{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T12:55:33Z","timestamp":1777294533376,"version":"3.51.4"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"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":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Objective<\/jats:title>\n            <jats:p>In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR)\u2009\u2265\u200950%, as malignant ones are more invasive. This study aims to develop and validate an AI-driven radiomics prediction model for such nodules to enhance diagnostic accuracy.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>Data of 2,591 pulmonary nodules from five medical centers (Zhengzhou People\u2019s Hospital, etc.) were collected. Applying exclusion criteria, 370 nodules (78 benign, 292 malignant) with 3D CTR\u2009\u2265\u200950% were selected and randomly split 7:3 into training and validation cohorts. Using R programming, Lasso regression with 10-fold cross-validation filtered features, followed by univariate and multivariate logistic regression to construct the model. Its efficacy was evaluated by ROC, DCA curves and calibration plots.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Lasso regression picked 18 non-zero coefficients from 108 features. Three significant factors\u2014patient age, solid component volume and mean CT value\u2014were identified. The logistic regression equation was formulated. In the training set, the ROC AUC was 0.721 (95%CI: 0.642\u20130.801); in the validation set, AUC was 0.757 (95%CI: 0.632\u20130.881), showing the model\u2019s stability and predictive ability.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>The model has moderate accuracy in differentiating benign from malignant 3D CTR\u2009\u2265\u200950% nodules, holding clinical potential. Future efforts could explore more to improve its precision and value.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Clinical trial number<\/jats:title>\n            <jats:p>Not applicable.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12880-024-01533-9","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T12:37:49Z","timestamp":1737117469000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR\u2009\u2265\u200950% utilizing artificial intelligence-driven radiomics analysis"],"prefix":"10.1186","volume":"25","author":[{"given":"Wensong","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuzhui","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guotao","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinsen","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengpan","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Yi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sikai","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huandong","family":"Huo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuaibo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yousheng","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyuan","family":"Ai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangnan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiyu","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"issue":"3","key":"1533_CR1","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1016\/j.athoracsur.2022.11.040","volume":"117","author":"S Choi","year":"2024","unstructured":"Choi S, Yoon DW, Shin S, Kim HK, Choi YS, Kim J, Shim YM, Cho JH. Importance of Lymph Node Evaluation in \u2264\u20092-cm pure-solid Non-small Cell Lung Cancer. Ann Thorac Surg. 2024;117(3):586\u201393.","journal-title":"Ann Thorac Surg"},{"key":"1533_CR2","doi-asserted-by":"crossref","unstructured":"Kim YT. Management of Ground-Glass nodules: when and how to operate? Cancers (Basel) 2022, 14(3).","DOI":"10.3390\/cancers14030715"},{"issue":"11","key":"1533_CR3","doi-asserted-by":"publisher","first-page":"1647","DOI":"10.1111\/1759-7714.13982","volume":"12","author":"R Tao","year":"2021","unstructured":"Tao R, Cao W, Zhu F, Nie J, Wang H, Wang L, Liu P, Chen H, Hong B, Zhao D. Liquid biopsies to distinguish malignant from benign pulmonary nodules. Thorac Cancer. 