IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0331336.html
   My bibliography  Save this article

Artificial intelligence-based CT histogram parameters differentiating bronchiolar adenoma and lung adenocarcinomas: A two-center study

Author

Listed:
  • Wen Zhao
  • Ziqian Zhao
  • Yingxia Wang
  • Haiyan Yang
  • Weiyuan Zhang
  • Jianyou Chen
  • Xinhui Yang
  • Zhijie Duan
  • Fengyi Li
  • Zhiquan Han
  • Xin Zhang
  • Zhilin Li
  • Dan Han
  • Tengfei Ke

Abstract

Purpose: Bronchiolar adenoma (BA) is a rare benign pulmonary neoplasm originating from the bronchial mucosal epithelium and mimics lung adenocarcinoma (LAC) both radiographically and microscopically. This study aimed to develop a nomogram for distinguishing BA from LAC by integrating clinical characteristics and artificial intelligence (AI)-derived histogram parameters across two medical centers. Methods: This retrospective study included 215 patients with diagnoses confirmed by postoperative pathology from two medical centers. Medical center 1 provided 151 patients (68 BA and 83 LAC nodules) as the training cohort, while medical center 2 contributed 64 patients (28 BA and 36 LAC nodules) as the external validation cohort. Risk predictors and the nomogram were developed using clinical characteristics and AI-derived histogram parameters. Results: Nodule density (solid, ground glass, and subsolid) exhibited a statistically significant difference between the BA and LAC groups (p

Suggested Citation

  • Wen Zhao & Ziqian Zhao & Yingxia Wang & Haiyan Yang & Weiyuan Zhang & Jianyou Chen & Xinhui Yang & Zhijie Duan & Fengyi Li & Zhiquan Han & Xin Zhang & Zhilin Li & Dan Han & Tengfei Ke, 2025. "Artificial intelligence-based CT histogram parameters differentiating bronchiolar adenoma and lung adenocarcinomas: A two-center study," PLOS ONE, Public Library of Science, vol. 20(9), pages 1-13, September.
    • Handle: RePEc:plo:pone00:0331336
    DOI: 10.1371/journal.pone.0331336
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0331336
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0331336&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0331336?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0331336. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.