{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T09:15:55Z","timestamp":1775639755549,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","award":["JPMJSP2106 and JPMJSP2180"],"award-info":[{"award-number":["JPMJSP2106 and JPMJSP2180"]}],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Mizuho Foundation for the Promotion of Sciences","award":["Mizuho Foundation for the Promotion of Sciences"],"award-info":[{"award-number":["Mizuho Foundation for the Promotion of Sciences"]}]},{"DOI":"10.13039\/501100001863","name":"New Energy and Industrial Technology Development Organization","doi-asserted-by":"publisher","award":["JPNP20006"],"award-info":[{"award-number":["JPNP20006"]}],"id":[{"id":"10.13039\/501100001863","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The Papanicolaou stain, consisting of five dyes, provides extensive color information essential for cervical cancer cytological screening. The visual observation of these colors is subjective and difficult to characterize. Direct RGB quantification is unreliable because RGB intensities vary with staining and imaging conditions. Stain unmixing offers a promising alternative by quantifying dye amounts. In previous work, multispectral imaging was utilized to estimate the dye amounts of Papanicolaou stain. However, its application to RGB images presents a challenge since the number of dyes exceeds the three RGB channels. This paper proposes a novel training-free Papanicolaou stain unmixing method for RGB images. This model enforces (i) nonnegativity, (ii) weighted nucleus sparsity for hematoxylin, and (iii) total variation smoothness, resulting in a convex optimization problem. Our method achieved excellent performance in stain quantification when validated against the results of multispectral imaging. We further used it to distinguish cells in lobular endocervical glandular hyperplasia (LEGH), a precancerous gastric-type adenocarcinoma lesion, from normal endocervical cells. Stain abundance features clearly separated the two groups, and a classifier based on stain abundance achieved 98.0% accuracy. By converting subjective color impressions into numerical markers, this technique highlights the strong promise of RGB-based stain unmixing for quantitative diagnosis.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1007\/s11517-025-03490-z","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T04:57:39Z","timestamp":1765861059000},"page":"911-929","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Papanicolaou stain unmixing for RGB image using weighted nucleus sparsity and total variation regularization"],"prefix":"10.1007","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7530-0853","authenticated-orcid":false,"given":"Nanxin","family":"Gong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saori","family":"Takeyama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahiro","family":"Yamaguchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takumi","family":"Urata","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fumikazu","family":"Kimura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keiko","family":"Ishii","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"3490_CR1","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1155\/2007\/874795","volume":"23","author":"D Jenkins","year":"2007","unstructured":"Jenkins D (2007) Histopathology and cytopathology of cervical cancer. Dis Markers 23:199","journal-title":"Dis Markers"},{"key":"3490_CR2","first-page":"477","volume":"21","author":"M Kai","year":"1999","unstructured":"Kai M, Nunobiki O, Taniguchi E, Sakamoto Y, Kakudo K (1999) Quantitative and qualitative analysis of stain color using RGB computer color specification. Anal Quant Cytol Histol 21:477\u2013480","journal-title":"Anal Quant Cytol Histol"},{"key":"3490_CR3","first-page":"289","volume":"24","author":"O