{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T20:02:54Z","timestamp":1779220974346,"version":"3.51.4"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T00:00:00Z","timestamp":1707868800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T00:00:00Z","timestamp":1707868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This article proposes a novel concept for a two-step Ki-67\/lymphocytes classification cell detection pipeline on Ki-67 stained histopathological slides utilizing commonly available and undedicated, in terms of the medical problem considered, deep learning models. Models used vary in implementation, complexity, and applications, allowing for the use of a dedicated architecture depending on the physician\u2019s needs. Moreover, generic models\u2019 performance was compared with the problem-dedicated one. Experiments highlight that with relatively small training datasets, commonly used architectures for instance segmentation and object detection are competitive with a dedicated model. To ensure generalization and minimize biased sampling, experiments were performed on data derived from two unrelated histopathology laboratories.<\/jats:p>","DOI":"10.1007\/s10462-023-10699-7","type":"journal-article","created":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T09:02:21Z","timestamp":1707901341000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep learning for cancer cell detection: do we need dedicated models?"],"prefix":"10.1007","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3523-1031","authenticated-orcid":false,"given":"Michal","family":"Karol","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7687-9002","authenticated-orcid":false,"given":"Martin","family":"Tabakov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7606-3057","authenticated-orcid":false,"given":"Urszula","family":"Markowska-Kaczmar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4858-2022","authenticated-orcid":false,"given":"Lukasz","family":"Fulawka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,14]]},"reference":[{"issue":"9","key":"10699_CR1","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1145\/361002.361007","volume":"18","author":"JL Bentley","year":"1975","unstructured":"Bentley JL (1975) Multidimensional binary search trees used for associative searching. Commun ACM 18(9):509\u2013517. https:\/\/doi.org\/10.1145\/361002.361007","journal-title":"Commun ACM"},{"key":"10699_CR2","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv:2004.10934"},{"key":"10699_CR3","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2019.00264","author":"N Dimitriou","year":"2019","unstructured":"Dimitriou N, Arandjelovi\u0107 O, Caie PD (2019) Deep learning for whole slide image analysis: an overview. Front Med. https:\/\/doi.org\/10.3389\/fmed.2019.00264","journal-title":"Front Med"},{"key":"10699_CR4","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00376-2","author":"KA Esteva","year":"2021","unstructured":"Esteva KA, Chou Yeung S, Naik N et al (2021) Deep learning-enabled medical computer vision. NPJ Digit Med. https:\/\/doi.org\/10.1038\/s41746-020-00376-2","journal-title":"NPJ Digit Med"},{"issue":"1","key":"10699_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13000-020-00957-5","volume":"15","author":"M Feng","year":"2020","unstructured":"Feng M, Deng Y, Yang L et al (2020) Automated quantitative analysis of Ki-67 staining and he images recognition and registration based on whole tissue sections in breast carcinoma. Diagn Pathol 15(1):1\u201312","journal-title":"Diagn Pathol"},{"key":"10699_CR6","first-page":"3965","volume":"36","author":"L Fulawka","year":"2016","unstructured":"Fulawka L, Halon A (2016) Proliferation index evaluation in breast cancer using imageJ and immunoratio applications. Anticancer Res 36:3965\u201372","journal-title":"Anticancer Res"},{"issue":"2","key":"10699_CR7","doi-asserted-by":"publisher","first-page":"177","DOI":"10.4103\/IJPM.IJPM_732_15","volume":"60","author":"L Fulawka","year":"2017","unstructured":"Fulawka L, Halon A (2017) Ki-67 evaluation in breast cancer: the daily diagnostic practice. Indian J Pathol Microbiol 60(2):177\u2013184. https:\/\/doi.org\/10.4103\/IJPM.IJPM_732_15","journal-title":"Indian J Pathol Microbiol"},{"key":"10699_CR8","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-06555-3","author":"L Fulawka","year":"2022","unstructured":"Fulawka L, Blaszczyk J, Tabakov M et al (2022) Assessment of Ki-67 proliferation index with deep learning in DCIS (ductal carcinoma in situ). Sci Rep. https:\/\/doi.org\/10.1038\/s41598-022-06555-3","journal-title":"Sci Rep"},{"key":"10699_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13010011","author":"RS Geread","year":"2021","unstructured":"Geread RS, Sivanandarajah A, Brouwer ER et al (2021) Pinet-an automated proliferation index calculator framework for Ki67 breast cancer images. Cancers. https:\/\/doi.org\/10.3390\/cancers13010011","journal-title":"Cancers"},{"key":"10699_CR10","doi-asserted-by":"publisher","unstructured":"Girshick R (2015) Fast R-CNN. In: Proceedings of the 2015 IEEE international conference on computer vision (ICCV). IEEE Computer Society, USA, ICCV \u201915, pp 1440\u20131448. https:\/\/doi.org\/10.1109\/ICCV.