Wuchao Li1,2,3, Liwen Zhang2,4, Chong Tian1,3, Hui Song1,3, Mengjie Fang2, Chaoen Hu2, Yali Zang2, Ying Cao3,5, Shiyuan Dai3,6, Fang Wang3,7, Di Dong2,4, Rongpin Wang8,9, Jie Tian10,11,12. 1. Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China. 2. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. 3. Guizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China. 4. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China. 5. Department of Pathology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China. 6. Department of Medical Records and Statistics, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China. 7. Department of General Surgery, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China. 8. Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China. wangrongpin@126.com. 9. Guizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China. wangrongpin@126.com. 10. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. tian@ieee.org. 11. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China. tian@ieee.org. 12. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China. tian@ieee.org.
Abstract
OBJECTIVES: The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric cancer who had undergone radical resection. METHODS: A total of 181 patients with gastric cancer who had undergone radical resection were enrolled in this retrospective study. The association between the R-signature and overall survival (OS) was assessed in the primary cohort and verified in the validation cohort. Furthermore, the performance of a radiomics nomogram integrating the R-signature and significant clinicopathological risk factors was evaluated. RESULTS: The R-signature, which consisted of six imaging features, stratified patients with gastric cancer who had undergone radical resection into two prognostic risk groups in both cohorts. The radiomics nomogram incorporating R-signature and significant clinicopathological risk factors (T stage, N stage, and differentiation) exhibited significant prognostic superiority over clinical nomogram and R-signature alone (Harrell concordance index, 0.82 vs 0.71 and 0.82 vs 0.74, respectively, p < 0.001 in both analyses). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the radiomics nomogram for clinical practice. CONCLUSIONS: The R-signature could be used to stratify patients with gastric cancer following radical resection into high- and low-risk groups. Furthermore, the radiomics nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling. KEY POINTS: • Radiomics can stratify the gastric cancer patients following radical resection into high- and low-risk groups. • Radiomics can improve the prognostic value of TNM staging system. • Radiomics may facilitate personalized treatment of gastric cancer patients.
OBJECTIVES: The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric cancer who had undergone radical resection. METHODS: A total of 181 patients with gastric cancer who had undergone radical resection were enrolled in this retrospective study. The association between the R-signature and overall survival (OS) was assessed in the primary cohort and verified in the validation cohort. Furthermore, the performance of a radiomics nomogram integrating the R-signature and significant clinicopathological risk factors was evaluated. RESULTS: The R-signature, which consisted of six imaging features, stratified patients with gastric cancer who had undergone radical resection into two prognostic risk groups in both cohorts. The radiomics nomogram incorporating R-signature and significant clinicopathological risk factors (T stage, N stage, and differentiation) exhibited significant prognostic superiority over clinical nomogram and R-signature alone (Harrell concordance index, 0.82 vs 0.71 and 0.82 vs 0.74, respectively, p < 0.001 in both analyses). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the radiomics nomogram for clinical practice. CONCLUSIONS: The R-signature could be used to stratify patients with gastric cancer following radical resection into high- and low-risk groups. Furthermore, the radiomics nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling. KEY POINTS: • Radiomics can stratify the gastric cancerpatients following radical resection into high- and low-risk groups. • Radiomics can improve the prognostic value of TNM staging system. • Radiomics may facilitate personalized treatment of gastric cancerpatients.
Authors: Virendra Kumar; Yuhua Gu; Satrajit Basu; Anders Berglund; Steven A Eschrich; Matthew B Schabath; Kenneth Forster; Hugo J W L Aerts; Andre Dekker; David Fenstermacher; Dmitry B Goldgof; Lawrence O Hall; Philippe Lambin; Yoganand Balagurunathan; Robert A Gatenby; Robert J Gillies Journal: Magn Reson Imaging Date: 2012-08-13 Impact factor: 2.546
Authors: Philippe Lambin; Emmanuel Rios-Velazquez; Ralph Leijenaar; Sara Carvalho; Ruud G P M van Stiphout; Patrick Granton; Catharina M L Zegers; Robert Gillies; Ronald Boellard; André Dekker; Hugo J W L Aerts Journal: Eur J Cancer Date: 2012-01-16 Impact factor: 9.162
Authors: Jianjun Zhang; Junya Fujimoto; Jianhua Zhang; David C Wedge; Xingzhi Song; Jiexin Zhang; Sahil Seth; Chi-Wan Chow; Yu Cao; Curtis Gumbs; Kathryn A Gold; Neda Kalhor; Latasha Little; Harshad Mahadeshwar; Cesar Moran; Alexei Protopopov; Huandong Sun; Jiabin Tang; Xifeng Wu; Yuanqing Ye; William N William; J Jack Lee; John V Heymach; Waun Ki Hong; Stephen Swisher; Ignacio I Wistuba; P Andrew Futreal Journal: Science Date: 2014-10-10 Impact factor: 47.728