{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:57:36Z","timestamp":1769637456730,"version":"3.49.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T00:00:00Z","timestamp":1678320000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T00:00:00Z","timestamp":1678320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["SFRH\/BD\/139108\/2018"],"award-info":[{"award-number":["SFRH\/BD\/139108\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["2021.06872.BD"],"award-info":[{"award-number":["2021.06872.BD"]}],"id":[{"id":"10.13039\/501100001871","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>Cervical cancer is the fourth most common female cancer worldwide and the fourth leading cause of cancer-related death in women. Nonetheless, it is also among the most successfully preventable and treatable types of cancer, provided it is early identified and properly managed. As such, the detection of pre-cancerous lesions is crucial. These lesions are detected in the squamous epithelium of the uterine cervix and are graded as low- or high-grade intraepithelial squamous lesions, known as LSIL and HSIL, respectively. Due to their complex nature, this classification can become very subjective. Therefore, the development of machine learning models, particularly directly on whole-slide images (WSI), can assist pathologists in this task. In this work, we propose a weakly-supervised methodology for grading cervical dysplasia, using different levels of training supervision, in an effort to gather a bigger dataset without the need of having all samples fully annotated. The framework comprises an epithelium segmentation step followed by a dysplasia classifier (non-neoplastic, LSIL, HSIL), making the slide assessment completely automatic, without the need for manual identification of epithelial areas. The proposed classification approach achieved a balanced accuracy of 71.07% and sensitivity of 72.18%, at the slide-level testing on 600 independent samples, which are publicly available upon reasonable request.<\/jats:p>","DOI":"10.1038\/s41598-023-30497-z","type":"journal-article","created":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T19:33:48Z","timestamp":1679859228000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A CAD system for automatic dysplasia grading on H&amp;E cervical whole-slide images"],"prefix":"10.1038","volume":"13","author":[{"given":"Sara P.","family":"Oliveira","sequence":"first","affiliation":[]},{"given":"Diana","family":"Montezuma","sequence":"additional","affiliation":[]},{"given":"Ana","family":"Moreira","sequence":"additional","affiliation":[]},{"given":"Domingos","family":"Oliveira","sequence":"additional","affiliation":[]},{"given":"Pedro C.","family":"Neto","sequence":"additional","affiliation":[]},{"given":"Ana","family":"Monteiro","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Monteiro","sequence":"additional","affiliation":[]},{"given":"Liliana","family":"Ribeiro","sequence":"additional","affiliation":[]},{"given":"Sofia","family":"Gon\u00e7alves","sequence":"additional","affiliation":[]},{"given":"Isabel M.","family":"Pinto","sequence":"additional","affiliation":[]},{"given":"Jaime S.","family":"Cardoso","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"30497_CR1","unstructured":"Ferlay, J. et\u00a0al. Global cancer observatory: Cancer today. https:\/\/gco.iarc.fr (2020). Accessed 19 Sep 2022."},{"key":"30497_CR2","unstructured":"WHO Classification of Tumours Editorial Board. Female Genital Tumours. Medicine Series (International Agency for Research on Cancer, 2020)."