{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T12:40:37Z","timestamp":1736167237584,"version":"3.32.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-024-01505-z","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T11:54:59Z","timestamp":1736164499000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application value of CT three-dimensional reconstruction technology in the identification of benign and malignant lung nodules and the characteristics of nodule distribution"],"prefix":"10.1186","volume":"25","author":[{"given":"Guanghai","family":"Ji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,6]]},"reference":[{"issue":"6","key":"1505_CR1","doi-asserted-by":"publisher","first-page":"4095","DOI":"10.1121\/10.0006666","volume":"150","author":"R Roshankhah","year":"2021","unstructured":"Roshankhah R, Blackwell J, Ali MH, Masuodi B, Egan T, Muller M. Detecting pulmonary nodules by using ultrasound multiple scattering. J Acoust Soc Am. 2021;150(6):4095.","journal-title":"J Acoust Soc Am"},{"issue":"2","key":"1505_CR2","doi-asserted-by":"publisher","first-page":"130","DOI":"10.5603\/FHC.a2023.0011","volume":"61","author":"K Popovic","year":"2023","unstructured":"Popovic K, Miladinovic M, Vuckovic L, Nedovic M, Vukovic. Rare benign lung tumours presenting with high clinical suspicion for malignancy: a case series and review of the literature. Folia Histochem Cytobiol. 2023;61(2):130\u201342.","journal-title":"Folia Histochem Cytobiol"},{"issue":"10","key":"1505_CR3","doi-asserted-by":"publisher","first-page":"2094","DOI":"10.21037\/tlcr-22-647","volume":"11","author":"B Zhang","year":"2022","unstructured":"Zhang B, Liang H, Liu W, Zhou X, Qiao S, Li F, et al. A novel approach for the non-invasive diagnosis of pulmonary nodules using low-depth whole-genome sequencing of cell-free DNA. Transl Lung Cancer Res. 2022;11(10):2094\u2013110.","journal-title":"Transl Lung Cancer Res"},{"issue":"11","key":"1505_CR4","doi-asserted-by":"publisher","first-page":"e076573","DOI":"10.1136\/bmjopen-2023-076573","volume":"13","author":"C Xie","year":"2023","unstructured":"Xie C, Huang Q, Liu Y. Utility of peripheral blood macrophage factor Apo10 and TKTL1 as markers in distinguishing malignant from benign lung nodules: a protocol for a prospective cohort study in Southern China. BMJ Open. 2023;13(11):e076573.","journal-title":"BMJ Open"},{"issue":"6","key":"1505_CR5","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.pulmoe.2020.06.011","volume":"28","author":"M Jacob","year":"2022","unstructured":"Jacob M, Romano J, Ara Jo D, Pereira JM, Ramos I, Hespanhol V. Predicting lung nodules malignancy. Pulmonology. 2022;28(6):454\u201360.","journal-title":"Pulmonology"},{"key":"1505_CR6","doi-asserted-by":"crossref","unstructured":"Zhu H, Liu W, Gao Z, Zhang H. Explainable classification of Benign-Malignant Pulmonary nodules with neural networks and information bottleneck. IEEE Trans Neural Netw Learn Syst; 2023.","DOI":"10.1109\/TNNLS.2023.3303395"},{"key":"1505_CR7","doi-asserted-by":"publisher","first-page":"p8769652","DOI":"10.1155\/2021\/8769652","volume":"2021","author":"E Lv","year":"2021","unstructured":"Lv E, Liu W, Wen P, Kang X. Classification of Benign and malignant lung nodules based on deep Convolutional Network feature extraction. J Healthc Eng. 2021;2021:p8769652.","journal-title":"J Healthc Eng"},{"issue":"7","key":"1505_CR8","doi-asserted-by":"publisher","first-page":"3207","DOI":"10.1002\/mp.13592","volume":"46","author":"J Uthoff","year":"2019","unstructured":"Uthoff J, Stephens MJ, Newell JD Jr., Hoffman EA, Larson J, Koehn N, et al. Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT. Med Phys. 2019;46(7):3207\u201316.","journal-title":"Med Phys"},{"issue":"4","key":"1505_CR9","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1109\/TMI.2018.2876510","volume":"38","author":"Y Xie","year":"2019","unstructured":"Xie Y, Xia Y, Zhang J, Song Y, Feng D, Fulham M, et al. Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule classification on chest CT. IEEE Trans Med Imaging. 