{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T21:00:58Z","timestamp":1793912458935,"version":"4.8.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T00:00:00Z","timestamp":1602806400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T00:00:00Z","timestamp":1602806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["095898\/Z\/11\/Z"],"award-info":[{"award-number":["095898\/Z\/11\/Z"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Although gold-standard histological assessment is subjective it remains central to diagnosis and clinical trial protocols and is crucial for the evaluation of any preclinical disease model. Objectivity and reproducibility are enhanced by quantitative analysis of histological images but current methods require application-specific algorithm training and fail to extract understanding from the histological context of observable features. We reinterpret histopathological images as disease landscapes to describe a generalisable framework defining topographic relationships in tissue using geoscience approaches. The framework requires no user-dependent training to operate on all image datasets in a classifier-agnostic manner but is adaptable and scalable, able to quantify occult abnormalities, derive mechanistic insights, and define a new feature class for machine-learning diagnostic classification. We demonstrate application to inflammatory, fibrotic and neoplastic disease in multiple organs, including the detection and quantification of occult lobular enlargement in the liver secondary to hilar obstruction. We anticipate this approach will provide a robust class of histological data for trial stratification or endpoints, provide quantitative endorsement of experimental models of disease, and could be incorporated within advanced approaches to clinical diagnostic pathology.<\/jats:p>","DOI":"10.1038\/s41598-020-74691-9","type":"journal-article","created":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T10:02:55Z","timestamp":1602842575000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Integration of geoscience frameworks into digital pathology analysis permits quantification of microarchitectural relationships in histological landscapes"],"prefix":"10.1038","volume":"10","author":[{"given":"Timothy J.","family":"Kendall","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine M.","family":"Duff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew M.","family":"Thomson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John P.","family":"Iredale","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,10,16]]},"reference":[{"key":"74691_CR1","doi-asserted-by":"publisher","DOI":"10.1038\/s41571-019-0252-y","author":"K Bera","year":"2019","unstructured":"Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V. & Madabhushi, A. Artificial intelligence in digital pathology\u2014new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. https:\/\/doi.org\/10.1038\/s41571-019-0252-y (2019).","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"74691_CR2","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s13402-019-00429-z","volume":"42","author":"OGF Geessink","year":"2019","unstructured":"Geessink, O. G. F. et al. Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer. Cell. Oncol. 42, 331\u2013341 (2019).","journal-title":"Cell. Oncol."},{"key":"74691_CR3","doi-asserted-by":"publisher","first-page":"e1002730","DOI":"10.1371\/journal.pmed.1002730","volume":"16","author":"JN Kather","year":"2019","unstructured":"Kather, J. N. et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLOS Med. 16, e1002730 (2019).","journal-title":"PLOS Med."},{"key":"74691_CR4","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","volume":"16","author":"T Falk","year":"2019","unstructured":"Falk, T. et al. U-Net: Deep learning for cell counting, detection, and morphometry. Nat. Methods 16, 67 (2019).","journal-title":"Nat. Methods"},{"key":"74691_CR5","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.1038\/s41379-018-0073-z","volume":"31","author":"BE Bejnordi","year":"2018","unstructured":"Bejnordi, B. E. et al. Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies. Mod. Pathol. 31, 1502 (2018).","journal-title":"Mod. Pathol."},{"key":"74691_CR6","doi-asserted-by":"publisher","first-page":"2126","DOI":"10.1109\/TMI.2018.2820199","volume":"37","author":"D Tellez","year":"2018","unstructured":"Tellez, D. et al. Whole-slide mitosis detection in H E breast histology using PHH3 as a reference to train distilled stain-invariant convolutional networks. IEEE Trans. Med. Imaging 37, 2126\u20132136 (2018).