Predicting pathological complete response following neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer using merged model integrating MRI-based radiomics and deep learning data
The pre-nCRT merged model including clinical factors, Radscore and DL model constitutes an effective non-invasive tool for pCR prediction in LARC.
Authors
- Yong Lu (ORCID: https://orcid.org/0000-0002-2528-3655)
- Minglu Liu
- Fu Shen
- Jianping Lu (ORCID: https://orcid.org/0000-0002-0380-7470)
- Haidi Lu
- Yuwei Xia (ORCID: https://orcid.org/0000-0002-2248-2296)
- Zhihui Li (ORCID: https://orcid.org/0000-0001-5356-932X)
- Xiaolu Ma (ORCID: https://orcid.org/0000-0002-2610-7450)
- Feng Shi (ORCID: https://orcid.org/0000-0003-3509-3473)
- Yuan Yuan (ORCID: https://orcid.org/0009-0002-9540-2100)
Institutions
- Second Military Medical University (CN)
- Ruijin Hospital (CN)
- Changhai Hospital (CN)
- United Imaging Healthcare (China) (CN)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2024-10-24
- DOI
- https://doi.org/10.1186/s12880-024-01474-3
- Citations
- 6
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.43
Funders
- National Natural Science Foundation of China