QuPath: Open source software for digital pathology image analysis

Scientific Reports - Tập 7 Số 1
Peter Bankhead1, Maurice B. Loughrey1, José A. Fernández1, Yvonne Dombrowski2, Darragh G. McArt1, Philip D. Dunne1, Stephen McQuaid1, Ronan T. Gray3, Liam Murray3, Helen G. Coleman3, Jacqueline A. James4, Manuel Salto‐Tellez4, Peter W. Hamilton1
1Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland, UK
2Centre for Experimental Medicine, Queen’s University Belfast, Belfast, Northern Ireland, UK
3Cancer Epidemiology and Health Services Research Group, Centre for Public Health, Queen’s University Belfast, Belfast, Northern Ireland, UK
4Tissue Pathology, Belfast Health and Social Care Trust, Belfast, Northern Ireland, Northern Ireland, UK

Tóm tắt

AbstractQuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.

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Tài liệu tham khảo

Pantanowitz, L. et al. Review of the current state of whole slide imaging in pathology. J. Pathol. Inform. 2, 36 (2011).

Hamilton, P. W. et al. Digital pathology and image analysis in tissue biomarker research. Methods 70, 59–73 (2014).

Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–82 (2012).

de Chaumont, F. et al. Icy: an open bioimage informatics platform for extended reproducible research. Nat. Methods 9, 690–6 (2012).

Lamprecht, M., Sabatini, D. & Carpenter, A. CellProfilerTM: free, versatile software for automated biological image analysis. Biotechniques 42, 71–75 (2007).

Carpenter, A. E., Kamentsky, L. & Eliceiri, K. W. A call for bioimaging software usability. Nat. Methods 9, 666–70 (2012).

Satyanarayanan, M., Goode, A., Gilbert, B., Harkes, J. & Jukic, D. OpenSlide: A vendor-neutral software foundation for digital pathology. J. Pathol. Inform. 4, 27 (2013).

Linkert, M. et al. Metadata matters: access to image data in the real world. J. Cell Biol. 189, 777–82 (2010).

Nelissen, B. G. L., van Herwaarden, Ja, Moll, F. L., van Diest, P. J. & Pasterkamp, G. SlideToolkit: An Assistive Toolset for the Histological Quantification of Whole Slide Images. PLoS One 9, e110289 (2014).

Tuominen, V. J., Ruotoistenmäki, S., Viitanen, A., Jumppanen, M. & Isola, J. ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67. Breast Cancer Res. 12, R56 (2010).

Marée, R. et al. Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics 32, 1395–1401 (2016).

Deroulers, C. et al. Analyzing huge pathology images with open source software. Diagn. Pathol. 8, 92 (2013).

Hamilton, P. W., Diest, P. J. Van, Williams, R. & Gallagher, A. G. Do we see what we think we see? The complexities of morphological assessment. 285–291, https://doi.org/10.1002/path (2009).

Polley, M.-Y. C. et al. An international Ki67 reproducibility study. J. Natl. Cancer Inst. 105, 1897–906 (2013).

Heindl, A., Nawaz, S. & Yuan, Y. Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology. Lab. Investig. 95, 377–384 (2015).

Ruifrok, A. C. & Johnston, D. A. Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol. 23, 291–9 (2001).

Galon, J. et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–4 (2006).

Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

Munro, A. J., Lain, S. & Lane, D. P. P53 abnormalities and outcomes in colorectal cancer: a systematic review. Br. J. Cancer 92, 434–44 (2005).

McCluggage, W. G., Soslow, R. A. & Gilks, C. B. Patterns of p53 immunoreactivity in endometrial carcinomas: ‘all or nothing’ staining is of importance. Histopathology 59, 786–788 (2011).

Boyle, D. P. et al. The prognostic significance of the aberrant extremes of p53 immunophenotypes in breast cancer. Histopathology 65, 1–13 (2014).

Garon, E. B. et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N. Engl. J. Med. 372, 2018–28 (2015).

Pyo, J.-S., Kang, G. & Kim, J. Y. Prognostic role of PD-L1 in malignant solid tumors: a meta-analysis. Int. J. Biol. Markers 0, https://doi.org/10.5301/JBM.2016.16048 (2016).

Dunne, P. D. et al. Immune-derived PD-L1 gene expression defines a subgroup of stage II/III colorectal cancer patients with favorable prognosis that may be harmed by adjuvant chemotherapy. Cancer Immunol. Res. 1–11, https://doi.org/10.1158/2326-6066.CIR-15-0302 (2016).

Lee, L. H. et al. Patterns and prognostic relevance of PD-1 and PD-L1 expression in colorectal carcinoma. Mod. Pathol. 29, 1433–1442 (2016).

Huijbers, A. et al. The proportion of tumor-stroma as a strong prognosticator for stage II and III colon cancer patients: Validation in the victor trial. Ann. Oncol. 24, 179–185 (2013).

Mesker, W. E. et al. The carcinoma-stromal ratio of colon carcinoma is an independent factor for survival compared to lymph node status and tumor stage. Cell Oncol 29, 387–398 (2007).

Altman, D. G., McShane, L. M., Sauerbrei, W. & Taube, S. E. Reporting Recommendations for Tumour Marker Prognostic studies (REMARK): Explanation and Elaboration. PLOS Med. 9, 1–32 (2012).

R Core Team. R: A Language and Environment for Statistical Computing. at https://www.r-project.org/ (2016).

Therneau, T. M. A Package for Survival Analysis in S. at https://cran.r-project.org/package=survival (2015).

Goulding, H. et al. A new immunohistochemical antibody for the assessment of estrogen receptor status on routine formalin-fixed tissue samples. Hum. Pathol. 26, 291–294 (1995).

Achanta, R. et al. SLIC Superpixels Compared to State-of-the-Art SuperpixelMethods. Pattern Anal. Mach. Intell. IEEE Trans. 34, 2274–2282 (2011).

Haralick, R. M., Shanmugam, K. & Dinstein, I. Textural Features for Image Classification. IEEE Trans. Syst. Man. Cybern. https://doi.org/10.1109/TSMC.1973.4309314 (1973).

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Tập 7 Số 1

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