Nunobiki","year":"2002","unstructured":"Nunobiki O, Sato M, Taniguchi E, Tang W, Nakamura M, Utsunomiya H, Nakamura Y, Mori I, Kakudo K (2002) Color image analysis of cervical neoplasia using RGB computer color specification. Anal Quant Cytol Histol 24:289\u2013294","journal-title":"Anal Quant Cytol Histol"},{"key":"3490_CR4","first-page":"250","volume":"20","author":"Y Sakamoto","year":"1998","unstructured":"Sakamoto Y, Taniguchi E, Kakudo K (1998) Objective evaluation of Papanicolaou staining. Anal Quant Cytol Histol 20:250\u2013256","journal-title":"Anal Quant Cytol Histol"},{"key":"3490_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1746-1596-6-S1-S15","volume":"6","author":"Y Yagi","year":"2011","unstructured":"Yagi Y (2011) Color standardization and optimization in whole slide imaging. Diagn Pathol 6:1\u201312","journal-title":"Diagn Pathol"},{"key":"3490_CR6","doi-asserted-by":"crossref","unstructured":"Fujii K, Yamaguchi M, Ohyama N, Mukai K (2002) Development of support systems for pathology using spectral transmittance: the quantification method of stain conditions. Medical Imaging 2002: Image Processing, vol. 4684, pp. 1516\u201323","DOI":"10.1117\/12.467118"},{"key":"3490_CR7","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/s10043-005-0293-6","volume":"12","author":"T Abe","year":"2005","unstructured":"Abe T, Murakami Y, Yamaguchi M, Ohyama N, Yagi Y (2005) Color correction of pathological images based on dye amount quantification. Opt Rev 12:293\u2013300","journal-title":"Opt Rev"},{"key":"3490_CR8","volume":"12","author":"S Takeyama","year":"2025","unstructured":"Takeyama S, Watanabe T, Gong N, Yamaguchi M, Urata T, Kimura F, Ishii K (2025) Dye amount quantification of Papanicolaou-stained cytological images by multispectral unmixing: spectral analysis of cytoplasmic mucin. J Med Imaging 12:17501","journal-title":"J Med Imaging"},{"key":"3490_CR9","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1016\/j.humpath.2009.04.006","volume":"40","author":"RS Weinstein","year":"2009","unstructured":"Weinstein RS, Graham AR, Richter LC, Barker GP, Krupinski EA, Lopez AM, Erps KA, Bhattacharyya AK, Yagi Y, Gilbertson JR (2009) Overview of telepathology, virtual microscopy, and whole slide imaging: prospects for the future. Hum Pathol 40:1057\u20131069","journal-title":"Hum Pathol"},{"key":"3490_CR10","first-page":"291","volume":"23","author":"AC Ruifrok","year":"2001","unstructured":"Ruifrok AC (2001) Johnston DA, others. Quantification of histochemical staining by color Deconvolution. Anal Quant Cytol Histol 23:291\u2013299","journal-title":"Anal Quant Cytol Histol"},{"key":"3490_CR11","first-page":"113","volume":"9420","author":"N Trahearn","year":"2015","unstructured":"Trahearn N, Snead D, Cree I, Rajpoot N (2015) Multi-class stain separation using independent component analysis. Med Imaging 2015: Digit Pathol 9420:113\u2013123","journal-title":"Med Imaging 2015: Digit Pathol"},{"key":"3490_CR12","doi-asserted-by":"crossref","unstructured":"Macenko M, Niethammer M, Marron JS, Borland D, Woosley JT, Guan X, Schmitt C, Thomas NE (2009) A method for normalizing histology slides for quantitative analysis. 2009 IEEE international symposium on biomedical imaging: from nano to macro, pp. 1107\u201310","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"3490_CR13","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1109\/TMI.2013.2239655","volume":"32","author":"M Gavrilovic","year":"2013","unstructured":"Gavrilovic M, Azar JC, Lindblad J, W\u00e4hlby C, Bengtsson E, Busch C, Carlbom IB (2013) Blind color decomposition of histological images. IEEE Trans Med Imaging 32:983\u2013994","journal-title":"IEEE Trans Med Imaging"},{"key":"3490_CR14","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.cmpb.2019.01.008","volume":"170","author":"Y Zheng","year":"2019","unstructured":"Zheng Y, Jiang Z, Zhang H, Xie F, Shi J, Xue