2021;12(11):1647\u201355.","journal-title":"Thorac Cancer"},{"issue":"3","key":"1533_CR4","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.jtho.2018.11.023","volume":"14","author":"LM Seijo","year":"2019","unstructured":"Seijo LM, Peled N, Ajona D, Boeri M, Field JK, Sozzi G, Pio R, Zulueta JJ, Spira A, Massion PP, et al. Biomarkers in Lung Cancer Screening: achievements, promises, and challenges. J Thorac Oncol. 2019;14(3):343\u201357.","journal-title":"J Thorac Oncol"},{"issue":"8","key":"1533_CR5","doi-asserted-by":"publisher","first-page":"3782","DOI":"10.1002\/cam4.2286","volume":"8","author":"Y Li","year":"2019","unstructured":"Li Y, Tian X, Gao L, Jiang X, Fu R, Zhang T, Ren T, Hu P, Wu Y, Zhao P, et al. Clinical significance of circulating tumor cells and tumor markers in the diagnosis of lung cancer. Cancer Med. 2019;8(8):3782\u201392.","journal-title":"Cancer Med"},{"issue":"21","key":"1533_CR6","doi-asserted-by":"publisher","first-page":"3018","DOI":"10.1111\/1759-7714.14653","volume":"13","author":"S Koike","year":"2022","unstructured":"Koike S, Shimizu K, Ide S, Mishima S, Matsuoka S, Takeda T, Miura K, Eguchi T, Hamanaka K, Araki T, et al. Is using a consolidation tumor ratio 0.5 as criterion feasible in daily practice? Evaluation of interobserver measurement variability of consolidation tumor ratio of lung cancer less than 3\u00a0cm in size. Thorac Cancer. 2022;13(21):3018\u201324.","journal-title":"Thorac Cancer"},{"issue":"8","key":"1533_CR7","doi-asserted-by":"publisher","first-page":"5122","DOI":"10.21037\/jtd-24-243","volume":"16","author":"Y Wang","year":"2024","unstructured":"Wang Y, Lyu D, Yu D, Hu S, Ma Y, Huang W, Duan S, Zhou T, Tu W, Zhou X, et al. Intratumoral and peritumoral radiomics combined with computed tomography features for predicting the invasiveness of lung adenocarcinoma presenting as a subpleural ground-glass nodule with a consolidation-to-tumor ratio\u2009\u2264\u200950. J Thorac Dis. 2024;16(8):5122\u201337.","journal-title":"J Thorac Dis"},{"key":"1533_CR8","doi-asserted-by":"crossref","unstructured":"Wan YL, Wu PW, Huang PC, Tsay PK, Pan KT, Trang NN, Chuang WY, Wu CY, Lo SB. The Use of Artificial Intelligence in the differentiation of malignant and benign lung nodules on computed Tomograms Proven by Surgical Pathology. Cancers (Basel) 2020, 12(8).","DOI":"10.3390\/cancers12082211"},{"issue":"1","key":"1533_CR9","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1186\/s12880-024-01421-2","volume":"24","author":"J Bin","year":"2024","unstructured":"Bin J, Wu M, Huang M, Liao Y, Yang Y, Shi X, Tao S. Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach. BMC Med Imaging. 2024;24(1):240.","journal-title":"BMC Med Imaging"},{"issue":"1","key":"1533_CR10","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1186\/s12880-024-01413-2","volume":"24","author":"BQ Qu","year":"2024","unstructured":"Qu BQ, Wang Y, Pan YP, Cao PW, Deng XY. The scoring system combined with radiomics and imaging features in predicting the malignant potential of incidental indeterminate small (<\u200920\u00a0mm) solid pulmonary nodules. BMC Med Imaging. 