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"74691_CR7","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0403-1","author":"E Moen","year":"2019","unstructured":"Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods https:\/\/doi.org\/10.1038\/s41592-019-0403-1 (2019).","journal-title":"Nat. Methods"},{"key":"74691_CR8","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1038\/nmeth.4391","volume":"14","author":"D Schapiro","year":"2017","unstructured":"Schapiro, D. et al. histoCAT: Analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat. Methods 14, 873\u2013876 (2017).","journal-title":"Nat. Methods"},{"key":"74691_CR9","unstructured":"McGarigal, K., Cushman, S. A. & Ene, E. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. (2012)."},{"key":"74691_CR10","doi-asserted-by":"publisher","DOI":"10.1101\/383281","author":"J Nowosad","year":"2018","unstructured":"Nowosad, J. & Stepinski, T. Information-theoretical approach to measuring landscape complexity. bioRxiv https:\/\/doi.org\/10.1101\/383281 (2018).","journal-title":"bioRxiv"},{"key":"74691_CR11","doi-asserted-by":"publisher","first-page":"2424","DOI":"10.1093\/bioinformatics\/btx180","volume":"33","author":"I Arganda-Carreras","year":"2017","unstructured":"Arganda-Carreras, I. et al. Trainable Weka Segmentation: A machine learning tool for microscopy pixel classification. Bioinform. Oxf. Engl. 33, 2424\u20132426 (2017).","journal-title":"Bioinform. Oxf. Engl."},{"key":"74691_CR12","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1038\/nmeth.2019","volume":"9","author":"J Schindelin","year":"2012","unstructured":"Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9, 676\u2013682 (2012).","journal-title":"Nat. Methods"},{"key":"74691_CR13","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1083\/jcb.201004104","volume":"189","author":"M Linkert","year":"2010","unstructured":"Linkert, M. et al. Metadata matters: Access to image data in the real world. J. Cell Biol. 189, 777\u2013782 (2010).","journal-title":"J. Cell Biol."},{"key":"74691_CR14","doi-asserted-by":"publisher","first-page":"255","DOI":"10.2307\/3212829","volume":"13","author":"BD Ripley","year":"1976","unstructured":"Ripley, B. D. The second-order analysis of stationary point processes. J. Appl. Probab. 13, 255\u2013266 (1976).","journal-title":"J. Appl. Probab."},{"key":"74691_CR15","first-page":"1238","volume":"21","author":"IR Wanless","year":"1995","unstructured":"Wanless, I. R. et al. Hepatic and portal vein thrombosis in cirrhosis: Possible role in development of parenchymal extinction and portal hypertension. Hepatology 21, 1238\u20131247 (1995).","journal-title":"Hepatology"},{"key":"74691_CR16","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1002\/hep.20476","volume":"40","author":"SL Friedman","year":"2004","unstructured":"Friedman, S. L. Stellate cells: A moving target in hepatic fibrogenesis. Hepatol. Baltim. Md 40, 1041\u20131043 (2004).","journal-title":"Hepatol. Baltim. Md"},{"key":"74691_CR17","doi-asserted-by":"publisher","first-page":"4688","DOI":"10.1038\/s41467-019-12701-9","volume":"10","author":"TJ Kendall","year":"2019","unstructured":"Kendall, T. J. et al. Embryonic mesothelial-derived hepatic lineage of quiescent and heterogenous scar-orchestrating cells defined but suppressed by WT1. Nat. Commun. 10, 4688 (2019).","journal-title":"Nat. Commun."},{"key":"74691_CR18","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1096\/fj.02-0494fje","volume":"17","author":"R Issa","year":"2003","unstructured":"Issa, R. et al. Mutation in collagen-1 that confers resistance to the action of collagenase results in failure of recovery from CCl4-induced liver fibrosis, persistence of activated hepatic stellate cells, and diminished hepatocyte regeneration. FASEB J. 17, 47\u201349 (2003).","journal-title":"FASEB J."},{"key":"74691_CR19","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1186\/1746-1596-8-92","volume":"8","author":"C Deroulers","year":"2013","unstructured":"Deroulers, C. et al. Analyzing huge pathology images with open source software. Diagn. Pathol. 8, 92 (2013).","journal-title":"Diagn. Pathol."