C (2019) Adaptive color deconvolution for histological WSI normalization. Comput Methods Programs Biomed 170:107\u2013120. https:\/\/doi.org\/10.1016\/j.cmpb.2019.01.008","journal-title":"Comput Methods Programs Biomed"},{"key":"3490_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2022.102048","volume":"97","author":"F P\u00e9rez-Bueno","year":"2022","unstructured":"P\u00e9rez-Bueno F, Serra JG, Vega M, Mateos J, Molina R, Katsaggelos AK (2022) Bayesian K-SVD for H and E blind color deconvolution. Applications to stain normalization, data augmentation and cancer classification. Comput Med Imaging Graph 97:102048","journal-title":"Comput Med Imaging Graph"},{"key":"3490_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105506","volume":"193","author":"M Salvi","year":"2020","unstructured":"Salvi M, Michielli N, Molinari F (2020) Stain color adaptive normalization (SCAN) algorithm: separation and standardization of histological stains in digital pathology. Comput Methods Programs Biomed 193:105506","journal-title":"Comput Methods Programs Biomed"},{"key":"3490_CR17","unstructured":"Rabinovich A, Agarwal S, Laris C, Price J, Belongie S (2003) Unsupervised color decomposition of histologically stained tissue samples. Adv Neural Inf Process Syst ;16"},{"key":"3490_CR18","doi-asserted-by":"publisher","first-page":"1962","DOI":"10.1109\/TMI.2016.2529665","volume":"35","author":"A Vahadane","year":"2016","unstructured":"Vahadane A, Peng T, Sethi A, Albarqouni S, Wang L, Baust M, Steiger K, Schlitter AM, Esposito I, Navab N (2016) Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans Med Imaging 35:1962\u20131971","journal-title":"IEEE Trans Med Imaging"},{"key":"3490_CR19","doi-asserted-by":"publisher","first-page":"e0225410","DOI":"10.1371\/journal.pone.0225410","volume":"14","author":"TD McRae","year":"2019","unstructured":"McRae TD, Oleksyn D, Miller J, Gao Y-R (2019) Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning. PLoS One 14:e0225410","journal-title":"PLoS One"},{"key":"3490_CR20","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.compmedimag.2015.04.001","volume":"46","author":"T Chen","year":"2015","unstructured":"Chen T, Srinivas C (2015) Group sparsity model for stain unmixing in brightfield multiplex immunohistochemistry images. Comput Med Imaging Graph 46:30\u201339","journal-title":"Comput Med Imaging Graph"},{"key":"3490_CR21","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/79.974727","volume":"19","author":"N Keshava","year":"2002","unstructured":"Keshava N, Mustard JF (2002) Spectral unmixing. IEEE Signal Process Mag 19:44\u201357","journal-title":"IEEE Signal Process Mag"},{"key":"3490_CR22","doi-asserted-by":"crossref","unstructured":"Bioucas-Dias JM, Figueiredo MAT (2010) Alternating direction algorithms for constrained sparse regression: application to hyperspectral unmixing. 2nd Workshop Hyperspectral Image Signal Processing: Evol Remote Sens 2010:1\u20134","DOI":"10.1109\/WHISPERS.2010.5594963"},{"key":"3490_CR23","doi-asserted-by":"publisher","first-page":"4484","DOI":"10.1109\/TGRS.2012.2191590","volume":"50","author":"M-D Iordache","year":"2012","unstructured":"Iordache M-D, Bioucas-Dias JM, Plaza A (2012) Total variation spatial regularization for sparse hyperspectral unmixing. IEEE Trans Geosci Remote Sens 50:4484\u20134502","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"3490_CR24","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1093\/bioinformatics\/btz674","volume":"36","author":"BJ Rossetti","year":"2020","unstructured":"Rossetti BJ, Wilbert SA, Mark Welch JL, Borisy GG, Nagy JG (2020) Semi-blind sparse affine spectral unmixing of autofluorescence-contaminated micrographs. Bioinformatics 36:910\u2013917. https:\/\/doi.org\/10.1093\/bioinformatics\/btz674","journal-title":"Bioinformatics"},{"key":"3490_CR25","doi-asserted-by":"crossref","unstructured":"Duggal