Whole slide image based prognosis prediction in rectal cancer using unsupervised artificial intelligence
The developed prognostic signature can effectively predict PFS in patients with rectal cancer and exploration of the underlying biological mechanisms may help to promote its clinical translation.
Authors
- Zhixian Gao (ORCID: https://orcid.org/0000-0003-2818-6671)
- Yandong Zhao (ORCID: https://orcid.org/0000-0001-8661-0229)
- Zongya Zhao (ORCID: https://orcid.org/0000-0002-6282-8001)
- Chong Wang (ORCID: https://orcid.org/0000-0002-0541-2210)
- Chang Wang (ORCID: https://orcid.org/0009-0005-8629-7742)
- Yi Yu (ORCID: https://orcid.org/0009-0009-0277-8813)
- Xuezhi Zhou (ORCID: https://orcid.org/0000-0002-4501-1817)
- Qingqing Zhao (ORCID: https://orcid.org/0000-0002-2363-1935)
- Jing Dai (ORCID: https://orcid.org/0009-0009-2385-9436)
- Yizhan Lu
- Wuteng Cao
- Yong Liu (ORCID: https://orcid.org/0000-0001-6037-4926)
Institutions
- Sun Yat-sen University (CN)
- Sixth Affiliated Hospital of Sun Yat-sen University (CN)
- Henan Medical University (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12885-024-13292-5
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.62
Funders
- National Natural Science Foundation of China
- Henan University