{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T03:04:08Z","timestamp":1767495848170,"version":"3.48.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T00:00:00Z","timestamp":1767484800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T00:00:00Z","timestamp":1767484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003407","name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["PRIN 2022 MUR 20228MZFAA- AIDA"],"award-info":[{"award-number":["PRIN 2022 MUR 20228MZFAA- AIDA"]}],"id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Umea University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Accurate prognosis of Non-Small Cell Lung Cancer (NSCLC) is crucial for enhancing patient care and treatment outcomes. Despite the advancements in deep learning, the task of overall survival prediction in NSCLC has not fully leveraged these techniques, yet. This study introduces a novel methodology for predicting 2-year overall survival (OS) in NSCLC patients using CT scans. Our approach integrates CT scan representations produced by EfficientNetB0 with a soft attention mechanism to identify the most relevant slices for survival risk prediction, which are then analyzed by a risk-assessment network. To validate our method and ensure reproducibility, we employed the public LUNG1 dataset and a smaller private dataset. Our approach was compared to benchmark 3D networks and two variants of our methodology: on the LUNG1 it outperformed the competitors achieving a mean\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$C^{td}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msup>\n                            <mml:mi>C<\/mml:mi>\n                            <mml:mrow>\n                              <mml:mi>td<\/mml:mi>\n                            <\/mml:mrow>\n                          <\/mml:msup>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -index of 0.584 over tenfold cross-validation. On the LUNG1 we also demonstrated the adaptability of our method with 5 other 2D backbones replacing the EfficientNetB0, confirming that our mechanism of combining 2D slice representations to construct a 3D volume representation is more effective for OS prediction compared to a traditional 3D approach. Finally, we used transfer learning on the private dataset, showing that it can significantly enhance performance in limited data scenarios, increasing the\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$C^{td}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msup>\n                            <mml:mi>C<\/mml:mi>\n                            <mml:mrow>\n                              <mml:mi>td<\/mml:mi>\n                            <\/mml:mrow>\n                          <\/mml:msup>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -index by 0.076 compared to model without transfer learning.\n                  <\/jats:p>","DOI":"10.1007\/s13755-025-00404-z","type":"journal-article","created":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T03:02:11Z","timestamp":1767495731000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting lung cancer survival with attention-based CT slices combination"],"prefix":"10.1007","volume":"14","author":[{"given":"Domenico","family":"Paolo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlo","family":"Greco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edy","family":"Ippolito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michele","family":"Fiore","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sara","family":"Ramella","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2621-072X","authenticated-orcid":false,"given":"Paolo","family":"Soda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matteo","family":"Tortora","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessandro","family":"Bria","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rosa","family":"Sicilia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,4]]},"reference":[{"key":"404_CR1","doi-asserted-by":"crossref","unstructured":"Shong LY-W, Lam DC-L. Emerging trends in global lung cancer burden. In: Seminars in respiratory and critical care medicine. New York: Thieme Medical Publishers Inc.; 2025","DOI":"10.1055\/a-2651-0612"},{"issue":"7","key":"404_CR2","doi-asserted-by":"publisher","first-page":"5930","DOI":"10.3390\/su15075930","volume":"15","author":"AW Salehi","year":"2023","unstructured":"Salehi AW, Khan S, Gupta G, Alabduallah BI, Almjally A, Alsolai H, et al. A study of CNN and transfer learning in medical imaging: advantages, challenges, future scope. Sustainability. 