Bi-regional and bi-phasic automated machine learning radiomics for defining metastasis to lesser curvature lymph node stations in gastric cancer.
Authors
Affiliations (17)
Affiliations (17)
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Department of Gastric Cancer Surgery, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, Tianjin, 300060, China.
- Department of Radiology, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060, China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- State Key Laboratory of Cancer Biology and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, 710000, Xi'an, China.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. [email protected].
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China. [email protected].
- National Key Laboratory of Kidney Diseases, Beijing, 100853, China. [email protected].
- Department of Gastric Cancer Surgery, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, Tianjin, 300060, China. [email protected].
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China. [email protected].
- Cancer center of Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine，Tsinghua University, 102218, Beijing, China. [email protected].
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. [email protected].
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China. [email protected].
- National Key Laboratory of Kidney Diseases, Beijing, 100853, China. [email protected].
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China. [email protected].
Abstract
Lymph node metastasis (LNM) is the primary metastatic mode in gastric cancer (GC), with frequent occurrences in lesser curvature. This study aims to establish a radiomic model to predict the metastatic status of lymph nodes in the lesser curvature for GC. We retrospectively collected data from 939 gastric cancer patients who underwent gastrectomy and D2 lymphadenectomy across two centers. Both the primary lesion and the lesser curvature region were segmented as representative region of interests (ROIs). The combination of bi-regional and bi-phasic CT imaging features were used to build a hybrid radiomic model to predict LNM in the lesser curvature. And the model was validated internally and externally. Further, the potential generalization ability of the hybrid model was investigated in predicting the metastasis status in the supra-pancreatic area. The hybrid model yielded substantially higher performance with AUCs of 0.847 (95% CI, 0.770-0.924) and 0.833 (95% CI, 0.800-0.867) in the two independent test cohorts, compared to the single regional and phasic models. Additionally, the hybrid model achieved AUCs ranging from 0.678 to 0.761 in the prediction of LNM in supra-pancreatic area, showing the potential generalization performance. The CT imaging features of primary tumor and adjacent tissues are significantly associated with LNM. And our as-developed model showed great diagnostic performance and might be of great application in the individual treatment of GC.