight.93487] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 04/27/2017] [Indexed: 12/17/2022] Open
Abstract
Intestinal tuft cells are a rare, poorly understood cell type recently shown to be a critical mediator of type 2 immune response to helminth infection. Here, we present advances in segmentation algorithms and analytical tools for multiplex immunofluorescence (MxIF), a platform that enables iterative staining of over 60 antibodies on a single tissue section. These refinements have enabled a comprehensive analysis of tuft cell number, distribution, and protein expression profiles as a function of anatomical location and physiological perturbations. Based solely on DCLK1 immunoreactivity, tuft cell numbers were similar throughout the mouse small intestine and colon. However, multiple subsets of tuft cells were uncovered when protein coexpression signatures were examined, including two new intestinal tuft cell markers, Hopx and EGFR phosphotyrosine 1068. Furthermore, we identified dynamic changes in tuft cell number, composition, and protein expression associated with fasting and refeeding and after introduction of microbiota to germ-free mice. These studies provide a foundational framework for future studies of intestinal tuft cell regulation and demonstrate the utility of our improved MxIF computational methods and workflow for understanding cellular heterogeneity in complex tissues in normal and disease states.
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Affiliation(s)
- Eliot T McKinley
- Epithelial Biology Center and.,Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Yunxia Sui
- General Electric Global Research Center, Niskayuna, New York, USA
| | - Yousef Al-Kofahi
- General Electric Global Research Center, Niskayuna, New York, USA
| | - Bryan A Millis
- Department of Cell and Developmental Biology.,Cell Imaging Shared Resource, and
| | - Matthew J Tyska
- Epithelial Biology Center and.,Department of Cell and Developmental Biology
| | - Joseph T Roland
- Epithelial Biology Center and.,Department of Surgery, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | | | | | - Christian Jobin
- Department of Medicine.,Department of Infectious Diseases and Pathology, and.,Department of Anatomy and Cell Physiology, University of Florida, Gainesville, Florida, USA
| | - Jeffrey L Franklin
- Epithelial Biology Center and.,Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Cell and Developmental Biology.,Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, Tennessee, USA
| | - Ken S Lau
- Epithelial Biology Center and.,Department of Cell and Developmental Biology
| | - Michael J Gerdes
- General Electric Global Research Center, Niskayuna, New York, USA
| | - Robert J Coffey
- Epithelial Biology Center and.,Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Cell and Developmental Biology.,Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, Tennessee, USA
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