2023;15(7):5930.","journal-title":"Sustainability"},{"key":"404_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.saa.2021.120400","volume":"265","author":"Y Qi","year":"2022","unstructured":"Qi Y, Yang L, Liu B, Liu L, Liu Y, Zheng Q, et al. Highly accurate diagnosis of lung adenocarcinoma and squamous cell carcinoma tissues by deep learning. Spectrochim Acta Part A. 2022;265:120400.","journal-title":"Spectrochim Acta Part A"},{"key":"404_CR4","doi-asserted-by":"crossref","unstructured":"Liu CZ, Sicilia R, Tortora M, Cordelli E, Nibid L, Sabarese G, et al. Exploring deep pathomics in lung cancer. In: 2021 IEEE 34th International symposium on computer-based medical systems (CBMS). IEEE; 2021. p. 407\u201312.","DOI":"10.1109\/CBMS52027.2021.00092"},{"issue":"1","key":"404_CR5","first-page":"3","volume":"59","author":"S Tiwari","year":"2023","unstructured":"Tiwari S, Jain G, Shetty DK, Sudhi M, Balakrishnan JM, Bhatta SR. A comprehensive review on the application of 3d convolutional neural networks in medical imaging. Eng Proc. 2023;59(1):3.","journal-title":"Eng. Proc."},{"issue":"2","key":"404_CR6","first-page":"155","volume":"1","author":"AC Bellail","year":"2012","unstructured":"Bellail AC, Hao C. Trail apoptotic pathway-targeted therapies for NSCLC. Transl Lung Cancer Res. 2012;1(2):155.","journal-title":"Transl. Lung Cancer Res."},{"issue":"1","key":"404_CR7","doi-asserted-by":"publisher","first-page":"14132","DOI":"10.1038\/s41598-022-18085-z","volume":"12","author":"A Braghetto","year":"2022","unstructured":"Braghetto A, Marturano F, Paiusco M, Baiesi M, Bettinelli A. Radiomics and deep learning methods for the prediction of 2-year overall survival in lung1 dataset. Sci Rep. 2022;12(1):14132.","journal-title":"Sci Rep"},{"key":"404_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2023.109483","volume":"180","author":"S Zheng","year":"2023","unstructured":"Zheng S, Guo J, Langendijk JA, Both S, Veldhuis RN, Oudkerk M, et al. Survival prediction for stage I-IIIA non-small cell lung cancer using deep learning. Radiother Oncol. 2023;180:109483.","journal-title":"Radiother Oncol"},{"issue":"3","key":"404_CR9","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1038\/s42256-024-00807-9","volume":"6","author":"S Pai","year":"2024","unstructured":"Pai S, Bontempi D, Hadzic I, Prudente V, Soka\u010d M, Chaunzwa TL, et al. Foundation model for cancer imaging biomarkers. Nat Mach Intell. 2024;6(3):354\u201367.","journal-title":"Nat Mach Intell"},{"key":"404_CR10","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1200\/CCI.21.00096","volume":"5","author":"FS Torres","year":"2021","unstructured":"Torres FS, Akbar S, Raman S, Yasufuku K, Schmidt C, Hosny A, et al. End-to-end non\u2013small-cell lung cancer prognostication using deep learning applied to pretreatment computed tomography. JCO Clin Cancer Inform. 2021;5:1141\u201350.","journal-title":"JCO Clin Cancer Inform"},{"key":"404_CR11","doi-asserted-by":"crossref","unstructured":"Haarburger C, Weitz P, Rippel O, Merhof D. Image-based survival prediction for lung cancer patients using CNNs. In: 2019 IEEE 16th International symposium on biomedical imaging (ISBI 2019). IEEE; 2019. p. 1197\u2013201.","DOI":"10.1109\/ISBI.2019.8759499"},{"key":"404_CR12","doi-asserted-by":"crossref","unstructured":"Chen M, Copley SJ, Viola P, Lu H, Aboagye EO. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. In: Seminars in cancer biology, vol. 93. Elsevier; 2023. p. 97\u2013113.","DOI":"10.1016\/j.semcancer.2023.05.004"},{"issue":"3","key":"404_CR13","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1007\/s10278-023-00778-0","volume":"36","author":"VH Le","year":"2023","unstructured":"Le VH, Kha QH, Minh TNT, Nguyen VH, Le VL, Le NQK. Development and validation of CT-based radiomics signature for overall survival prediction in multi-organ cancer. J Digit Imaging. 2023;36(3):911\u201322.","journal-title":"J Digit Imaging"},{"issue":"1","key":"404_CR14","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1007\/s10916-025-02156-5","volume":"49","author":"VH Le","year":"2025","unstructured":"Le VH, Minh TNT, Kha QH, Le NQK. Deep learning radiomics for survival prediction in non-small-cell lung cancer patients from CT images. J Med Syst. 2025;49(1):22.","journal-title":"J Med Syst"},{"key":"404_CR15","doi-asserted-by":"crossref","unstructured":"Mall PK, Singh PK, Srivastav S, Narayan V, Paprzycki M, Jaworska T, et al. A comprehensive review of deep neural networks for medical image processing: recent developments and future opportunities. Healthc Anal. 2023;100216.","DOI":"10.1016\/j.health.2023.100216"},{"key":"404_CR16","doi-asserted-by":"publisher","unstructured":"Aerts HJWL, Wee L, Rios Velazquez E, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Data from NSCLC-radiomics (version 4) [Data set]. The Cancer Imaging Archive 2014. https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.PF0M9REI","DOI":"10.7937\/K9\/TCIA.2015.PF0M9REI"},{"key":"404_CR17","first-page":"258","volume":"2011","author":"DR Aberle","year":"2007","unstructured":"Aberle DR, Berg CD, Black W, et al. The national lung screening trial: overview and study design. Radiology. 