AI-enabled molecular phenotyping and prognostic predictions in lung cancer through multimodal clinical information integration
Lung cancer remains the leading cause of cancer-related mortality worldwide. The need for cost-effective, non-invasive methods to detect specific gene mutations for targeted therapy and predict patient survival outcomes underscores the importance of advancing diagnostic and prognostic capabilities. Contemporary lung cancer diagnostic models often fail to integrate diverse patient data, leading to incomplete clinical assessments. To address these challenges, we propose LUCID, a multimodal data integration framework designed to predict epidermal growth factor receptor (EGFR) mutation status and survival outcomes in patients with lung cancer. Tailored for early-stage clinical assessment, LUCID leverages lung computed tomography (CT) images, chief complaints, laboratory test results, and demographic data to deliver comprehensive, non-invasive predictions. LUCID achieved strong performance in a retrospective cohort of 5,175 patients, with areas under the receiver operating characteristic curve (AUCs) ranging from 0.851 to 0.881 for EGFR mutation prediction and from 0.821 to 0.912 for survival time prediction. The model also demonstrated robustness across external validation cohorts and in scenarios with missing modalities.
Authors
- Chengdi Wang (ORCID: https://orcid.org/0000-0002-5284-2889)
- Yuxing Lu (ORCID: https://orcid.org/0000-0002-8207-4411)
- Bojiang Chen (ORCID: https://orcid.org/0000-0002-4882-9817)
- Caiwen Ou (ORCID: https://orcid.org/0000-0002-9515-6125)
- Jinzhuo Wang (ORCID: https://orcid.org/0000-0002-9464-4426)
- Yunfang Yu (ORCID: https://orcid.org/0000-0001-7475-1796)
- Xiaoying Huang (ORCID: https://orcid.org/0000-0002-4599-8869)
- Kang Zhang (ORCID: https://orcid.org/0009-0008-7438-5579)
- Miao Man
- Wenyao Yu (ORCID: https://orcid.org/0009-0001-8970-3997)
- Fei Liu (ORCID: https://orcid.org/0000-0003-1734-7214)
- Kun Li (ORCID: https://orcid.org/0009-0005-3363-5023)
- Zixing Zou
Institutions
- Macau University of Science and Technology (MO)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking University (CN)
- University of Macau (MO)
- Sichuan University (CN)
- Wenzhou Medical University (CN)
- First Affiliated Hospital of Wenzhou Medical University (CN)
- Affiliated Eye Hospital of Wenzhou Medical College (CN)
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality (CN)
- Precision for Medicine (United States) (US)
- Dongguan People’s Hospital (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- Cell Reports Medicine
- Published
- 2025-07-01
- DOI
- https://doi.org/10.1016/j.xcrm.2025.102216
- Citations
- 7
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 9.52
Funders
- Wenzhou Medical University
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Beijing Association for Science and Technology
- Beijing Nova Program
- National Key Research and Development Program of China