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"issue":"1","key":"10699_CR11","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1038\/s41598-020-67880-z","volume":"10","author":"D Govind","year":"2020","unstructured":"Govind D, Jen KY, Matsukuma K et al (2020) Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning. Sci Rep 10(1):2045\u20132322. https:\/\/doi.org\/10.1038\/s41598-020-67880-z","journal-title":"Sci Rep"},{"key":"10699_CR12","unstructured":"He K, Zhang X, Ren S et al (2015) Deep residual learning for image recognition. CoRR abs\/1512.03385. https:\/\/arxiv.org\/abs\/arXiv:1512.03385"},{"key":"10699_CR13","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P et\u00a0al (2017) Mask R-CNN. In: 2017 IEEE international conference on computer vision (ICCV), pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.322"},{"key":"10699_CR14","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Weinberger KQ (2016) Densely connected convolutional networks. CoRR abs\/1608.06993. https:\/\/arxiv.org\/abs\/arXiv:1608.06993","DOI":"10.1109\/CVPR.2017.243"},{"key":"10699_CR15","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1007\/s10549-013-2560-8","volume":"139","author":"EC Inwald","year":"2013","unstructured":"Inwald EC, Klinkhammer-Schalke M, Hofstaedter F et al (2013) Ki-67 is a prognostic parameter in breast cancer patients: results of a large population-based cohort of a cancer registry. Breast Cancer Res Treat 139:539\u2013552","journal-title":"Breast Cancer Res Treat"},{"key":"10699_CR16","doi-asserted-by":"publisher","unstructured":"Iqbal I, Younus M,Walayat K et al (2021) Automated multi-class classification\nof skin lesions through deep convolutional neural network with dermoscopic\nimages. Comput Med Imaging Graph 88:101,843. https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101843","DOI":"10.1016\/j.compmedimag.2020.101843"},{"key":"10699_CR17","doi-asserted-by":"crossref","unstructured":"Kammerer-Jacquet SF, Ahmad A, ller H et al (2019) Ki-67 is an independent predictor of prostate cancer death in routine needle biopsy samples: proving utility for routine assessments. Mod Pathol 32(9):1303\u20131309","DOI":"10.1038\/s41379-019-0268-y"},{"key":"10699_CR18","unstructured":"Kingma DP, Ba J (2017) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"key":"10699_CR19","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L et al (eds) Advances in neural information processing systems, vol 25. Curran Associates Inc., New York, pp 1097\u20131105"},{"key":"10699_CR20","doi-asserted-by":"publisher","unstructured":"Kuprieiev R, Skshetry, Rowlands P et al (2023) DVC: data version control\u2014git for data & models. https:\/\/doi.org\/10.5281\/zenodo.7990791","DOI":"10.5281\/zenodo.7990791"},{"key":"10699_CR21","doi-asserted-by":"crossref","unstructured":"LeCun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, pp 2278\u20132324","DOI":"10.1109\/5.726791"},{"key":"10699_CR22","doi-asserted-by":"publisher","unstructured":"Li X, Xu Y, Xiang F et al (2021) Kinet: a non-invasive method for predicting Ki67 index of glioma. In: 2021 IEEE international conference on image processing (ICIP), pp 150\u2013154. https:\/\/doi.org\/10.1109\/ICIP42928.2021.9506741","DOI":"10.1109\/ICIP42928.2021.9506741"},{"issue":"20","key":"10699_CR23","first-page":"1746","volume":"17","author":"L Li","year":"2022","unstructured":"Li L, Han D, Yu Y et al (2022) Artificial intelligence-assisted interpretation of Ki-67 expression and repeatability in breast cancer. Diagn Pathol 17(20):1746\u20131796","journal-title":"Diagn Pathol"},{"key":"10699_CR24","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick RB et\u00a0al (2017) Focal loss for dense object detection. 