},{"issue":"7","key":"30497_CR3","doi-asserted-by":"publisher","first-page":"e510","DOI":"10.1016\/S2468-2667(21)00046-3","volume":"6","author":"M Bonjour","year":"2021","unstructured":"Bonjour, M. et al. Global estimates of expected and preventable cervical cancers among girls born between 2005 and 2014: A birth cohort analysis. Lancet Public Health 6(7), e510\u2013e521. https:\/\/doi.org\/10.1016\/S2468-2667(21)00046-3 (2021).","journal-title":"Lancet Public Health"},{"issue":"4","key":"30497_CR4","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1136\/ijgc-2020-001285","volume":"30","author":"M Gultekin","year":"2020","unstructured":"Gultekin, M., Ramirez, P. T., Broutet, N. & Hutubessy, R. World health organization call for action to eliminate cervical cancer globally. Int. J. Gynecol. Cancer 30(4), 426\u2013427. https:\/\/doi.org\/10.1136\/ijgc-2020-001285 (2020).","journal-title":"Int. J. Gynecol. Cancer"},{"key":"30497_CR5","unstructured":"World Health Organization (WHO). Global strategy to accelerate the elimination of cervical cancer as a public health problem and its associated goals and targets for the period 2020\u20132030 (2020)."},{"issue":"5","key":"30497_CR6","doi-asserted-by":"publisher","first-page":"e582","DOI":"10.1016\/S2214-109X(20)30522-2","volume":"9","author":"NM Rodriguez","year":"2021","unstructured":"Rodriguez, N. M. Participatory innovation for human papillomavirus screening to accelerate the elimination of cervical cancer. Lancet Glob. Health 9(5), e582\u2013e583. https:\/\/doi.org\/10.1016\/S2214-109X(20)30522-2\u00a0(2021).","journal-title":"Lancet Glob. Health"},{"key":"30497_CR7","doi-asserted-by":"publisher","first-page":"46450","DOI":"10.1038\/srep46450","volume":"7","author":"A Cruz-Roa","year":"2017","unstructured":"Cruz-Roa, A. et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: A deep learning approach for quantifying tumor extent. Sci. Rep. 7, 46450. https:\/\/doi.org\/10.1038\/srep46450 (2017).","journal-title":"Sci. Rep."},{"issue":"8","key":"30497_CR8","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","volume":"25","author":"G Campanella","year":"2019","unstructured":"Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25(8), 1301\u20131309. https:\/\/doi.org\/10.1038\/s41591-019-0508-1 (2019).","journal-title":"Nat. Med."},{"issue":"6","key":"30497_CR9","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu, M. Y. et al. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5(6), 555\u2013570. https:\/\/doi.org\/10.1038\/s41551-020-00682-w\u00a0(2021).","journal-title":"Nat. Biomed. Eng."},{"key":"30497_CR10","doi-asserted-by":"publisher","unstructured":"Bulten, W. et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 21(2). https:\/\/doi.org\/10.1016\/S1470-2045(19)30739-9\u00a0(2020).","DOI":"10.1016\/S1470-2045(19)30739-9"},{"issue":"10","key":"30497_CR11","doi-asserted-by":"publisher","first-page":"2489","DOI":"10.3390\/cancers14102489","volume":"14","author":"PC Neto","year":"2022","unstructured":"Neto, P. C. et al. iMIL4PATH: A semi-supervised interpretable approach for colorectal whole-slide images. Cancers 14(10), 2489. https:\/\/doi.org\/10.3390\/cancers14102489\u00a0(2022).","journal-title":"Cancers"},{"issue":"1","key":"30497_CR12","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/TMI.2022.3202248","volume":"42","author":"P Huang","year":"2023","unstructured":"Huang, P. et al. A ViT-AMC network with adaptive model fusion and multiobjective optimization for interpretable laryngeal tumor grading from histopathological images. IEEE Trans. Med. Imaging 42(1), 15\u201328. https:\/\/doi.org\/10.1109\/TMI.2022.3202248 (2023).","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"30497_CR13","doi-asserted-by":"publisher","first-page":"1500","DOI":"10.1001\/jama.285.11.1500","volume":"285","author":"MH Stoler","year":"2001","unstructured":"Stoler, M. H. et al. Interobserver reproducibility of cervical cytologic and histologic interpretations: Realistic estimates from the ASCUS-LSIL triage study. JAMA 285(11), 1500\u20131505. https:\/\/doi.org\/10.1001\/jama.285.11.1500\u00a0(2001).","journal-title":"JAMA"},{"issue":"8","key":"30497_CR14","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1097\/PAS.0b013e3181e8b2c4","volume":"34","author":"MT Galgano","year":"2010","unstructured":"Galgano, M. T. et al. Using biomarkers as objective standards in the diagnosis of cervical biopsies. Am. J. Surg. Pathol. 