CT‐based whole lung radiomics nomogram to identify middle‐aged and elderly COVID‐19 patients at high risk of progressing to critical disease
Our nomogram model incorporates radiomics features and clinical indicators, it provides a new pathway to increase predictive accuracy or clinical utility, further helping to provide personalized management for middle-aged and elderly patients with COVID-19.
Authors
- Qinling Jiang
- Li Fan (ORCID: https://orcid.org/0000-0003-4722-3933)
- Xiuxiu Zhou (ORCID: https://orcid.org/0000-0003-2405-0566)
- Taohu Zhou (ORCID: https://orcid.org/0000-0001-6208-2249)
- Chao Zhou (ORCID: https://orcid.org/0000-0003-1785-3969)
- Xiaoyan Xin
- Qianyun Ma
- Shiyuan Liu (ORCID: https://orcid.org/0000-0002-0470-7308)
- Yun Wang (ORCID: https://orcid.org/0000-0001-9438-5586)
- Jingyi Zhao (ORCID: https://orcid.org/0000-0002-4058-1095)
- Xin'ang Jiang (ORCID: https://orcid.org/0009-0009-2985-6789)
- Jun Hu (ORCID: https://orcid.org/0009-0008-5619-7742)
- Fei Yao (ORCID: https://orcid.org/0000-0002-1769-9140)
- Yi Sun (ORCID: https://orcid.org/0000-0001-6590-501X)
- Wen Yang (ORCID: https://orcid.org/0000-0002-2032-1982)
- Kang Shi
Institutions
- Shanghai University (CN)
- Weifang Medical University (CN)
- Shanghai Changzheng Hospital (CN)
- Nanjing Drum Tower Hospital (CN)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2024-11-29
- DOI
- https://doi.org/10.1002/acm2.14562
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Science and Technology Commission of Shanghai Municipality
- National Key Research and Development Program of China