结直肠癌是全球发病率第三位的恶性肿瘤。TNM分期系统是判断预后、指导辅助治疗和进行临床研究的重要工具。我们发现，沿用传统线性设计模式的TNM分期低估了无淋巴结转移患者的预后风险，导致II期预后劣于IIIa期的“反常现象”；对于非转移性结直肠癌，TNM分期只考虑肿瘤浸润深度和淋巴结转移两个因素，忽略了其他预后相关的病理信息。我们提出了非线性的T-plus分期系统，并证明其判断预后的能力优于TNM分期系统。同时，首次发现多个因素对结直肠癌预后的影响呈现时间依赖现象。本研究拟基于SEER和NCDR数据库及浙江大学组织库，用机器学习，整合解剖和分子病理信息，构建考虑时间依赖现象的多因素非线性预后预测系统，以精准预测非转移性结直肠癌患者预后；并构建移动应用和电子化随访登记系统，为开展前瞻性验证研究提供技术平台。研究结果对于指导精准的个体化辅助治疗从而提高生存率和开展前瞻性的临床研究具有重要意义。

Colorectal cancer is the third most common cancer globally. The AJCC (American Joint Committee on Cancer) TNM staging system is widely used to predict prognosis for patients with colorectal cancer, to guide adjuvant therapy after potentially curative surgery and to design clinical trials. Our previous researches found that TNM staging system underestimates the hazard of patients without lymph nodes involved which induced the survival paradox between stage IIIa and stage II. Moreover, for non-metastasis colorectal cancer patients, TNM staging system cover only local invasion depth (T stage) and lymph node involvement (N stage) without the consideration of other prognostic pathological factors. We proposed the nonlinear T-plus staging system which showed better performance to predict the prognosis of colorectal cancer than did by TNM staging system. Moreover, we found that several prognostic factors showed time-related hazard ratio for colorectal cancer patients. This phenomenon was never reported to the best of our knowledge. The SEER and NCDR databases and the Tissue Bank of Zhejiang University will be used in this study for data mining. The factors of anatomical pathology and molecular pathology will be synthesized by the artificial intelligence technology machine learning algorithms to construct the time-related multi-factors nonlinear prognosis predictive system to predict accurately the prognosis of non-metastasis colorectal cancer patients. The mobile application and electronic follow-up registration system for the future prospective validation study. The study results are of important significance to predict accurately the prognosis of colorectal cancer patients, guide to individual adjuvant therapy to improve the long term survival and design prospective clinical trials.