{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T08:35:28Z","timestamp":1775032528698,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Mach Intell"],"DOI":"10.1038\/s42256-021-00303-4","type":"journal-article","created":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T17:02:59Z","timestamp":1615222979000},"page":"355-366","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":134,"title":["Morphological and molecular breast cancer profiling through explainable machine learning"],"prefix":"10.1038","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9605-6209","authenticated-orcid":false,"given":"Alexander","family":"Binder","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9249-4292","authenticated-orcid":false,"given":"Michael","family":"Bockmayr","sequence":"additional","affiliation":[]},{"given":"Miriam","family":"H\u00e4gele","sequence":"additional","affiliation":[]},{"given":"Stephan","family":"Wienert","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Heim","sequence":"additional","affiliation":[]},{"given":"Katharina","family":"Hellweg","sequence":"additional","affiliation":[]},{"given":"Masaru","family":"Ishii","sequence":"additional","affiliation":[]},{"given":"Albrecht","family":"Stenzinger","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Hocke","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2249-0982","authenticated-orcid":false,"given":"Carsten","family":"Denkert","sequence":"additional","affiliation":[]},{"given":"Klaus-Robert","family":"M\u00fcller","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9131-2389","authenticated-orcid":false,"given":"Frederick","family":"Klauschen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"303_CR1","doi-asserted-by":"publisher","first-page":"108ra113","DOI":"10.1126\/scitranslmed.3002564","volume":"3","author":"AH Beck","year":"2011","unstructured":"Beck, A. H. et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci. Transl. Med. 3, 108ra113 (2011).","journal-title":"Sci. Transl. Med."},{"key":"303_CR2","doi-asserted-by":"publisher","first-page":"a026583","DOI":"10.1101\/cshperspect.a026583","volume":"6","author":"Y Yuan","year":"2016","unstructured":"Yuan, Y. Spatial heterogeneity in the tumor microenvironment. Cold Spring Harb. Perspect. Med. 6, a026583 (2016).","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"303_CR3","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/j.immuni.2012.07.011","volume":"37","author":"MY Gerner","year":"2012","unstructured":"Gerner, M. Y., Kastenmuller, W., Ifrim, I., Kabat, J. & Germain, R. N. Histo-cytometry: a method for highly multiplex quantitative tissue imaging analysis applied to dendritic cell subset microanatomy in lymph nodes. Immunity 37, 364\u2013376 (2012).","journal-title":"Immunity"},{"key":"303_CR4","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1038\/nmeth.2896","volume":"11","author":"DL Rimm","year":"2014","unstructured":"Rimm, D. L. Next-gen immunohistochemistry. Nat. Methods 11, 381\u2013383 (2014).","journal-title":"Nat. Methods"},{"key":"303_CR5","doi-asserted-by":"publisher","unstructured":"Samek, W. & M\u00fcller, K.-R. in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (eds. Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K. & M\u00fcller, K.-R.) 5\u201322 (Springer, 2019); https:\/\/doi.org\/10.1007\/978-3-030-28954-6_1","DOI":"10.1007\/978-3-030-28954-6_1"},{"key":"303_CR6","doi-asserted-by":"publisher","first-page":"e0130140","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S. et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10, e0130140 (2015).","journal-title":"PLoS ONE"},{"key":"303_CR7","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/nature11412","volume":"490","author":"Cancer Genome Atlas Network.","year":"2012","unstructured":"Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61\u201370 (2012).","journal-title":"Nature"},{"key":"303_CR8","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1038\/s41591-019-0462-y","volume":"25","author":"JN Kather","year":"2019","unstructured":"Kather, J. N. et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25, 1054\u20131056 (2019).","journal-title":"Nat. Med."},{"key":"303_CR9","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1038\/s42256-019-0052-1","volume":"1","author":"Z Zhang","year":"2019","unstructured":"Zhang, Z. et al. Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nat. Mach. Intell. 1, 236\u2013245 (2019).","journal-title":"Nat. Mach. Intell."},{"key":"303_CR10","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","volume":"24","author":"N Coudray","year":"2018","unstructured":"Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559\u20131567 (2018).","journal-title":"Nat. Med."