34(8), 1077\u20131087. https:\/\/doi.org\/10.1097\/PAS.0b013e3181e8b2c4 \u00a0(2010).","journal-title":"Am. J. Surg. Pathol."},{"key":"30497_CR15","doi-asserted-by":"publisher","unstructured":"Hou, X. et al. Artificial intelligence in cervical cancer screening and diagnosis. Front. Oncol. 12. https:\/\/doi.org\/10.3389\/fonc.2022.851367 (2022).","DOI":"10.3389\/fonc.2022.851367"},{"key":"30497_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102197","volume":"73","author":"L Cao","year":"2021","unstructured":"Cao, L. et al. A novel attention-guided convolutional network for the detection of abnormal cervical cells in cervical cancer screening. Med. Image Anal. 73, 102197. https:\/\/doi.org\/10.1016\/j.media.2021.102197 (2021).","journal-title":"Med. Image Anal."},{"issue":"1","key":"30497_CR17","doi-asserted-by":"publisher","first-page":"16244","DOI":"10.1038\/s41598-021-95545-y","volume":"11","author":"C-W Wang","year":"2021","unstructured":"Wang, C.-W. et al. Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning. Sci. Rep. 11(1), 16244. https:\/\/doi.org\/10.1038\/s41598-021-95545-y\u00a0(2021).","journal-title":"Sci. Rep."},{"key":"30497_CR18","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1159\/000455774","volume":"25","author":"L Pantanowitz","year":"2020","unstructured":"Pantanowitz, L. & Bui, M. M. Computer-assisted pap test screening. Mod. Tech. Cytopathol. 25, 67\u201374. https:\/\/doi.org\/10.1159\/000455774\u00a0(2020).","journal-title":"Mod. Tech. Cytopathol."},{"key":"30497_CR19","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.pvr.2015.06.006","volume":"1","author":"L von Karsa","year":"2015","unstructured":"von Karsa, L. et al. European guidelines for quality assurance in cervical cancer screening. Summary of the supplements on HPV screening and vaccination. Papillomavirus Res. 1, 22\u201331. https:\/\/doi.org\/10.1016\/j.pvr.2015.06.006\u00a0(2015).","journal-title":"Papillomavirus Res."},{"key":"30497_CR20","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1001\/jama.2018.10897","volume":"320","author":"SJ Curry","year":"2018","unstructured":"Curry, S. J. et al. Screening for cervical cancer: US preventive services task force recommendation statement. JAMA 320(7), 674\u2013686. https:\/\/doi.org\/10.1001\/jama.2018.10897\u00a0(2018).","journal-title":"JAMA"},{"issue":"5","key":"30497_CR21","doi-asserted-by":"publisher","first-page":"321","DOI":"10.3322\/caac.21628","volume":"70","author":"ET Fontham","year":"2020","unstructured":"Fontham, E. T. et al. Cervical cancer screening for individuals at average risk: 2020 guideline update from the American Cancer Society. CA Cancer J. Clin. 70(5), 321\u2013346. https:\/\/doi.org\/10.3322\/caac.21628 (2020).","journal-title":"CA Cancer J. Clin."},{"key":"30497_CR22","doi-asserted-by":"publisher","first-page":"4821","DOI":"10.1007\/s10462-020-09808-7","volume":"53","author":"C Li","year":"2020","unstructured":"Li, C. et al. A review for cervical histopathology image analysis using machine vision approaches. Artif. Intell. Rev. 53, 4821\u20134862. https:\/\/doi.org\/10.1007\/s10462-020-09808-7\u00a0(2020).","journal-title":"Artif. Intell. Rev."