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- Title
An optimal posttreatment surveillance strategy for cancer survivors based on an individualized risk-based approach.
- Authors
Zhou, Guan-Qun; Wu, Chen-Fei; Deng, Bin; Gao, Tian-Sheng; Lv, Jia-Wei; Lin, Li; Chen, Fo-ping; Kou, Jia; Zhang, Zhao-Xi; Huang, Xiao-Dan; Zheng, Zi-Qi; Ma, Jun; Liang, Jin-Hui; Sun, Ying
- Abstract
The optimal post-treatment surveillance strategy that can detect early recurrence of a cancer within limited visits remains unexplored. Here we adopt nasopharyngeal carcinoma as the study model to establish an approach to surveillance that balances the effectiveness of disease detection versus costs. A total of 7,043 newly-diagnosed patients are grouped according to a clinic-molecular risk grouping system. We use a random survival forest model to simulate the monthly probability of disease recurrence, and thereby establish risk-based surveillance arrangements that can maximize the efficacy of recurrence detection per visit. Markov decision-analytic models further validate that the risk-based surveillance outperforms the control strategies and is the most cost-effective. These results are confirmed in an external validation cohort. Finally, we recommend the risk-based surveillance arrangement which requires 10, 11, 13 and 14 visits for group I to IV. Our surveillance strategies might pave the way for individualized and economic surveillance for cancer survivors. Monitoring patients for the recurrence of cancer can be costly and it is important to devise the optimum strategy for a given cancer and population. Here, the authors use nasopharyngeal cancer as a model and show using patient data an optimal follow-up schedule to detect recurrence of the cancer.
- Subjects
CANCER survivors; CANCER relapse; NASOPHARYNX cancer; DISEASE relapse; MARKOV processes
- Publication
Nature Communications, 2020, Vol 11, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-020-17672-w