1
School of Medicine and Life Sciences Chengdu University of Traditional Chinese Medicine Chengdu 611137 Sichuan
China
2
Institute for Emergency and Disaster Medicine Sichuan Clinical Research Center for Emergency and Critical Care Sichuan
Provincial People's Hospital Affiliated Hospital of University of Electronic Science and Technology of China Chengdu 610072 Sichuan
China
3 Gastrointestinal Surgery Sichuan Provincial People 's Hospital Affiliated Hospital of University of Electronic Science and
Technology of China Chengdu 610072 Sichuan China
4
Sichuan Provincial Center for Emergency Medicine Sichuan Provincial
People' s Hospital Affiliated Hospital of University of Electronic Science and Technology of China Chengdu 610072 Sichuan
China
5Department of Mathematics University of South Carolina Columbia SC 29208 United States
Abstract：Background Colorectal cancer ranks among the top three most prevalent cancers worldwide posing a significant
challenge despite surgical intervention. Detecting tumor recurrence early is paramount to improving the survival outcomes of patients
post-colorectal cancer surgery. Presently recurrence prediction relies heavily on clinicians' subjective judgment drawing from clinical
examination blood tests imaging and other clinical data. However this method lacks objective reliability and risks patients missing
optimal intervention opportunities. While new diagnostic methods have emerged they are often costly and challenging to implement
universally. Moreover they typically operate reactively detecting tumors only after significant in vivo development serving as a
debriefing mode. Method This study is based on retrospective cohort data collected from patients with colorectal cancer admitted to
the Department of Gastrointestinal Surgery and Emergency Surgery at Sichuan Provincial People 's Hospital Affiliated Hospital of
University of Electronic Science and Technology of China between January 2013 and October 2018. Stringent exclusion criteria will be
applied during patient selection and clinical data from colon cancer patients are gathered spanning five years before and after
surgery. The collected data will undergo thorough cleaning preprocessing classification and enrichment processes to create an AI-ready dataset. Leveraging a combination of data-driven and mechanistic modeling approaches we aim to develop a digital twin
model capable of monitoring and predicting colorectal cancer recurrence within a five-year timeframe. Conclusion Current studies have
shown that the recurrence rate of colorectal cancer five years after surgery is as high as 30% and a reliable tool for early warning of
postoperative recurrence of colorectal cancer is urgently needed in clinic. Digital twin technology can realize multi-dimensional data
processing establish multi -module model prediction and establish a prediction model with time series which is suitable for the
prediction of colorectal cancer patients' recurrence. Based on digital twin technology this study intends to establish a prediction model
of tumor recurrence in patients with colorectal cancer within 5 years after surgery in order to achieve the monitoring and early warning
of tumor recurrence and reduce the mortality of patients with colorectal cancer recurrence after surgery.
1,2谭 馨,3罗 斌,4陈宁波,5王 奇,2江 华. 结直肠癌患者复发风险监测与预测的数字孪生模型:一项临床研究方案[J]. 肿瘤代谢与营养电子杂志, 2024, 11(2): 264-269.
1,2Tan Xin,3Luo Bin,4Chen Ningbo,5Wang Qi,2Jiang Hua. Digital twin for monitoring and predicting recurrence risk in colorectal cancer patients a clinical study protocol. Electron J Metab Nutr Cancer, 2024, 11(2): 264-269.