},{"key":"303_CR11","doi-asserted-by":"publisher","unstructured":"Andr\u00e9, B., Vercauteren, T., Buchner, A. M., Wallace, M. B. & Ayache, N. Endomicroscopic video retrieval using mosaicing and visualwords. In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 1419\u20131422 (2010); https:\/\/doi.org\/10.1109\/ISBI.2010.5490265","DOI":"10.1109\/ISBI.2010.5490265"},{"key":"303_CR12","doi-asserted-by":"crossref","unstructured":"Caicedo, J. C., Cruz, A. & Gonzalez, F. A. Histopathology image classification using bag of features and kernel functions. In Conference on Artificial Intelligence in Medicine in Europe 126\u2013135 (Springer, 2009).","DOI":"10.1007\/978-3-642-02976-9_17"},{"key":"303_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms12474","volume":"7","author":"K-H Yu","year":"2016","unstructured":"Yu, K.-H. et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat. Commun. 7, 12474 (2016).","journal-title":"Nat. Commun."},{"key":"303_CR14","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.semcancer.2018.07.001","volume":"52","author":"F Klauschen","year":"2018","unstructured":"Klauschen, F. et al. Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning. Semin. Cancer Biol. 52, 151\u2013157 (2018).","journal-title":"Semin. Cancer Biol."},{"key":"303_CR15","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y Benjamini","year":"1995","unstructured":"Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289\u2013300 (1995).","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"303_CR16","doi-asserted-by":"publisher","first-page":"7006","DOI":"10.1158\/1078-0432.CCR-17-0696","volume":"23","author":"M Sabbaghi","year":"2017","unstructured":"Sabbaghi, M. et al. Defective cyclin B1 induction in trastuzumab-emtansine (T-DM1) acquired resistance in HER2-positive breast cancer. Clin. Cancer Res. 23, 7006\u20137019 (2017).","journal-title":"Clin. Cancer Res."},{"key":"303_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41389-017-0002-x","volume":"6","author":"JC Harrell","year":"2017","unstructured":"Harrell, J. C., Shroka, T. M. & Jacobsen, B. M. Estrogen induces c-Kit and an aggressive phenotype in a model of invasive lobular breast cancer. Oncogenesis 6, 396 (2017).","journal-title":"Oncogenesis"},{"key":"303_CR18","doi-asserted-by":"publisher","first-page":"4365","DOI":"10.1158\/1078-0432.CCR-11-3028","volume":"18","author":"F Kuonen","year":"2012","unstructured":"Kuonen, F. et al. Inhibition of the Kit ligand\/c-Kit axis attenuates metastasis in a mouse model mimicking local breast cancer relapse after radiotherapy. Clin. Cancer Res. 18, 4365\u20134374 (2012).","journal-title":"Clin. Cancer Res."},{"key":"303_CR19","doi-asserted-by":"publisher","first-page":"e70746","DOI":"10.1371\/journal.pone.0070746","volume":"8","author":"Y Jiang","year":"2013","unstructured":"Jiang, Y., Zou, L., Lu, W.-Q., Zhang, Y. & Shen, A.-G. Foxo3a expression is a prognostic marker in breast cancer. PLoS ONE 8, e70746 (2013).","journal-title":"PLoS ONE"},{"key":"303_CR20","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1200\/JCO.18.01010","volume":"37","author":"S Loi","year":"2019","unstructured":"Loi, S. et al. Tumor-infiltrating lymphocytes and prognosis: a pooled individual patient analysis of early-stage triple-negative breast cancers. J. Clin. Oncol. 37, 559\u2013569 (2019).","journal-title":"J. Clin. Oncol."},{"key":"303_CR21","doi-asserted-by":"publisher","DOI":"10.1038\/s41523-020-0154-2","volume":"6","author":"M Amgad","year":"2020","unstructured":"Amgad, M. et al. Report on computational assessment of tumor infiltrating lymphocytes from the International Immuno-Oncology Biomarker Working Group. NPJ Breast Cancer 6, 16 (2020).","journal-title":"NPJ Breast Cancer"},{"key":"303_CR22","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1002\/path.5406","volume":"250","author":"PI Gonzalez-Ericsson","year":"2020","unstructured":"Gonzalez-Ericsson, P. I. et al. The path to a better biomarker: application of a risk management framework for the implementation of PD-L1 and TILs as immuno-oncology biomarkers in breast cancer clinical trials and daily practice. J. Pathol. 250, 667\u2013684 (2020).","journal-title":"J. Pathol."},{"key":"303_CR23","doi-asserted-by":"publisher","first-page":"256","DOI":"10.2307\/622936","volume":"21","author":"AC Gatrell","year":"1996","unstructured":"Gatrell, A. C., Bailey, T. C., Diggle, P. J. & Rowlingson, B. S. Spatial point pattern analysis and its application in geographical epidemiology. Trans. Inst. Br. Geogr. 21, 256\u2013274 (1996).","journal-title":"Trans. Inst. Br. Geogr."},{"key":"303_CR24","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1002\/cjp2.25","volume":"1","author":"J Budczies","year":"2015","unstructured":"Budczies, J. et al. Classical pathology and mutational load of breast cancer\u2013integration of two worlds. J. Pathol. Clin. Res. 1, 225\u2013238 (2015).","journal-title":"J. Pathol. Clin. Res."},{"key":"303_CR25","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/S1470-2045(17)30904-X","volume":"19","author":"C Denkert","year":"2018","unstructured":"Denkert, C. et al. Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy. Lancet Oncol. 