2024;24(1):234.","journal-title":"BMC Med Imaging"},{"key":"1533_CR11","doi-asserted-by":"publisher","first-page":"175346662412856","DOI":"10.1177\/17534666241285606","volume":"18","author":"Z Zhu","year":"2024","unstructured":"Zhu Z, Jiang W, Zhou D, Zhu W, Chen C. Risk analysis of visceral pleural invasion in malignant solitary pulmonary nodules that appear touching the pleural surface. Ther Adv Respir Dis. 2024;18:17534666241285606.","journal-title":"Ther Adv Respir Dis"},{"key":"1533_CR12","doi-asserted-by":"crossref","unstructured":"Zhao WH, Zhang LJ, Li X, Luo TY, Lv FJ, Li Q. Clinical and computed tomography characteristics of inflammatory solid pulmonary nodules with morphology suggesting malignancy. Acad Radiol. 2024 Sep 21:S1076-6332(24)00665-2.","DOI":"10.1016\/j.acra.2024.09.016"},{"issue":"10","key":"1533_CR13","doi-asserted-by":"publisher","first-page":"7265","DOI":"10.21037\/qims-24-912","volume":"14","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Ding BW, Wang LN, Ma WL, Zhu L, Chen QH, Yu H. Using CT features of cystic airspace to predict lung adenocarcinoma invasiveness. Quant Imaging Med Surg. 2024;14(10):7265\u201378.","journal-title":"Quant Imaging Med Surg"},{"key":"1533_CR14","doi-asserted-by":"crossref","unstructured":"Mattolini M, Citi S, Franchi R, Meucci V, Carozzi G, Gianni B, Caleri E, Rossi F. Computed tomographic features of pulmonary and extrapulmonary lesions can be useful in prioritizing the diagnosis of hemangiosarcoma metastases in dogs. Am J Vet Res. 2024 Oct 3;85(12):ajvr.24.08.0219.","DOI":"10.2460\/ajvr.24.08.0219"},{"key":"1533_CR15","doi-asserted-by":"crossref","unstructured":"Gao Z, Liu S, Li X, Xu L, Xiao H, Guo JC, Yu Y, Li M, Ren WG, Peng ZM. Preoperative markers for identifying CT\u2009\u2264\u20092 cm solid nodules of lung adenocarcinoma based on image deep learning. Thorac Cancer. 2024 Nov;15(31):2272-2282","DOI":"10.1111\/1759-7714.15448"},{"key":"1533_CR16","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.ejrad.2019.06.010","volume":"117","author":"C Gao","year":"2019","unstructured":"Gao C, Xiang P, Ye J, Pang P, Wang S, Xu M. Can texture features improve the differentiation of infiltrative lung adenocarcinoma appearing as ground glass nodules in contrast-enhanced CT? Eur J Radiol. 2019;117:126\u201331.","journal-title":"Eur J Radiol"},{"issue":"1","key":"1533_CR17","doi-asserted-by":"publisher","first-page":"21871","DOI":"10.1038\/s41598-024-72592-9","volume":"14","author":"W Zhang","year":"2024","unstructured":"Zhang W, Cui X, Wang J, Cui S, Yang J, Meng J, Zhu W, Li Z, Niu J. The study of plain CT combined with contrast-enhanced CT-based models in predicting malignancy of solitary solid pulmonary nodules. Sci Rep. 2024;14(1):21871.","journal-title":"Sci Rep"},{"key":"1533_CR18","doi-asserted-by":"crossref","unstructured":"Yu L, Zhang B, Zou H, Shi Y, Cheng L, Zhang Y, Zhen H. Multivariate Analysis on Development of Lung Adenocarcinoma Lesion from Solitary Pulmonary Nodule. Contrast Media Mol Imaging 2022, 2022:8330111.","DOI":"10.1155\/2022\/8330111"},{"key":"1533_CR19","doi-asserted-by":"publisher","first-page":"1198338","DOI":"10.3389\/fcell.2023.1198338","volume":"11","author":"X Huang","year":"2023","unstructured":"Huang X, Lu Z, Jiang X, Zhang Z, Yan K, Yu G. Single-cell RNA sequencing reveals distinct tumor microenvironment of ground glass nodules and solid nodules in lung adenocarcinoma. Front Cell Dev Biol. 2023;11:1198338.","journal-title":"Front Cell Dev Biol"},{"key":"1533_CR20","doi-asserted-by":"crossref","unstructured":"Kim H, Lee JK, Oh AC, Kim HR, Hong YJ. The Usefulness of the Ratio of Antigen-Autoantibody Immune Complexes to Their Free Antigens in the Diagnosis of Non-Small Cell Lung Cancer. Diagnostics (Basel) 2023, 13(18).","DOI":"10.3390\/diagnostics13182999"},{"key":"1533_CR21","doi-asserted-by":"crossref","unstructured":"Liang W, Chen Z, Li C, Liu J, Tao J, Liu X, Zhao D, Yin W, Chen H, Cheng C et al. Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test. J Clin Invest 2021, 131(10).","DOI":"10.1172\/JCI145973"},{"issue":"3","key":"1533_CR22","doi-asserted-by":"publisher","first-page":"566","DOI":"10.21037\/tlcr-23-145","volume":"12","author":"Z Wan","year":"2023","unstructured":"Wan Z, He H, Zhao M, Ma X, Sun S, Wang T, Deng J, Zhong Y, She Y, Ma M, et al. The development and validation of a circulating tumor cells-based integrated model for improving the indeterminate lung solid nodules diagnosis. Transl Lung Cancer Res. 2023;12(3):566\u201379.","journal-title":"Transl Lung Cancer Res"},{"issue":"3","key":"1533_CR23","doi-asserted-by":"publisher","first-page":"208","DOI":"10.21037\/tlcr.2019.06.09","volume":"8","author":"M Zhao","year":"2019","unstructured":"Zhao M, Xin XF, Hu H, Pan XH, Lv TF, Liu HB, Zhang JY, Song Y. 18F-fluorodeoxyglucose positron emission tomography\/computed tomography in the diagnosis of benign pulmonary lesions in sarcoidosis. Transl Lung Cancer Res. 2019;8(3):208\u201313.","journal-title":"Transl Lung Cancer Res"},{"issue":"18","key":"1533_CR24","doi-asserted-by":"publisher","first-page":"e70216","DOI":"10.1002\/cam4.70216","volume":"13","author":"M Sun","year":"2024","unstructured":"Sun M, Lu D, Li X, Wang J, Zhang L, Yang P, Yang Y, Shen J. Combination of circulating tumor cells and 18F-FDG PET\/CT for precision diagnosis in patients with non-small cell lung cancer. Cancer Med. 2024;13(18):e70216.","journal-title":"Cancer Med"},{"issue":"23","key":"1533_CR25","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.21037\/atm-22-2647","volume":"10","author":"C Ren","year":"2022","unstructured":"Ren C, Xu M, Zhang J, Zhang F, Song S, Sun Y, Wu K, Cheng J. Classification of solid pulmonary nodules using a machine-learning nomogram based on (18)F-FDG PET\/CT radiomics integrated clinicobiological features. Ann Transl Med. 2022;10(23):1265.","journal-title":"Ann Transl Med"},{"issue":"1","key":"1533_CR26","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1097\/MNM.0000000000000605","volume":"38","author":"Z Ruilong","year":"2017","unstructured":"Ruilong Z, Daohai X, Li G, Xiaohong W, Chunjie W, Lei T. Diagnostic value of 18F-FDG-PET\/CT for the evaluation of solitary pulmonary nodules: a systematic review and meta-analysis. Nucl Med Commun. 2017;38(1):67\u201375.","journal-title":"Nucl Med Commun"},{"key":"1533_CR27","doi-asserted-by":"publisher","first-page":"1212608","DOI":"10.3389\/fonc.2023.1212608","volume":"13","author":"B Yang","year":"2023","unstructured":"Yang B, Gao Y, Lu J, Wang Y, Wu R, Shen J, Ren J, Wu F, Xu H. Quantitative analysis of chest MRI images for benign malignant diagnosis of pulmonary solid nodules. Front Oncol. 2023;13:1212608.","journal-title":"Front Oncol"},{"issue":"1","key":"1533_CR28","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s40644-023-00531-4","volume":"23","author":"F Sanchez","year":"2023","unstructured":"Sanchez F, Tyrrell PN, Cheung P, Heyn C, Graham S, Poon I, Ung Y, Louie A, Tsao M, Oikonomou A. Detection of solid and subsolid pulmonary nodules with lung MRI: performance of UTE, T1 gradient-echo, and single-shot T2 fast spin echo. Cancer Imaging. 2023;23(1):17.","journal-title":"Cancer Imaging"},{"key":"1533_CR29","doi-asserted-by":"publisher","first-page":"1147479","DOI":"10.3389\/fonc.2023.1147479","volume":"13","author":"X Xie","year":"2023","unstructured":"Xie X, Liu K, Luo K, Xu Y, Zhang L, Wang M, Shen W, Zhou Z. Value of dual-layer spectral detector computed tomography in the diagnosis of benign\/malignant solid solitary pulmonary nodules and establishment of a prediction model. Front Oncol. 