Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer
Creators
- 1. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- 2. Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), Radiology Department, Peking University Cancer Hospital & Institute, Beijing, China
- 3. Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangshu, China
- 4. The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- 5. Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
- 6. Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
- 7. Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
- 8. Department of Radiology, Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy
Description
CTNomogram4AGC-N: A CT based deep learning radiomic nomogram (DLRN) for preoperative N staging in patients with advanced gastric cancer was developed and validated on multi-center datasets. The DLRN has good predictive ability for preoperatively discriminating pathologic N0, N1, N2, N3a, and N3b, i.e. the accurate categorization. We suggest the clinicians and physicians to use it to supplement the clinical judgment. To use this model, one should outline the 2D tumor region in the images of arterial phase CT, venous phase CT, and unenhanced CT. Three radiomic signatures could be obtained by combining 19 radiomic features, including deep learning features and hand-crafted features. Two signatures and the CT-finding N staging are input into the final DLRN. Then the possible exact N stage can be given.
Our study on this nomogram has been submitted to Annals of Oncology.
Files
sample.zip
Files
(242.2 MB)
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