2017 IEEE international conference on computer vision (ICCV), pp 2999\u20133007","DOI":"10.1109\/ICCV.2017.324"},{"key":"10699_CR25","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer vision\u2014ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu W, Anguelov D, Erhan D et al (2016) SSD: single shot multibox detector. In: Leibe B, Matas J, Sebe N et al (eds) Computer vision\u2014ECCV 2016. Springer, Cham, pp 21\u201337"},{"key":"10699_CR26","doi-asserted-by":"publisher","DOI":"10.3389\/fmolb.2020.00183","author":"Y Liu","year":"2020","unstructured":"Liu Y, Li X, Zheng A et al (2020) Predict Ki-67 positive cells in H &E-stained images using deep learning independently from IHC-stained images. Front Mol Biosci. https:\/\/doi.org\/10.3389\/fmolb.2020.00183","journal-title":"Front Mol Biosci"},{"key":"10699_CR27","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.compmedimag.2022.102125","volume":"102","author":"X Lu","year":"2022","unstructured":"Lu X, Zhang S, Liu Z et al (2022) Ultrasonographic pathological grading of prostate cancer using automatic region-based Gleason grading network. Comput Med Imag Graph 102:125. https:\/\/doi.org\/10.1016\/j.compmedimag.2022.102125","journal-title":"Comput Med Imag Graph"},{"key":"10699_CR28","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.cmpb.2016.11.002","volume":"139","author":"T Mungle","year":"2017","unstructured":"Mungle T, Tewary S, Arun I et al (2017) Automated characterization and counting of Ki-67 protein for breast cancer prognosis: a quantitative immunohistochemistry approach. Comput Methods Program Biomed 139:149\u2013161. https:\/\/doi.org\/10.1016\/j.cmpb.2016.11.002","journal-title":"Comput Methods Program Biomed"},{"issue":"1","key":"10699_CR29","doi-asserted-by":"publisher","first-page":"8489","DOI":"10.1038\/s41598-021-86912-w","volume":"11","author":"F Negahbani","year":"2021","unstructured":"Negahbani F, Sabzi R, Bea Pakniyat Jahromi (2021) Pathonet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer. Sci Rep 11(1):8489","journal-title":"Sci Rep"},{"key":"10699_CR30","doi-asserted-by":"publisher","unstructured":"Neubeck A, Van\u00a0Gool L (2006) Efficient non-maximum suppression. In: Proceedings of the 18th international conference on pattern recognition\u2014volume 03. IEEE Computer Society, USA, ICPR \u201906, pp 850\u2013855. https:\/\/doi.org\/10.1109\/ICPR.2006.479","DOI":"10.1109\/ICPR.2006.479"},{"key":"10699_CR31","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.019562","author":"M Niazi","year":"2018","unstructured":"Niazi M, Tavolara T, Arole V et al (2018) Identifying tumor in pancreatic neuroendocrine neoplasms from ki67 images using transfer learning. PLoS ONE. https:\/\/doi.org\/10.1371\/journal.pone.019562","journal-title":"PLoS ONE"},{"issue":"7","key":"10699_CR32","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1093\/jnci\/djaa201","volume":"113","author":"TO Nielsen","year":"2020","unstructured":"Nielsen TO, Leung SCY, Rimm DL et al (2020) Assessment of Ki67 in breast cancer: updated recommendations from the international Ki67 in breast cancer working group. J Natl Cancer Inst 113(7):808\u2013819. https:\/\/doi.org\/10.1093\/jnci\/djaa201","journal-title":"J Natl Cancer Inst"},{"key":"10699_CR33","unstructured":"Paszke A, Gross S, Massa F et\u00a0al (2019) Pytorch: an imperative style, high-performance deep learning library. In: Wallach H, Larochelle H, Beygelzimer A et al (eds) Advances in neural information processing systems 32. Curran Associates, Inc., pp 8024\u20138035"},{"key":"10699_CR34","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"10699_CR35","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv:1804.02767"},{"key":"10699_CR36","doi-asserted-by":"crossref","unstructured":"Ren S, He K, Girshick R et al (2016) Faster R-CNN: towards real-time object detection with region proposal networks. arXiv:1506.01497","DOI":"10.1109\/TPAMI.2016.2577031"},{"issue":"3","key":"10699_CR37","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1214\/aoms\/1177729586","volume":"22","author":"H Robbins","year":"1951","unstructured":"Robbins H, Monro S (1951) A stochastic approximation method. Ann Math Stat 22(3):400\u2013407. https:\/\/doi.org\/10.1214\/aoms\/1177729586","journal-title":"Ann Math Stat"},{"key":"10699_CR38","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015a) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, New York, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10699_CR39","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015b) U-net: convolutional networks for biomedical image segmentation. https:\/\/doi.org\/10.48550\/ARXIV.1505.04597","DOI":"10.48550\/ARXIV.1505.04597"},{"key":"10699_CR40","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-017-1934-z","author":"CT Rueden","year":"2017","unstructured":"Rueden CT, Schindelin J, Hiner MC et al (2017) Imagej 2: Imagej for the next generation of