2023;13:1147479.","journal-title":"Front Oncol"},{"issue":"2","key":"1533_CR30","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.21037\/qims-23-995","volume":"14","author":"XQ He","year":"2024","unstructured":"He XQ, Huang XT, Luo TY, Liu X, Li Q. The differential computed tomography features between small benign and malignant solid solitary pulmonary nodules with different sizes. Quant Imaging Med Surg. 2024;14(2):1348\u201358.","journal-title":"Quant Imaging Med Surg"},{"issue":"1","key":"1533_CR31","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1186\/s12880-022-00824-3","volume":"22","author":"G Liang","year":"2022","unstructured":"Liang G, Yu W, Liu SQ, Xie MG, Liu M. The value of radiomics based on dual-energy CT for differentiating benign from malignant solitary pulmonary nodules. BMC Med Imaging. 2022;22(1):95.","journal-title":"BMC Med Imaging"},{"key":"1533_CR32","doi-asserted-by":"crossref","unstructured":"Kudo Y, Nakamura T, Matsubayashi J, Ichinose A, Goto Y, Amemiya R, Park J, Shimada Y, Kakihana M, Nagao T et al. AI-driven characterization of solid pulmonary nodules on CT imaging for enhanced malignancy prediction in small-sized lung adenocarcinoma. Clin Lung Cancer.  2024 Jul;25(5):431-439.","DOI":"10.1016\/j.cllc.2024.04.015"},{"key":"1533_CR33","doi-asserted-by":"publisher","first-page":"1286433","DOI":"10.3389\/fmed.2023.1286433","volume":"10","author":"L Zhang","year":"2023","unstructured":"Zhang L, Shao Y, Chen G, Tian S, Zhang Q, Wu J, Bai C, Yang D. An artificial intelligence-assisted diagnostic system for the prediction of benignity and malignancy of pulmonary nodules and its practical value for patients with different clinical characteristics. Front Med (Lausanne). 2023;10:1286433.","journal-title":"Front Med (Lausanne)"},{"issue":"10","key":"1533_CR34","doi-asserted-by":"publisher","first-page":"5475","DOI":"10.21037\/jtd-23-985","volume":"15","author":"J Liu","year":"2023","unstructured":"Liu J, Qi L, Wang Y, Li F, Chen J, Cheng S, Zhou Z, Yu Y, Wang J. Diagnostic performance of a deep learning-based method in differentiating malignant from benign subcentimeter (\u2264\u200910\u00a0mm) solid pulmonary nodules. J Thorac Dis. 2023;15(10):5475\u201384.","journal-title":"J Thorac Dis"},{"key":"1533_CR35","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.lungcan.2022.01.002","volume":"165","author":"HL Lancaster","year":"2022","unstructured":"Lancaster HL, Zheng S, Aleshina OO, Yu D, Yu Chernina V, Heuvelmans MA, de Bock GH, Dorrius MD, Gratama JW, Morozov SP, et al. Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification. Lung Cancer. 2022;165:133\u201340.","journal-title":"Lung Cancer"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01533-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-024-01533-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01533-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T12:37:55Z","timestamp":1737117475000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-024-01533-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,17]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1533"],"URL":"https:\/\/doi.org\/10.1186\/s12880-024-01533-9","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,17]]},"assertion":[{"value":"24 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 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":"The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (IRB) of The Fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People\u2019s Hospital) (No.2024011155). The need for informed consent was waived by the Zhengzhou People\u2019s Hospital, as the study involved the analysis of de-identified data and no direct interaction with subjects.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"21"}}