2007;2011:258.","journal-title":"Radiology"},{"key":"404_CR18","unstructured":"Bakr S, Gevaert O, Echegaray S, Ayers K, Zhou M, Shafiq M, et al. Data for NSCLC Radiogenomics collection. The Cancer Imaging Archive. 2017."},{"issue":"3","key":"404_CR19","doi-asserted-by":"publisher","first-page":"0118261","DOI":"10.1371\/journal.pone.0118261","volume":"10","author":"O Grove","year":"2015","unstructured":"Grove O, Berglund AE, Schabath MB, Aerts HJ, Dekker A, Wang H, et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS ONE. 2015;10(3):0118261.","journal-title":"PLoS ONE"},{"key":"404_CR20","unstructured":"Kirk S, Lee Y, Kumar P, Filippini J, Albertina B, Watson M, Rieger-Christ K, Lemmerman J. The cancer genome atlas lung squamous cell carcinoma collection (TCGA-LUSC) (version 4) [Data set]. The Cancer Imaging Archive. 2016"},{"key":"404_CR21","unstructured":"Albertina B, Watson M, Holback C, al: Radiology data from the cancer genome atlas lung adenocarcinoma [TCGA-LUAD] collection. The Cancer Imaging Arch. 2016"},{"key":"404_CR22","doi-asserted-by":"crossref","unstructured":"Lee C, Zame W, Yoon J, Van Der\u00a0Schaar M. Deephit: A deep learning approach to survival analysis with competing risks. In: Proceedings of the AAAI conference on artificial intelligence, vol. 32. 2018","DOI":"10.1609\/aaai.v32i1.11842"},{"key":"404_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102137","volume":"119","author":"M Tortora","year":"2021","unstructured":"Tortora M, Cordelli E, Sicilia R, Miele M, Matteucci P, Iannello G, et al. Deep reinforcement learning for fractionated radiotherapy in non-small cell lung carcinoma. Artif Intell Med. 2021;119:102137.","journal-title":"Artif Intell Med"},{"key":"404_CR24","doi-asserted-by":"publisher","first-page":"47563","DOI":"10.1109\/ACCESS.2023.3275126","volume":"11","author":"M Tortora","year":"2023","unstructured":"Tortora M, Cordelli E, Sicilia R, Nibid L, Ippolito E, Perrone G, et al. Radiopathomics: multimodal learning in non-small cell lung cancer for adaptive radiotherapy. IEEE Access. 2023;11:47563\u201378. https:\/\/doi.org\/10.1109\/ACCESS.2023.3275126.","journal-title":"IEEE Access"},{"issue":"11","key":"404_CR25","doi-asserted-by":"publisher","first-page":"298","DOI":"10.3390\/jimaging8110298","volume":"8","author":"CM Caruso","year":"2022","unstructured":"Caruso CM, Guarrasi V, Cordelli E, Sicilia R, Gentile S, Messina L, et al. A multimodal ensemble driven by multiobjective optimisation to predict overall survival in non-small-cell lung cancer. J Imaging. 2022;8(11):298.","journal-title":"J Imaging"},{"key":"404_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41747-020-00173-2","volume":"4","author":"J Hofmanninger","year":"2020","unstructured":"Hofmanninger J, Prayer F, Pan J, R\u00f6hrich S, Prosch H, Langs G. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. Eur Radiol Exp. 2020;4:1\u201313.","journal-title":"Eur Radiol Exp"},{"key":"404_CR27","doi-asserted-by":"crossref","unstructured":"Patel CH, Undaviya D, Dave H, Degadwala S, Vyas D. EfficientNetB0 for brain stroke classification on computed tomography scan. In: 2023 2nd International conference on applied artificial intelligence and computing (ICAAIC). IEEE; 2023. p. 713\u20138.","DOI":"10.1109\/ICAAIC56838.2023.10141195"},{"key":"404_CR28","doi-asserted-by":"crossref","unstructured":"Mandal AC, Phatak A. Optimizing deep learning based retinal diseases classification on optical coherence tomography scans. In: European conference on biomedical optics. Optica Publishing Group; 2023. p. 1263220","DOI":"10.1117\/12.2672249"},{"key":"404_CR29","doi-asserted-by":"crossref","unstructured":"Tadepalli Y, Kollati M, Kuraparthi S, Kora P. EfficientNet-B0 based monocular dense-depth map estimation. Traitement du Signal. 2021;38(5)","DOI":"10.18280\/ts.380524"},{"key":"404_CR30","doi-asserted-by":"publisher","first-page":"1396160","DOI":"10.3389\/frai.2024.1396160","volume":"7","author":"V Anand","year":"2024","unstructured":"Anand V, Koundal D, Alghamdi WY, Alsharbi BM. Smart grading of diabetic retinopathy: an intelligent recommendation-based fine-tuned efficientNetB0 framework. Front Artif Intell. 