Evidence map›Paper›PMID 32747627›Full record

ArticleNature communications2020

An optimal posttreatment surveillance strategy for cancer survivors based on an individualized risk-based approach.

Guan-Qun Zhou, Chen-Fei Wu, Bin Deng, Tian-Sheng Gao, Jia-Wei Lv, Li Lin, Fo-Ping Chen, Jia Kou, Zhao-Xi Zhang, Xiao-Dan Huang and 4 more

Abstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

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  17. Artificial intelligence and machine learning for medical imaging: A technology review.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Guan-Qun Zhou *State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.
Chen-Fei Wu *Zhongshan School of Medicine, Sun Yat-sen University, 510060, Guangzhou, China.
Bin Deng *Department of Radiation Oncology, Wuzhou Red Cross Hospital, Guangzhou, 543002, Guangxi, China.
Tian-Sheng GaoDepartment of Radiation Oncology, Wuzhou Red Cross Hospital, Guangzhou, 543002, Guangxi, China.
Jia-Wei LvState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.
Li LinState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.
Fo-Ping ChenState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.
Jia KouState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.
Zhao-Xi ZhangZhongshan School of Medicine, Sun Yat-sen University, 510060, Guangzhou, China.
Xiao-Dan HuangState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.
Zi-Qi ZhengState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.
Jun MaState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.
Jin-Hui LiangDepartment of Radiation Oncology, Wuzhou Red Cross Hospital, Guangzhou, 543002, Guangxi, China. 566jv@sina.cn.
Ying SunState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China. sunying@sysucc.org.cn.ORCID http://orcid.org/0000-0002-5888-2929

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Cancer SurvivorsAdultCost-Benefit AnalysisDisease-Free SurvivalFemaleHumansMaleMarkov ChainsMiddle AgedMonitoring, PhysiologicNasopharyngeal CarcinomaNasopharyngeal NeoplasmsNeoplasm Recurrence, LocalPrecision MedicineRisk Factors

Identifiers

PMID32747627
PMCPMC7400511

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.