R, Gupta A, Gupta R, Mallick P (2017) SD-layer: stain deconvolutional layer for CNNs in medical microscopic imaging. International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 435\u201343","DOI":"10.1007\/978-3-319-66179-7_50"},{"key":"3490_CR26","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1109\/JBHI.2020.2983206","volume":"25","author":"Y Zheng","year":"2020","unstructured":"Zheng Y, Jiang Z, Zhang H, Xie F, Hu D, Sun S, Shi J, Xue C (2020) Stain standardization capsule for application-driven histopathological image normalization. IEEE J Biomed Health Inf 25:337\u2013347","journal-title":"IEEE J Biomed Health Inf"},{"key":"3490_CR27","doi-asserted-by":"crossref","unstructured":"Chen J, Liu LY, Han W, Wang D, Cheung AM, Tsui H, Martel AL (2022) General stain deconvolution of histopathology images with physics-guided deep learning. bioRxiv 2012\u201322","DOI":"10.1101\/2022.12.06.519385"},{"key":"3490_CR28","first-page":"1","volume":"15","author":"DJ Fassler","year":"2020","unstructured":"Fassler DJ, Abousamra S, Gupta R, Chen C, Zhao M, Paredes D, Batool SA, Knudsen BS, Escobar-Hoyos L (2020) Shroyer KR (eds) 15:1\u201311","journal-title":"Shroyer KR (eds)"},{"key":"3490_CR29","first-page":"74","volume":"227","author":"S Abousamra","year":"2023","unstructured":"Abousamra S, Fassler D, Yao J, Gupta R, Kurc T, Escobar-Hoyos L, Samaras D, Shroyer K, Saltz J, Chen C (2023) Unsupervised stain decomposition via inversion regulation for multiplex immunohistochemistry images. Proc Mach Learn Res 227:74","journal-title":"Proc Mach Learn Res"},{"key":"3490_CR30","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3490_CR31","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1038\/s42256-022-00471-x","volume":"4","author":"P Ghahremani","year":"2022","unstructured":"Ghahremani P, Li Y, Kaufman A, Vanguri R, Greenwald N, Angelo M, Hollmann TJ, Nadeem S (2022) Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification. Nat Mach Intell 4:401\u2013412","journal-title":"Nat Mach Intell"},{"key":"3490_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2023.104318","volume":"145","author":"S Yang","year":"2024","unstructured":"Yang S, Perez-Bueno F, Castro-Macias FM, Molina R, Katsaggelos AK (2024) BCD-net: stain separation of histological images using deep variational Bayesian blind color deconvolution. Dig Signal Process 145:104318","journal-title":"Dig Signal Process"},{"key":"3490_CR33","doi-asserted-by":"publisher","first-page":"886","DOI":"10.1097\/00000478-199908000-00005","volume":"23","author":"MR Nucci","year":"1999","unstructured":"Nucci MR, Clement PB, Young RH (1999) Lobular endocervical glandular hyperplasia, not otherwise specified: a clinicopathologic analysis of thirteen cases of a distinctive pseudoneoplastic lesion and comparison with fourteen cases of adenoma malignum. Am J Surg Pathol 23:886","journal-title":"Am J Surg Pathol"},{"key":"3490_CR34","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1097\/PGP.0000000000000139","volume":"33","author":"MR Nucci","year":"2014","unstructured":"Nucci MR (2014) Pseudoneoplastic glandular lesions of the uterine cervix: a selective review. Int J Gynecol Pathol 33:330\u2013338","journal-title":"Int J Gynecol Pathol"},{"key":"3490_CR35","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1002\/dc.24466","volume":"48","author":"R Kanai","year":"2020","unstructured":"Kanai R, Ohshima K, Ishii K, Sonohara M, Ishikawa M, Yamaguchi M, Ohtani Y, Kobayashi Y, Ota H, Kimura F (2020) Discriminant analysis and interpretation of nuclear chromatin distribution and coarseness using gray-level co-occurrence matrix features for lobular endocervical glandular hyperplasia. Diagn Cytopathol 48:724\u2013735","journal-title":"Diagn Cytopathol"},{"key":"3490_CR36","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1002\/(SICI)1097-0142(19991025)87:5<245::AID-CNCR2>3.0.CO;2-0","volume":"87","author":"K