Optimizing Strategy for Lung Cancer Screening: From Risk Prediction to Clinical Decision Support
Integrating ML and causal inference into clinical workflows offers a robust tool for enhancing lung cancer screening. This pipeline provides accurate risk assessments and actionable insights tailored to individuals, empowering clinicians and patients to make informed screening decisions. The differential risk reduction across subgroups highlights the importance of personalized screening in improving outcomes for populations at risk of lung cancer.
Authors
- Jiang Bian (ORCID: https://orcid.org/0000-0002-2238-5429)
- Yu Huang (ORCID: https://orcid.org/0000-0001-7373-4716)
- Xuhong Zhang (ORCID: https://orcid.org/0000-0002-8864-5326)
- Yi Guo (ORCID: https://orcid.org/0000-0003-0587-4105)
- Jingchuan Guo (ORCID: https://orcid.org/0000-0001-9799-2592)
- Tiancheng Zhou (ORCID: https://orcid.org/0000-0001-5173-6446)
- Xing He (ORCID: https://orcid.org/0000-0003-0290-8058)
- Hao Dai (ORCID: https://orcid.org/0000-0001-7950-3759)
- Yuxi Liu (ORCID: https://orcid.org/0000-0003-1265-7926)
Institutions
- Indiana University Health (US)
- Regenstrief Institute (US)
- University of Florida Health (US)
- University of Florida (US)
- Indiana University Bloomington (US)
- Indiana University School of Medicine
- Indiana University – Purdue University Indianapolis (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1200/cci-24-00291
- Citations
- 3
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.56