Consistency mapping of 16 lymph node stations in gastric cancer by CT-based vessel-guided delineation of 255 patients
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Shuhang Xu1,*, Lingling Feng2,3,*, Yongming Chen2,4,*, Ying Sun2,3, Yao Lu5, Shaomin Huang2,3, Yang Fu6, Rongqin Zheng1, Yujing Zhang2,3 and Rong Zhang2,7
1Department of Ultrasound, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China
2State Key Laboratory of Oncology in Southern China, Guangzhou 510060, China
3Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
4Department of Gastric Surgery, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
5Guangdong Province Key Laboratory of Computational Science, School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, China
6Department of Statistical Science, Sun Yat-Sen University School of Mathematics, Guangzhou 510275, China
7Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
*These authors have contributed equally to this work
Rong Zhang, email: [email protected]
Yujing Zhang, email: [email protected]
Rongqin Zheng, email: [email protected]
Keywords: gastric cancer, lymph node stations, mapping, computer software, clinical target volume
Received: November 30, 2016 Accepted: May 21, 2017 Published: June 08, 2017
In order to refine the location and metastasis-risk density of 16 lymph node stations of gastric cancer for neoadjuvant radiotherapy, we retrospectively reviewed the initial images and pathological reports of 255 gastric cancer patients with lymphatic metastasis. Metastatic lymph nodes identified in the initial computed tomography images were investigated by two radiologists with gastrointestinal specialty. A circle with a diameter of 5 mm was used to identify the central position of each metastatic lymph node, defined as the LNc (the central position of the lymph node). The LNc was drawn at the equivalent location on the reference images of a standard patient based on the relative distances to the same reference vessels and the gastric wall using a Monaco® version 5.0 workstation. The image manipulation software Medi-capture was programmed for image analysis to produce a contour and density atlas of 16 lymph node stations. Based on a total of 2846 LNcs contoured (31–599 per lymph node station), we created a density distribution map of 16 lymph node drainage stations of the stomach on computed tomography images, showing the detailed radiographic delineation of each lymph node station as well as high-risk areas for lymph node metastasis. Our mapping can serve as a template for the delineation of gastric lymph node stations when defining clinical target volume in pre-operative radiotherapy for gastric cancer.
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