A Novel Tool for Predicting Malignant Disease in Adult Patients With Dermatomyositis
This cohort study found that the TIP-CA model effectively stratified cancer-associated likelihood in patients with DM using routinely available clinical data. By using data from multidisciplinary patient cohorts and incorporating machine learning techniques, the model minimized referral bias. This proposed model may have the potential to guide clinicians in implementing targeted cancer screening strategies and improve patient outcomes.
Authors
- Mingyu Ye (ORCID: https://orcid.org/0009-0009-6803-434X)
- Qian Zhao (ORCID: https://orcid.org/0000-0002-2277-6243)
- Chengqiang Xia (ORCID: https://orcid.org/0000-0001-7439-7280)
- Shuang Ye (ORCID: https://orcid.org/0000-0002-5257-5768)
- Kaile He (ORCID: https://orcid.org/0000-0001-6903-0054)
- Jiaming Teng (ORCID: https://orcid.org/0009-0003-3309-2802)
- Haixi Wu
- Hua Cao (ORCID: https://orcid.org/0000-0001-5908-6206)
- Wanlong Wu (ORCID: https://orcid.org/0000-0003-2727-7950)
- Hao Li (ORCID: https://orcid.org/0000-0001-7900-0852)
- Jie Zheng (ORCID: https://orcid.org/0000-0002-7961-6427)
- Xiaoqing Zhao (ORCID: https://orcid.org/0000-0002-9232-1412)
- Jiaqi Ye (ORCID: https://orcid.org/0000-0002-5824-2518)
Institutions
- Shanghai Jiao Tong University (CN)
- Renji Hospital (CN)
- Ruijin Hospital (CN)
Publication Details
- Journal
- JAMA Dermatology
- Published
- 2025-12-03
- DOI
- https://doi.org/10.1001/jamadermatol.2025.4824
- Citations
- 2
- Primary Topic
- Inflammatory Myopathies and Dermatomyositis
- Type
- article
- Field-Weighted Citation Impact
- 2.71