2019;38(4):991\u20131004.","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"1505_CR10","first-page":"683","volume":"24","author":"X Zhao","year":"2021","unstructured":"Zhao X, Lu H, Zhang Z. [Preliminary study of CT three-dimensional Reconstruction combined with Ground Glass Nodules of Natural Lung Collapse in Thoracoscopic Pulmonary Segmental Resection]. Zhongguo Fei Ai Za Zhi. 2021;24(10):683\u20139.","journal-title":"Zhongguo Fei Ai Za Zhi"},{"issue":"3","key":"1505_CR11","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.21037\/tlcr-21-202","volume":"10","author":"Y Ji","year":"2021","unstructured":"Ji Y, Zhang T, Yang L, Wang X, Qi L, Tan F, et al. The effectiveness of three-dimensional reconstruction in the localization of multiple nodules in lung specimens: a prospective cohort study. Transl Lung Cancer Res. 2021;10(3):1474\u201383.","journal-title":"Transl Lung Cancer Res"},{"issue":"4","key":"1505_CR12","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1097\/MCP.0000000000000886","volume":"28","author":"S Sethi","year":"2022","unstructured":"Sethi S, Cicenia J. Role of biomarkers in lung nodule evaluation. Curr Opin Pulm Med. 2022;28(4):275\u201381.","journal-title":"Curr Opin Pulm Med"},{"key":"1505_CR13","doi-asserted-by":"crossref","unstructured":"Dakua SP, Sahambi JS. LV Contour Extraction from Cardiac MR Images Using Random Walks Approach.. IEEE international advance computing conference, 2009. pp. 228\u2013233.","DOI":"10.1109\/IADCC.2009.4809012"},{"key":"1505_CR14","doi-asserted-by":"crossref","unstructured":"Cardone B, Di Martino F, Miraglia V. A novel fuzzy-based remote sensing image Segmentation Method. Sens (Basel), 2023. 23(24).","DOI":"10.3390\/s23249641"},{"key":"1505_CR15","doi-asserted-by":"publisher","first-page":"106478","DOI":"10.1016\/j.compbiomed.2022.106478","volume":"153","author":"MY Ansari","year":"2022","unstructured":"Ansari MY, Yang Y, Meher PK, Dakua SP. Dense-PSP-UNet: a neural network for fast inference liver ultrasound segmentation. Comput Biol Med. 2022;153:106478.","journal-title":"Comput Biol Med"},{"issue":"4","key":"1505_CR16","doi-asserted-by":"publisher","first-page":"372","DOI":"10.4103\/0377-2063.86338","volume":"57","author":"SP Dakua","year":"2011","unstructured":"Dakua SP, Sahambi JS. Detection of left ventricular myocardial contours from ischemic cardiac MR images. Iete J Res. 2011;57(4):372\u201384.","journal-title":"Iete J Res"},{"key":"1505_CR17","doi-asserted-by":"publisher","first-page":"9890","DOI":"10.1109\/ACCESS.2022.3233110","volume":"11","author":"MY Ansari","year":"2022","unstructured":"Ansari MY, Chandrasekar V, Singh AV, Dakua SP. Re-routing drugs to blood brain barrier: a comprehensive analysis of machine learning approaches with fingerprint amalgamation and data balancing. IEEE Access. 2022;11:9890\u2013906.","journal-title":"IEEE Access"},{"key":"1505_CR18","doi-asserted-by":"publisher","first-page":"7397","DOI":"10.1007\/s11042-020-10064-8","volume":"80","author":"S Garg","year":"2021","unstructured":"Garg S, Jindal B. Skin lesion segmentation using k-mean and optimized fire fly algorithm. Multimed Tools Appl. 2021;80:7397\u2013410.","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"1505_CR19","first-page":"29","volume":"19","author":"S Garg","year":"2022","unstructured":"Garg S, Jindal B. Skin lesion segmentation in Dermoscopy Imagery. Int Arab J Inf Technol. 2022;19(1):29\u201337.","journal-title":"Int Arab J Inf Technol"},{"issue":"12","key":"1505_CR20","doi-asserted-by":"publisher","first-page":"36115","DOI":"10.1007\/s11042-023-17143-6","volume":"83","author":"S Garg","year":"2024","unstructured":"Garg S, Jindal B. FDLM: an enhanced feature based deep learning model for skin lesion detection. Multimed Tools Appl. 2024;83(12):36115\u201327.","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"1505_CR21","doi-asserted-by":"publisher","first-page":"6053","DOI":"10.1007\/s11042-022-13589-2","volume":"82","author":"B Jindal","year":"2023","unstructured":"Jindal B, Garg S. FIFE: fast and indented feature extractor for medical imaging based on shape features. Multimed Tools Appl. 2023;82(4):6053\u201369.","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"1505_CR22","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/s10558-009-9091-2","volume":"10","author":"SP