},{"key":"30497_CR23","doi-asserted-by":"publisher","unstructured":"Li, C. et al. Transfer learning based classification of cervical cancer immunohistochemistry images. In Proceedings of the Third International Symposium on Image Computing and Digital Medicine (ISICDM 2019), 102\u2013106. https:\/\/doi.org\/10.1145\/3364836.3364857 (2019).","DOI":"10.1145\/3364836.3364857"},{"key":"30497_CR24","doi-asserted-by":"publisher","unstructured":"Li, C. et al. Weakly supervised cervical histopathological image classification using multilayer hidden conditional random fields. In International Conference on Information Technologies in Biomedicine (ITIB 2019), \n209\u2013221. https:\/\/doi.org\/10.1007\/978-3-030-23762-2_19 (2019).","DOI":"10.1007\/978-3-030-23762-2_19"},{"key":"30497_CR25","doi-asserted-by":"publisher","unstructured":"Xue, Y. et al. Synthetic augmentation and feature-based filtering for improved cervical histopathology image classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2019), 387\u2013396. https:\/\/doi.org\/10.1007\/978-3-030-32239-7_43 (2019).","DOI":"10.1007\/978-3-030-32239-7_43"},{"key":"30497_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101816","volume":"67","author":"Y Xue","year":"2021","unstructured":"Xue, Y. et al. Selective synthetic augmentation with histoGAN for improved histopathology image classification. Med. Image Anal. 67, 101816. https:\/\/doi.org\/10.1016\/j.media.2020.101816\u00a0(2021).","journal-title":"Med. Image Anal."},{"key":"30497_CR27","doi-asserted-by":"publisher","first-page":"24219","DOI":"10.1109\/ACCESS.2020.2970121","volume":"8","author":"P Huang","year":"2020","unstructured":"Huang, P. et al. Classification of cervical biopsy images based on LASSO and EL-SVM. IEEE Access 8, 24219\u201324228. https:\/\/doi.org\/10.1109\/ACCESS.2020.2970121 (2020).","journal-title":"IEEE Access"},{"key":"30497_CR28","doi-asserted-by":"publisher","first-page":"40","DOI":"10.4103\/jpi.jpi_50_20","volume":"11","author":"S Sornapudi","year":"2020","unstructured":"Sornapudi, S. et al.  DeepCIN: Attention-Based Cervical histology Image Classification with Sequential Feature Modeling for Pathologist-Level Accuracy. J Pathol Inform 11, 40. https:\/\/doi.org\/10.4103\/jpi.jpi_50_20 (2020).","journal-title":"J Pathol Inform"},{"issue":"1","key":"30497_CR29","doi-asserted-by":"publisher","first-page":"122","DOI":"10.3390\/s21010122","volume":"21","author":"P Huang","year":"2021","unstructured":"Huang, P., Tan, X., Chen, C., Lv, X. & Li, Y. AF-SEnet: Classification of cancer in cervical tissue pathological images based on fusing deep convolution features. Sensors 21(1), 122. https:\/\/doi.org\/10.3390\/s21010122 (2021).","journal-title":"Sensors"},{"key":"30497_CR30","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.1007\/s11517-021-02388-w","volume":"59","author":"A Albayrak","year":"2021","unstructured":"Albayrak, A. et al. A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability. Med. Biol. Eng. Comput. 59, 1545\u20131561. https:\/\/doi.org\/10.1007\/s11517-021-02388-w\u00a0(2021).","journal-title":"Med. Biol. Eng. Comput."},{"issue":"2","key":"30497_CR31","doi-asserted-by":"publisher","first-page":"548","DOI":"10.3390\/diagnostics12020548","volume":"12","author":"B-J Cho","year":"2022","unstructured":"Cho, B.-J. et al. Automated diagnosis of cervical intraepithelial neoplasia in histology images via deep learning. Diagnostics 12(2), 548. https:\/\/doi.org\/10.3390\/diagnostics12020548\u00a0(2022).","journal-title":"Diagnostics"},{"key":"30497_CR32","doi-asserted-by":"publisher","first-page":"163","DOI":"10.2147\/MDER.S366303","volume":"15","author":"LW Habtemariam","year":"2022","unstructured":"Habtemariam, L. W., Zewde, E. T. & Simegn, G. L. Cervix type and cervical cancer classification system using deep learning techniques. Med. Devices (Auckland, NZ) 15, 163. https:\/\/doi.org\/10.2147\/MDER.S366303\u00a0(2022).","journal-title":"Med. Devices (Auckland, NZ)"},{"issue":"1","key":"30497_CR33","doi-asserted-by":"publisher","first-page":"26","DOI":"10.4103\/jpi.jpi_52_20","volume":"12","author":"S Sornapudi","year":"2021","unstructured":"Sornapudi, S. et al. Automated cervical digitized histology whole-slide image analysis toolbox. J. Pathol. Inform. 