CT Radiomic Nomogram Using Optimal Volume of Interest for Preoperatively Predicting Invasive Mucinous Adenocarcinomas in Patients with Incidental Pulmonary Nodules: A Multicenter, Large-Scale Study
A nomogram that combines a recognized image-finding classifier with an optimal VOI-based radiomic model effectively predicts IMA in IPNs, aiding physicians in developing comprehensive treatment strategies.
Authors
- Shuangping Zhang (ORCID: https://orcid.org/0000-0002-8321-4690)
- Zhichao Zuo (ORCID: https://orcid.org/0009-0004-4824-6138)
- Ying Zeng (ORCID: https://orcid.org/0000-0003-2795-4616)
- Guochao Zhang (ORCID: https://orcid.org/0000-0002-2011-4537)
- Shanyue Lin
- Qi Xue (ORCID: https://orcid.org/0000-0002-5086-4654)
- Xiaohong Fan
- Yang Lü (ORCID: https://orcid.org/0000-0003-1212-5508)
- Wu Ge
- Wanyin Qi (ORCID: https://orcid.org/0009-0004-5691-4669)
- Jing Chen (ORCID: https://orcid.org/0000-0003-0676-1794)
- Haibo Liu (ORCID: https://orcid.org/0000-0002-4213-2883)
Institutions
- Zhejiang Normal University (CN)
- Guilin Medical University (CN)
- Shanxi Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Affiliated Hospital of Southwest Medical University (CN)
- Xiangyang Central Hospital (CN)
Publication Details
- Journal
- Technology in Cancer Research & Treatment
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1177/15330338241308307
- Citations
- 4
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.68
Funders
- Peking Union Medical College Hospital
- Beijing Municipal Science and Technology Commission
- Peking Union Medical College
- Cancer Foundation of China
- Bijzonder Onderzoeksfonds UGent