Dakua","year":"2010","unstructured":"Dakua SP, Sahambi JS. Automatic left ventricular contour extraction from cardiac magnetic resonance images using cantilever beam and random walk approach. Cardiovasc Eng. 2010;10(1):30\u201343.","journal-title":"Cardiovasc Eng"},{"issue":"1","key":"1505_CR23","doi-asserted-by":"publisher","first-page":"14153","DOI":"10.1038\/s41598-022-16828-6","volume":"12","author":"MY Ansari","year":"2022","unstructured":"Ansari MY, Yang Y, Balakrishnan S, Abinahed J, Al-Ansari A, Warfa M, et al. A lightweight neural network with multiscale feature enhancement for liver CT segmentation. Sci Rep. 2022;12(1):14153.","journal-title":"Sci Rep"},{"key":"1505_CR24","doi-asserted-by":"crossref","unstructured":"Wang C, Xu R, Xu S, Meng W, Xiao J, Zhang X. Accurate lung nodule segmentation with detailed representation transfer and soft Mask Supervision. IEEE Trans Neural Netw Learn Syst; 2023.","DOI":"10.1109\/TNNLS.2023.3315271"},{"issue":"3","key":"1505_CR25","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1001\/jama.2021.24287","volume":"327","author":"PJ Mazzone","year":"2022","unstructured":"Mazzone PJ, Lam L. Evaluating the patient with a pulmonary nodule: a review. JAMA. 2022;327(3):264\u201373.","journal-title":"JAMA"},{"issue":"2","key":"1505_CR26","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":"4","key":"1505_CR27","first-page":"265","volume":"26","author":"Y Zhou","year":"2023","unstructured":"Zhou Y, Zhang Y, Zhang S, Zhang C, Chen Z. [Growth regularity of Pulmonary Ground Glass nodules based on 3D Reconstruction Technology]. Zhongguo Fei Ai Za Zhi. 2023;26(4):265\u201373.","journal-title":"Zhongguo Fei Ai Za Zhi"},{"issue":"2","key":"1505_CR28","first-page":"93","volume":"99","author":"W Wang","year":"2019","unstructured":"Wang W, Zhan P, Xie Q, Hu HD, Wang YC, Yuan Q, et al. [Combination of CT mulitplane 3D reconstruction, radial endobronchial ultrasound and rapid on-site evaluation for diagnosing peripheral solitary pulmonary nodules]. Zhonghua Yi Xue Za Zhi. 2019;99(2):93\u20138.","journal-title":"Zhonghua Yi Xue Za Zhi"},{"key":"1505_CR29","doi-asserted-by":"crossref","unstructured":"Lu Z, Long F, He X. Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction. Comput Math Methods Med, 2022. 2022: p. 3490463.","DOI":"10.1155\/2022\/3490463"},{"issue":"1","key":"1505_CR30","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1186\/s13019-021-01642-4","volume":"16","author":"L Zhao","year":"2021","unstructured":"Zhao L, Yang W, Hong R, Fei J. Application of three-dimensional reconstruction combined with dial positioning in small pulmonary nodules surgery. J Cardiothorac Surg. 2021;16(1):254.","journal-title":"J Cardiothorac Surg"},{"issue":"12","key":"1505_CR31","doi-asserted-by":"publisher","first-page":"7826","DOI":"10.1002\/mp.15298","volume":"48","author":"B Zheng","year":"2021","unstructured":"Zheng B, Yang D, Zhu Y, Liu Y, Hu J, Bai C. 3D gray density coding feature for benign-malignant pulmonary nodule classification on chest CT. Med Phys. 2021;48(12):7826\u201336.","journal-title":"Med Phys"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01505-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-024-01505-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01505-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T12:03:46Z","timestamp":1736165026000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-024-01505-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,6]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1505"],"URL":"https:\/\/doi.org\/10.1186\/s12880-024-01505-z","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,6]]},"assertion":[{"value":"28 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 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":"All patients provided informed consent and signed a written informed consent form. The study was approved by the Ethics Committee of the First Affiliated Hospital of Yangtze University (approval number: 20210915) and adhered to the Helsinki Declaration. Clinical trial number: ChiCTR2100120326.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"7"}}