Predicting anastomotic leak in patients with esophageal squamous cell cancer treated with neoadjuvant chemoradiotherapy using a nomogram based on CT radiomic and clinicopathologic factors
We developed and validated a nomogram based on radiomics and clinicopathologic characteristics. This predictive model could be a powerful tool to predict AL occurrence in patients with ESCC treated with NCRT.
Authors
- Haining Luo (ORCID: https://orcid.org/0000-0003-2409-3355)
- Junfeng Zhao (ORCID: https://orcid.org/0009-0000-1935-9214)
- Shanshan Li (ORCID: https://orcid.org/0000-0002-5905-5247)
- Ying Li (ORCID: https://orcid.org/0000-0001-5062-0230)
- Baosheng Li (ORCID: https://orcid.org/0000-0002-5390-7269)
- Dan Han (ORCID: https://orcid.org/0000-0002-0408-9175)
- Qiang Cao (ORCID: https://orcid.org/0000-0001-8160-8883)
- Guanli Yang
Institutions
- Binzhou Medical University (CN)
- Shandong University of Aeronautics (CN)
- Shandong Tumor Hospital (CN)
- Second Hospital of Shandong University (CN)
- The Central Hospital of Xiao gan (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-03-15
- DOI
- https://doi.org/10.1186/s12885-025-13884-9
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36