Ishii","year":"1999","unstructured":"Ishii K, Katsuyama T, Ota H, Watanabe T, Matsuyama I, Tsuchiya S, Shiozawa T, Toki T (1999) Cytologic and cytochemical features of adenoma malignum of the uterine cervix. Cancer Cytopathology: Interdisciplinary Int J Am Cancer Soc 87:245\u2013253","journal-title":"Cancer Cytopathology: Interdisciplinary Int J Am Cancer Soc"},{"key":"3490_CR37","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/S0009-8981(01)00611-8","volume":"312","author":"K Ishii","year":"2001","unstructured":"Ishii K (2001) A new diagnostic method for adenoma malignum and related lesions: latex agglutination test with a new monoclonal antibody, HIK1083. Clin Chim Acta 312:231\u2013233","journal-title":"Clin Chim Acta"},{"key":"3490_CR38","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1002\/dc.24155","volume":"47","author":"F Kimura","year":"2019","unstructured":"Kimura F, Kobayashi T, Kanai R, Kobayashi Y, Yuhi O, Ota H, Yamaguchi M, Yokokawa Y, Uehara T, Ishii K (2019) Image quantification technology of the heterochromatin and euchromatin region for differential diagnosis in the lobular endocervical glandular hyperplasia. Diagn Cytopathol 47:553\u2013563","journal-title":"Diagn Cytopathol"},{"key":"3490_CR39","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","volume":"60","author":"LI Rudin","year":"1992","unstructured":"Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Phys D 60:259\u2013268","journal-title":"Phys D"},{"key":"3490_CR40","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1023\/B:JMIV.0000011325.36760.1e","volume":"20","author":"A Chambolle","year":"2004","unstructured":"Chambolle A (2004) An algorithm for total variation minimization and applications. J Math Imaging Vis 20:89\u201397","journal-title":"J Math Imaging Vis"},{"key":"3490_CR41","first-page":"1","volume":"3","author":"S Boyd","year":"2011","unstructured":"Boyd S, Parikh N, Chu E, Peleato B, Eckstein Jothers. (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends\u00ae Mach Learn 3:1\u2013122","journal-title":"Found Trends\u00ae Mach Learn"},{"key":"3490_CR42","doi-asserted-by":"publisher","first-page":"e0266973","DOI":"10.1371\/journal.pone.0266973","volume":"17","author":"H Hu","year":"2022","unstructured":"Hu H, Qiao S, Hao Y, Bai Y, Cheng R, Zhang W, Zhang G (2022) Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy. PLoS One 17:e0266973","journal-title":"PLoS One"},{"key":"3490_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106453","volume":"211","author":"F P\u00e9rez-Bueno","year":"2021","unstructured":"P\u00e9rez-Bueno F, Vega M, Sales MA, Aneiros-Fern\u00e1ndez J, Naranjo V, Molina R, Katsaggelos AK (2021) Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference. Comput Methods Programs Biomed 211:106453","journal-title":"Comput Methods Programs Biomed"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03490-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-025-03490-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03490-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T08:40:17Z","timestamp":1775637617000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-025-03490-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,16]]},"references-count":43,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["3490"],"URL":"https:\/\/doi.org\/10.1007\/s11517-025-03490-z","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,16]]},"assertion":[{"value":"15 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Medical Ethics Committee of Shinshu University (Nov. 6, 2024, No. 5470).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Patients signed informed consent regarding publishing their data and photographs.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors declare that they have no conflict of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}