},{"key":"74691_CR20","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1002\/mrd.22489","volume":"82","author":"J Schindelin","year":"2015","unstructured":"Schindelin, J., Rueden, C. T., Hiner, M. C. & Eliceiri, K. W. The ImageJ ecosystem: An open platform for biomedical image analysis. Mol. Reprod. Dev. 82, 518\u2013529 (2015).","journal-title":"Mol. Reprod. Dev."},{"key":"74691_CR21","doi-asserted-by":"publisher","first-page":"16878","DOI":"10.1038\/s41598-017-17204-5","volume":"7","author":"P Bankhead","year":"2017","unstructured":"Bankhead, P. et al. QuPath: Open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).","journal-title":"Sci. Rep."},{"key":"74691_CR22","unstructured":"R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2016)."},{"key":"74691_CR23","unstructured":"Sterratt, D. C. & Vihtakari, M. RImageJROI: Read \u2018ImageJ\u2019 Region of Interest (ROI) Files (2015). https:\/\/CRAN.R-project.org\/package=RImageJROI."},{"key":"74691_CR24","doi-asserted-by":"crossref","unstructured":"Baddeley, A., Rubak, E. & Turner, R. Spatial Point Patterns: Methodology and Applications with R. (Chapman and Hall\/CRC, 2015). https:\/\/cran.r-project.org\/package=spatstat.","DOI":"10.1201\/b19708"},{"key":"74691_CR25","unstructured":"GDAL\/OGR contributors. GDAL\/OGR Geospatial Data Abstraction software Library. (Open Source Geospatial Foundation, 2019)."},{"key":"74691_CR26","unstructured":"PROJ contributors. PROJ coordinate transformation software library. (Open Source Geospatial Foundation, 2019)."},{"key":"74691_CR27","unstructured":"Hesselbarth, M. H. K., Sciaini, M., Nowosad, J. & Hanss, S. landscapemetrics: Landscape Metrics for Categorical Map Patterns (2019). https:\/\/cran.r-project.org\/package=landscapemetrics."},{"key":"74691_CR28","doi-asserted-by":"crossref","unstructured":"Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics32, 2847\u20132849 (2016). https:\/\/bioconductor.org\/packages\/ComplexHeatmap\/.","DOI":"10.1093\/bioinformatics\/btw313"},{"key":"74691_CR29","unstructured":"Kuhn, M. et al. caret: Classification and Regression Training (2019). https:\/\/cran.r-project.org\/package=caret."},{"key":"74691_CR30","unstructured":"Peterson, R. A. bestNormalize: A suite of normalizing transformations (2017). https:\/\/cran.r-project.org\/package=bestNormalize."},{"key":"74691_CR31","unstructured":"Liaw, A. & Wiener, M. Classification and Regression by randomForest. R News2, 18\u201322 (2002). https:\/\/cran.r-project.org\/package=randomForest."},{"key":"74691_CR32","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. Random forests. Mach. Learn. 45, 5\u201332 (2001).","journal-title":"Mach. Learn."},{"key":"74691_CR33","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1198\/jasa.2009.tm08622","volume":"105","author":"H Ishwaran","year":"2010","unstructured":"Ishwaran, H., Kogalur, U. B., Gorodeski, E. Z., Minn, A. J. & Lauer, M. S. High-dimensional variable selection for survival data. J. Am. Stat. Assoc. 105, 205\u2013217 (2010).","journal-title":"J. Am. Stat. Assoc."},{"key":"74691_CR34","unstructured":"Paluszynska, A., Biecek, P. & Jiang, Y. randomForestExplainer: Explaining and Visualizing Random Forests in Terms of Variable Importance (2020). https:\/\/cran.r-project.org\/package=randomForestExplainer."},{"key":"74691_CR35","doi-asserted-by":"crossref","unstructured":"Sekhon, J. Multivariate and propensity score matching software with automated balance optimization: The matching package for R. J. Stat. Softw.42, 1\u201352 (2011). https:\/\/cran.r-project.org\/package=Matching.","DOI":"10.18637\/jss.v042.i07"},{"key":"74691_CR36","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1093\/beheco\/ark016","volume":"17","author":"GD Ruxton","year":"2006","unstructured":"Ruxton, G. D. The unequal variance t-test is an underused alternative to Student\u2019s t-test and the Mann-Whitney U test. Behav. Ecol. 17, 688\u2013690 (2006).","journal-title":"Behav. Ecol."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-74691-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-74691-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-74691-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T21:34:49Z","timestamp":1670362489000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-74691-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,16]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["74691"],"URL":"https:\/\/doi.org\/10.1038\/s41598-020-74691-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,16]]},"assertion":[{"value":"15 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17572"}}