12(1), 26. https:\/\/doi.org\/10.4103\/jpi.jpi_52_20 (2021).","journal-title":"J. Pathol. Inform."},{"key":"30497_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.modpat.2022.100086","volume":"20","author":"D Montezuma","year":"2023","unstructured":"Montezuma, D. et al. Annotating for artificial intelligence applications in digital pathology: A practical guide for pathologists and researchers. Mod. Pathol. 20, 100086.https:\/\/doi.org\/10.1016\/j.modpat.2022.100086 (2023).","journal-title":"Mod. Pathol."},{"key":"30497_CR35","unstructured":"Pathcore. Sedeen viewer. https:\/\/pathcore.com\/sedeen (2020)."},{"key":"30497_CR36","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015), vol. 9351 of LNCS, 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28 (Springer, 2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"30497_CR37","doi-asserted-by":"publisher","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","volume":"9","author":"N Siddique","year":"2021","unstructured":"Siddique, N., Paheding, S., Elkin, C. P. & Devabhaktuni, V. U-net and its variants for medical image segmentation: A review of theory and applications. IEEE Access 9, 82031\u201382057. https:\/\/doi.org\/10.1109\/ACCESS.2021.3086020 (2021).","journal-title":"IEEE Access"},{"key":"30497_CR38","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1155\/2022\/4189781","volume":"1\u201316","author":"X-X Yin","year":"2022","unstructured":"Yin, X.-X., Sun, L., Fu, Y., Lu, R. & Zhang, Y. U-net-based medical image segmentation. J. Healthc. Eng. 1\u201316, 2022. https:\/\/doi.org\/10.1155\/2022\/4189781 (2022).","journal-title":"J. Healthc. Eng."},{"key":"30497_CR39","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62\u201366. https:\/\/doi.org\/10.1109\/TSMC.1979.4310076 (1979).","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"30497_CR40","doi-asserted-by":"publisher","first-page":"14358","DOI":"10.1038\/s41598-021-93746-z","volume":"11","author":"SP Oliveira","year":"2021","unstructured":"Oliveira, S. P. et al. CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance. Sci. Rep. 11, 14358. https:\/\/doi.org\/10.1038\/s41598-021-93746-z (2021).","journal-title":"Sci. Rep."},{"key":"30497_CR41","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1037\/h0026256","volume":"70","author":"J Cohen","year":"1968","unstructured":"Cohen, J. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychol. Bull. 70, 213\u2013220. https:\/\/doi.org\/10.1037\/h0026256 (1968).","journal-title":"Psychol. Bull."},{"key":"30497_CR42","unstructured":"Kingma, D.\u00a0P. & Ba, J. Adam: A method for stochastic optimization. arXiv:1412.6980 (arXiv preprint) (2014)."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-30497-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-30497-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-30497-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T19:38:05Z","timestamp":1679859485000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-30497-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,9]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["30497"],"URL":"https:\/\/doi.org\/10.1038\/s41598-023-30497-z","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,9]]},"assertion":[{"value":"6 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"D.M., D.O., A.M., J.M., L.R. and S.G. are employees at IMP Diagnostics; I.M.P. is an owner at IMP Diagnostics. The remaining authors declare no conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"3970"}}