AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma
The molecular imaging strategy for minimally invasive lymphoma diagnosis that can achieve a recognition rate of ~ 91.7 ± 2.1%. We anticipate that these results will catalyze the development of a series of histological SERS-AI technologies for diagnosing various diseases, including other types of cancer. In this work, we present a reliable tool to facilitate clinicians in the diagnosis of lymphoma.
Authors
- Houyu Wang (ORCID: https://orcid.org/0000-0002-5134-9881)
- Xiaofeng Wu (ORCID: https://orcid.org/0000-0002-8961-2163)
- Yao He (ORCID: https://orcid.org/0000-0003-1672-4057)
- Huayi Shi
- Haiting Cao
- Binbin Chu (ORCID: https://orcid.org/0000-0001-6549-7909)
- Fenglin Dong
Institutions
- Soochow University (CN)
- First Affiliated Hospital of Soochow University (CN)
Publication Details
- Journal
- Journal of Nanobiotechnology
- Published
- 2025-04-16
- DOI
- https://doi.org/10.1186/s12951-025-03339-5
- Citations
- 7
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 9.52
Funders
- National Natural Science Foundation of China
- Soochow University
- Priority Academic Program Development of Jiangsu Higher Education Institutions
- Collaborative Innovation Center of Suzhou Nano Science and Technology
- Higher Education Discipline Innovation Project