scientific image data. BMC Bioinform. https:\/\/doi.org\/10.1186\/s12859-017-1934-z","journal-title":"BMC Bioinform"},{"issue":"3213","key":"10699_CR41","first-page":"2045","volume":"7","author":"M Saha","year":"2017","unstructured":"Saha M, Chakraborty C, Arun I et al (2017) An advanced deep learning approach for Ki-67 stained hotspot detection and proliferation rate scoring for prognostic evaluation of breast cancer. Sci Rep 7(3213):2045\u20132322","journal-title":"Sci Rep"},{"key":"10699_CR42","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1038\/nmeth.2089","volume":"9","author":"CA Schneider","year":"2012","unstructured":"Schneider CA, Rasband WS, Eliceiri KW (2012) NIH image to Imagej: 25 years of image analysis. Nat Methods 9:671\u2013675. https:\/\/doi.org\/10.1038\/nmeth.2089","journal-title":"Nat Methods"},{"key":"10699_CR43","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. https:\/\/doi.org\/10.48550\/ARXIV.1409.1556","DOI":"10.48550\/ARXIV.1409.1556"},{"issue":"1","key":"10699_CR44","doi-asserted-by":"publisher","DOI":"10.1017\/cts.2020.531","volume":"5","author":"B Smith","year":"2020","unstructured":"Smith B, Hermsen M, Ravichandar LDE et al (2020) Developing image analysis pipelines of whole-slide images: pre- and post-processing. J Clin Transl Sci 5(1):e38","journal-title":"J Clin Transl Sci"},{"issue":"101","key":"10699_CR45","first-page":"813","volume":"67","author":"CL Srinidhi","year":"2021","unstructured":"Srinidhi CL, Ciga O, Martel AL (2021) Deep neural network models for computational histopathology: a survey. Med Image Anal 67(101):813","journal-title":"Med Image Anal"},{"key":"10699_CR46","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y et al (2014) Going deeper with convolutions. arXiv:1409.4842","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10699_CR47","unstructured":"Vos KD. Cell counter. https:\/\/imagej.nih.gov\/ij\/plugins\/cell-counter.html. Accessed 12 Jul 2022"},{"key":"10699_CR48","doi-asserted-by":"publisher","unstructured":"Wang CY, Liao HYM, Yeh IH et al (2019) CSPNET: a new backbone that can enhance learning capability of CNN. https:\/\/doi.org\/10.48550\/ARXIV.1911.11929","DOI":"10.48550\/ARXIV.1911.11929"},{"issue":"1","key":"10699_CR49","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.annonc.2021.09.007","volume":"33","author":"Y Wang","year":"2022","unstructured":"Wang Y, Acs B, Robertson S et al (2022) Improved breast cancer histological grading using deep learning. Ann Oncol 33(1):89\u201398","journal-title":"Ann Oncol"},{"issue":"1","key":"10699_CR50","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1186\/s12931-018-0843-7","volume":"19","author":"DM Wei","year":"2018","unstructured":"Wei DM, Chen WJ, Meng RM et al (2018) Augmented expression of Ki-67 is correlated with clinicopathological characteristics and prognosis for lung cancer patients: an up-dated systematic review and meta-analysis with 108 studies and 14,732 patients. Respir Res 19(1):150","journal-title":"Respir Res"},{"key":"10699_CR51","doi-asserted-by":"publisher","unstructured":"Xing F, Su FAU, Neltner J et al (2014) Automatic Ki-67 counting using robust cell detection and online dictionary learning. IEEE Trans Biomed Eng 61(3):859\u201370. https:\/\/doi.org\/10.1109\/TBME.2013.2291703","DOI":"10.1109\/TBME.2013.2291703"},{"key":"10699_CR52","doi-asserted-by":"publisher","first-page":"3088","DOI":"10.1109\/TBME.2019.2900378","volume":"66","author":"F Xing","year":"2019","unstructured":"Xing F, Cornish TC, Bennett TD et al (2019) Pixel-to-pixel learning with weak supervision for single-stage nucleus recognition in Ki67 images. IEEE Trans Biomed Eng 66:3088\u20133097","journal-title":"IEEE Trans Biomed Eng"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10699-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-023-10699-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10699-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T09:09:18Z","timestamp":1709629758000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-023-10699-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,14]]},"references-count":52,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["10699"],"URL":"https:\/\/doi.org\/10.1007\/s10462-023-10699-7","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,14]]},"assertion":[{"value":"31 December 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The research was conducted in accordance with local guidelines and regulations. The study was approved by the local ethics committee (Bioethics Committee Wroclaw Medical University).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"53"}}