2024;7:1396160.","journal-title":"Front Artif Intell"},{"key":"404_CR31","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on computer vision and pattern recognition. IEEE; 2009. p. 248\u201355.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"404_CR32","doi-asserted-by":"crossref","unstructured":"Tran D, Wang H, Torresani L, Ray J, LeCun Y, Paluri M. A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. p. 6450\u201359","DOI":"10.1109\/CVPR.2018.00675"},{"key":"404_CR33","unstructured":"Kay W, Carreira J, Simonyan K, Zhang B, Hillier C, Vijayanarasimhan S, Viola F, Green T, Back T, Natsev P, et al.: The kinetics human action video dataset. 2017. arXiv:1705.06950"},{"issue":"1","key":"404_CR34","doi-asserted-by":"publisher","first-page":"23979","DOI":"10.1038\/s41598-025-09041-8","volume":"15","author":"G M\u00fcller-Franzes","year":"2025","unstructured":"M\u00fcller-Franzes G, Khader F, Siepmann R, Han T, Kather JN, Nebelung S, et al. Medical slice transformer for improved diagnosis and explainability on 3D medical images with DINOv2. Sci Rep. 2025;15(1):23979.","journal-title":"Sci Rep"},{"key":"404_CR35","unstructured":"Oquab M, Darcet T, Moutakanni T, Vo H, Szafraniec M, Khalidov V, Fernandez P, Haziza D, Massa F, El-Nouby A, et al.: Dinov2: learning robust visual features without supervision. 2023. arXiv:2304.07193"},{"key":"404_CR36","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. arXiv:1409.1556"},{"key":"404_CR37","unstructured":"Krizhevsky A. One weird trick for parallelizing convolutional neural networks. 2014. arXiv:1404.5997"},{"key":"404_CR38","unstructured":"Alexey D. An image is worth 16x16 words: transformers for image recognition at scale. 2020. arXiv: 2010.11929"},{"key":"404_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106791","volume":"157","author":"ON Manzari","year":"2023","unstructured":"Manzari ON, Ahmadabadi H, Kashiani H, Shokouhi SB, Ayatollahi A. Medvit: a robust vision transformer for generalized medical image classification. Comput Biol Med. 2023;157:106791.","journal-title":"Comput Biol Med"},{"issue":"24","key":"404_CR40","doi-asserted-by":"publisher","first-page":"3927","DOI":"10.1002\/sim.2427","volume":"24","author":"L Antolini","year":"2005","unstructured":"Antolini L, Boracchi P, Biganzoli E. A time-dependent discrimination index for survival data. Stat Med. 2005;24(24):3927\u201344.","journal-title":"Stat Med"},{"issue":"18","key":"404_CR41","doi-asserted-by":"publisher","first-page":"2543","DOI":"10.1001\/jama.1982.03320430047030","volume":"247","author":"FE Harrell","year":"1982","unstructured":"Harrell FE, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating the yield of medical tests. JAMA. 1982;247(18):2543\u20136.","journal-title":"JAMA"},{"key":"404_CR42","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst. 2019;32."},{"issue":"1","key":"404_CR43","doi-asserted-by":"publisher","first-page":"14933","DOI":"10.1038\/s41598-020-71996-7","volume":"10","author":"HW Lee","year":"2020","unstructured":"Lee HW, Park YS, Park S, Lee C-H. Poor prognosis of NSCLC located in lower lobe is partly mediated by lower frequency of EGFR mutations. Sci Rep. 2020;10(1):14933.","journal-title":"Sci Rep"},{"issue":"2","key":"404_CR44","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1111\/1759-7714.12957","volume":"10","author":"Y-H Lin","year":"2019","unstructured":"Lin Y-H, Hung J-J, Yeh Y-C, Hsu W-H. Prognostic significance of basal versus superior segment in patients with completely resected lung adenocarcinoma in the lower lobe. Thoracic Cancer. 2019;10(2):312\u201320.","journal-title":"Thoracic cancer"},{"issue":"12","key":"404_CR45","doi-asserted-by":"publisher","first-page":"1614","DOI":"10.1111\/1759-7714.12869","volume":"9","author":"HW Lee","year":"2018","unstructured":"Lee HW, Lee C-H, Park YS. Location of stage I\u2013III non-small cell lung cancer and survival rate: systematic review and meta-analysis. Thoracic Cancer. 2018;9(12):1614\u201322.","journal-title":"Thoracic cancer"}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-025-00404-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13755-025-00404-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-025-00404-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T03:02:14Z","timestamp":1767495734000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13755-025-00404-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,4]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["404"],"URL":"https:\/\/doi.org\/10.1007\/s13755-025-00404-z","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,4]]},"assertion":[{"value":"12 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"20"}}