Evidence map›Paper›PMID 41803782›Full record

ArticleBMC cancer2026

Prediction of treatment outcome in nasopharyngeal carcinoma patients.

Guanzhi Zhou, Nanna M Sijtsema, Yan Li, Tiantian Zhai, Lihong Xi, Jun Li, Arjen van der Schaaf, Lisanne V van Dijk, Yingrui Shi, Pei Yang and 1 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

11 authors.

Guanzhi ZhouDepartment of Radiation Oncology, Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, No. 283 Tongzipo Road, Yuelu District, Changsha, Hunan, 410013, China.
Nanna M SijtsemaDepartment of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Yan LiDepartment of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Tiantian ZhaiDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Lihong XiDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Jun LiDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Arjen van der SchaafDepartment of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Lisanne V van DijkDepartment of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Yingrui ShiHunan Province Maternal and Child Care Hospital, The Maternal and Child Care Hospital of South University of China, Changsha, Hunan, China.
Pei YangDepartment of Radiation Oncology, Xiangya School of Medicine, Hunan Cancer Hospital, Central South University, No. 283 Tongzipo Road, Yuelu District, Changsha, Hunan, 410013, China. yangpei@hnca.org.cn.
Johannes A LangendijkDepartment of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCurrently, the American Joint Committee on Cancer staging systems do not reliably predict outcomes in nasopharyngeal carcinoma as patients with similar tumour stages and treatments often experience divergent outcomes. This study aimed to develop prediction models for overall survival (OS), progression-free survival (PFS), locoregional control (LRC), and distant control (DC) based on routinely collected clinical parameters in nasopharyngeal carcinoma patients treated with (chemo)radiation to support personalised treatment strategies.

methodsData from 956 patients treated with intensity-modulated radiotherapy at Hunan Cancer Hospital were used for model training, with 409 patients reserved for internal testing. An external cohort of 484 patients from the Cancer Hospital of Shantou University Medical College was used to validate the OS model. Eleven clinical variables were included with multivariate imputation applied to missing values. Highly correlated variables were excluded through pre-selection, followed by stepwise forward Cox regression modelling. Model performance was compared to AJCC staging for each endpoint.

resultsThe OS model achieved good performance in both the training set (C-index = 0.73, 95% CI [0.68–0.77]), internal test set (0.71 [0.64–0.77]) and the external test set (0.67 [0.61–0.72]). Both OS and PFS models performed significantly better than the AJCC models (DeLong’s test: both p < 0.01).

conclusionsModels for OS, PFS and DC effectively stratified patients into low and high-risk groups with significantly different outcomes, demonstrating their potential to guide individualised treatment decision.

Indexed as

Nasopharyngeal CarcinomaNasopharyngeal NeoplasmsAdultAgedChemoradiotherapyFemaleHumansMaleMiddle AgedNeoplasm StagingPrognosisRadiotherapy, Intensity-ModulatedTreatment OutcomeCox regressionEpstein-Barr virusNasopharyngeal carcinomaNomogramPrediction modelRisk stratificationTreatment outcome

Identifiers

PMID41803782
PMCPMC13154427

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Registered trials

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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.