{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T05:09:16Z","timestamp":1741324156142,"version":"3.38.0"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T00:00:00Z","timestamp":1741219200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T00:00:00Z","timestamp":1741219200000},"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":["Cancer Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Apart from rare cases such as lymphomas, germ cell tumors, neuroendocrine neoplasms, and thymic hyperplasia, thymic mass lesions (TMLs) are typically categorized into cysts, and thymomas. However, the classification results cannot be determined in advance and can only be confirmed through postoperative pathology. Therefore, the objective of this study is to rely on clinical parameters and radiomic features extracted from chest computed tomography (CT) scans to facilitate the preoperative classification of TMLs. The model development specifically focused on thymic cysts and thymomas, as these are the most commonly encountered anterior mediastinal tumors in clinical practice.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Materials and methods<\/jats:title>\n            <jats:p>This retrospective study included 400 participants from 3 hospitals between September 2017 and September 2024 due to TMLs. The participants were classified into 7 groups based on the ultimately confirmed etiology: thymic cysts and thymomas, including types A, AB, B1, B2, B3, and C. All participants underwent contrast-enhanced chest CT scans, with senior radiologists delineating regions of interest to extract radiomic features. Additionally, the participants\u2019 ages were also collected as clinical parameters for analysis. The participants were randomly allocated into a training set and a validation set at a 7:3 ratio. A classifier models were developed using the data from the training set, and their performances were evaluated on the validation set.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The model exhibited good classification performance with accuracies of 0.8547.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>The model can assist in early diagnosis and the development of personalized treatment strategies for patients with TMLs.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s40644-025-00839-3","type":"journal-article","created":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T09:47:45Z","timestamp":1741254465000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Preoperative multiclass classification of thymic mass lesions based on radiomics and machine learning"],"prefix":"10.1186","volume":"25","author":[{"given":"Yan","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aichao","family":"Ruan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyu","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5235-583X","authenticated-orcid":false,"given":"Zhenzhong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,6]]},"reference":[{"issue":"10","key":"839_CR1","doi-asserted-by":"publisher","first-page":"e20230892","DOI":"10.1084\/jem.20230892","volume":"221","author":"F Pala","year":"2024","unstructured":"Pala F, Notarangelo LD, Bosticardo M. 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The study was approved by the Institutional Review Board and Ethics Review Committee at The Affiliated Huai\u2019an No.1 People\u2019s Hospital, Nanjing Medical University (KY-2024-365-01), approved by the Ethics Committee on Biomedical Research, West China Hospital, Sichuan University (No. 2024\u2009\u2212\u20091511), approved by the Ethics Review Committee, Huai\u2019an Cancer Hospital (No. 2024056). The study protocols and procedures were reviewed and approved by the committee prior to the commencement of data collection.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Informed consent was obtained from all human participants involved in this study. They were provided with a clear explanation of the research objectives, procedures, potential risks, and benefits, and they voluntarily agreed to participate. To ensure confidentiality and anonymity, all personal identifying information was anonymized or coded during data analysis and reporting.","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":"25"}}