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Clinical Prognostic Factors in Gastric Cancer in Chinese Patients: Experience from the Cancer Hospital/Institute, Chinese Academy of Medical Sciences Yuankai Shi, M.D. Department of Medical Oncology, Cancer Hospital/Institute, Chinese Academy of Medical Sciences (CAMS)
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Background 4th most common cancer, 2nd most common cause of death, 2/3 patients in developing countries, 42% of them in China Gastric cancer worldwide in 2002: Parkin DM, et al. CA Cancer J Clin. 2005;55:74
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5-year overall survival: 15%-54% Parkin DM, et al. CA Cancer J Clin. 2005;55:74
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Gastric cancer in China in 2005 Incidence: 376,000 new cases 376,000 new cases Third most common cancer Third most common cancer Mortality: 24.71/10 24.71/10 5 Third from cancer Third cause of death from cancer Behind Lung cancer and Liver cancer Behind Lung cancer and Liver cancer http://www.moh.gov.cn
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Objective To evaluate the therapeutic effects and prognostic factors in patients with operable gastric cancer To construct a prognostic model
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Materials and methods Identify prognostic factors with data from 1043 pts treated with combined modality therapy based on gastrectomy, between 1999 and 2002, in the Cancer Hospital, CAMS Construct prognostic model with data from 1284 pts with combined modality treatment based on gastrectomy, between 1999-2006, in the Cancer Hospital, CAMS Statistical analysis Life Table, Cox hazard proportional model, Logistic regression model Life Table, Cox hazard proportional model, Logistic regression model
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Results 5-year 39% 15% 5-year OS 100% 91% 84% 61% 33% 19% 5% Median survival: 39.5 mo median relapse-free survival: 22.7 mo 5-y OS: 39% 5-y RFS: 15% 1043 pts with operable gastric cancer median follow-up: 51.6 mo
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Univariate analysis in 1043 pts
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Independent prognostic factors
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All pts n=1284 Training sample n=963 Testing sample n=321 Randomized Prognostic equation Prognostic index Prognostic model Construct Validate
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Fig. Overall survival (OS) and relapse-free survival (RFS) for 1284 patients. Overall survival and relapse-free survival Median follow-up: 35.7 mo 5-y OS: 40% 5-y RFS: 12%
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Univariate analysis in 1284 pts
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Independent prognostic factors of OS in training sample
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Prognostic model LNM: Lymphonode metastases; PCI: peritoneal cavity involvement. FactorsScore Age>60y1 Proximal1 Stage Ⅲ / Ⅳ 2 LNM ratio>1/3 2 PCI2 Risk group Score Low risk (L) 0-1 Low intermediate (LI) 2-3 High intermediate (HI) 4-5 High risk (H) 6-8
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Risk model defined by prognostic index
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Risk model in age group 5-year OS 82% 60% 27% 7% 5-y OS 81% 48% 20% 0 P<0.001 5-y OS 82% 60% 27% 7%
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Risk model in stage
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Survival curve according to risk model LI L L
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Risk model in treatment
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Discussion Lagarde SM, et al. J Clin Oncol, 2006, 24:4347
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AuthorTimeNPatients Independent prognostic factors Zhan2005497curative Stage, Size, Perioperative chemotherapy Yolota2002697operable Site, T category, LNM Schwarz20071377operableIIIA-IV Age, Site, Number of LNM, T category, Sex The present study1043operable Age, Site, Size, Stage, Ratio of LNM, PCI, Curative resection, Combined therapy Comparison in gastric cancer prognostic factors Zhan YQ, et al. Ai Zheng 2005,24:596; Yokota T, et al. Anticancer Res 2002,22:3673; Schwarz RE et al. Ann Surg Oncol 2007,14:317
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Conclusion This risk model could identify various outcomes in pts with the same stage from IB to IV This risk model also could identify different outcomes in pts with the same treatment status Prospective study are warranted to validate these findings.
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Acknowledgements Dept. of Abdominal Surgical Oncology Zhi-xiang Zhou Zhi-xiang Zhou Hai-zeng Zhang Hai-zeng Zhang Yi Shan Yi Shan Ping Zhao Ping Zhao Xiang Wang Xiang Wang Jian-xiong Wu Jian-xiong Wu Yong-fu Zhao Yong-fu Zhao Dept. of Medical Oncology Jing Wang Jing Wang Jin-wan Wang Jin-wan Wang Hong-gang Zhang Hong-gang Zhang Yu-sheng Li Yu-sheng Li Jing Huang Jing Huang Xiao-hui He Xiao-hui He Xiao-hong Han Xiao-hong Han Dept. of Pathology Xun Zhang Xun Zhang Yan Song Yan Song Shang-mei Liu Shang-mei Liu Dept. of Imaging Diagnosis Chun-wu Zhou Chun-wu Zhou Li-ming Jiang Li-ming Jiang Zhu Wang Zhu Wang
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Thank you for your attention!
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