Imatinib adherence prediction using machine learning approach in patients with gastrointestinal stromal tumor
This study represents the first real-world investigation using ML techniques to predict risk factors associated with imatinib nonadherence in patients with GIST. By highlighting the potential factors and identifying high-risk patients, the multidisciplinary medical team can devise targeted strategies to effectively address the daily challenges of treatment adherence.
Authors
- Linhua Chen
- Xiangyu Zeng (ORCID: https://orcid.org/0000-0003-4913-3704)
- Ze Yu (ORCID: https://orcid.org/0000-0002-4727-8427)
- Li Liu (ORCID: https://orcid.org/0000-0001-8523-9720)
- Zhiliang Chen (ORCID: https://orcid.org/0000-0001-5575-0501)
- Jinyuan Zhang (ORCID: https://orcid.org/0009-0001-2605-5486)
- Xiao Huang (ORCID: https://orcid.org/0000-0003-0686-9113)
- Zhou Hong (ORCID: https://orcid.org/0000-0003-2072-2652)
- Hefen Chen
- Ziying Fan
- Zhujun Gong
- Hongkun Tian
- Jiannan Yan
- Peng Zhang (ORCID: https://orcid.org/0009-0004-7146-1805)
Institutions
- Union Hospital (HK)
- Wuhan Union Hospital (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Cancer
- Published
- 2024-09-06
- DOI
- https://doi.org/10.1002/cncr.35548
- Citations
- 3
- Primary Topic
- Gastrointestinal Tumor Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- National Key Research and Development Program of China