19, 40\u201350 (2018).","journal-title":"Lancet Oncol."},{"key":"303_CR26","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","volume":"2","author":"MN Gurcan","year":"2009","unstructured":"Gurcan, M. N. et al. Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147\u2013171 (2009).","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"303_CR27","doi-asserted-by":"crossref","unstructured":"Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K. & M\u00fcller, K.-R. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (Springer, 2019).","DOI":"10.1007\/978-3-030-28954-6"},{"key":"303_CR28","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-08987-4","volume":"10","author":"S Lapuschkin","year":"2019","unstructured":"Lapuschkin, S. et al. Unmasking clever Hans predictors and assessing what machines really learn. Nat. Commun. 10, 1096 (2019).","journal-title":"Nat. Commun."},{"key":"303_CR29","unstructured":"Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J. & M\u00fcller, K.-R. Toward interpretable machine learning: transparent deep neural networks and beyond. Preprint at https:\/\/arxiv.org\/abs\/2003.07631 (2020)."},{"key":"303_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","volume":"73","author":"G Montavon","year":"2018","unstructured":"Montavon, G., Samek, W. & M\u00fcller, K.-R. Methods for interpreting and understanding deep neural networks. Digital Signal Process. 73, 1\u201315 (2018).","journal-title":"Digital Signal Process."},{"key":"303_CR31","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/72.914517","volume":"12","author":"K-R M\u00fcller","year":"2001","unstructured":"M\u00fcller, K.-R., Mika, S., R\u00e4tsch, G., Tsuda, K. & Sch\u00f6lkopf, B. An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12, 181\u2013201 (2001).","journal-title":"IEEE Trans. Neural Netw."},{"key":"303_CR32","unstructured":"Csurka, G., Dance, C., Fan, L., Willamowski, J. & Bray, C. Visual categorization with bags of keypoints. In Workshop on Statistical Learning in Computer Vision (2004)."},{"key":"303_CR33","first-page":"1799","volume":"11","author":"S Sonnenburg","year":"2010","unstructured":"Sonnenburg, S. et al. The SHOGUN machine learning toolbox. J. Mach. Learn. Res. 11, 1799\u20131802 (2010).","journal-title":"J. Mach. Learn. Res."},{"key":"303_CR34","doi-asserted-by":"crossref","unstructured":"Lapuschkin, S., Binder, A., Montavon, G., Muller, K.-R. & Samek, W. Analyzing classifiers: Fisher vectors and deep neural networks. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2912\u20132920 (2016).","DOI":"10.1109\/CVPR.2016.318"},{"key":"303_CR35","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.cviu.2012.09.006","volume":"117","author":"A Binder","year":"2013","unstructured":"Binder, A., Samek, W., M\u00fcller, K.-R. & Kawanabe, M. Enhanced representation and multi-task learning for image annotation. Comput. Vision Image Understanding 117, 466\u2013478 (2013).","journal-title":"Comput. Vision Image Understanding"},{"key":"303_CR36","doi-asserted-by":"crossref","unstructured":"Bishop, C. M. Neural Networks for Pattern Recognition (Oxford Univ. Press, 1995).","DOI":"10.1201\/9781420050646.ptb6"},{"key":"303_CR37","doi-asserted-by":"crossref","unstructured":"Zien, A. & Ong, C. S. Multiclass multiple kernel learning. in Proc. 24th International Conference on Machine Learning 1191\u20131198 (2007).","DOI":"10.1145\/1273496.1273646"},{"key":"303_CR38","unstructured":"Raschka, S. Model evaluation, model selection, and algorithm selection in machine learning. Preprint at https:\/\/arxiv.org\/abs\/1811.12808 (2018)."},{"key":"303_CR39","doi-asserted-by":"crossref","unstructured":"Hoeffding, W. Probability inequalities for sums of bounded random variables. J. Am. Stat. Assoc. 58, 13\u201330 (1963).","DOI":"10.1080\/01621459.1963.10500830"},{"key":"303_CR40","doi-asserted-by":"publisher","unstructured":"Binder, A. & Bockmayr, M. Morphological and molecular breast cancer profiling through explainable machine learning. figshare https:\/\/doi.org\/10.6084\/m9.figshare.13078835 (2021).","DOI":"10.6084\/m9.figshare.13078835"}],"container-title":["Nature Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00303-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00303-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00303-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T17:02:13Z","timestamp":1724605333000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00303-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,8]]},"references-count":40,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["303"],"URL":"https:\/\/doi.org\/10.1038\/s42256-021-00303-4","relation":{},"ISSN":["2522-5839"],"issn-type":[{"value":"2522-5839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,8]]},"assertion":[{"value":"6 June 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2021","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"}}]}}