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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Liu, Yunsong Wang, Yi Wang, Xin Xue, Liyan Zhang, Huan Ma, Zeliang Deng, Heping Yang, Zhaoyang Sun, Xujie Men, Yu Ye, Feng Men, Kuo Qin, Jianjun Bi, Nan Wang, Qifeng Hui, Zhouguang |
| Abstract | Background More than 40% of patients with resectable esophageal squamous cell cancer (ESCC) achieve pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT), who have favorable prognosis and may benefit from an organ-preservation strategy. Our study aims to develop and validate a machine learning model based on MR radiomics to accurately predict the pCR of ESCC patients after nCRT. Methods In this retrospective multicenter study, eligible patients with ESCC who underwent baseline MR (T2-weighted imaging) and nCRT plus surgery were enrolled between September 2014 and September 2022 at institution 1 (training set) and between December 2017 and August 2021 at institution 2 (testing set). Models were constructed using machine learning algorithms based on clinical factors and MR radiomics to predict pCR after nCRT. The area under the curve (AUC) and cutoff analysis were used to evaluate model performance. Results A total of 155 patients were enrolled in this study, 82 in the training set and 73 in the testing set. The radiomics model was constructed based on two radiomics features, achieving AUCs of 0.968 (95%CI 0.933–0.992) in the training set and 0.885 (95%CI 0.800-0.958) in the testing set. The cutoff analysis resulted in an accuracy of 82.2% (95%CI 72.6-90.4%), a sensitivity of 75.0% (95%CI 58.3-91.7%), and a specificity of 85.7% (95%CI 75.5-96.0%) in the testing set. Conclusion A machine learning model based on MR radiomics was developed and validated to accurately predict pCR after nCRT in patients with ESCC. |
| Related Links | https://cancerimagingjournal.biomedcentral.com/counter/pdf/10.1186/s40644-024-00659-x.pdf |
| Ending Page | 10 |
| Page Count | 10 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14707330 |
| DOI | 10.1186/s40644-024-00659-x |
| Journal | Cancer Imaging |
| Issue Number | 1 |
| Volume Number | 24 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-01-23 |
| Access Restriction | Open |
| Subject Keyword | Oncology Cancer Research Imaging Radiology Nuclear Medicine Esophageal neoplasms Magnetic resonance Treatment outcome Neoadjuvant Chemoradiotherapy |
| Content Type | Text |
| Resource Type | Article |
| Subject | Radiological and Ultrasound Technology Radiology, Nuclear Medicine and Imaging Oncology |
| Journal Impact Factor | 3.5/2023 |
| 5-Year Journal Impact Factor | 4.3/2023 |
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