Evidence map›Paper›PMID 40170185›Full record

ArticleEuropean journal of medical research2025

Identifying chemotherapy beneficiaries in nasal and paranasal sinus cancers: epidemiological trends and machine learning insights.

Zihan Chen, Zongwei Huang, Yuhui Pan, Youliang Weng, Zijie Wu, Jing Wang, Wenxi Wu, Xinyi Hong, Xin Chen, Sufang Qiu

Abstract read
In one paragraph

Article in European journal of medical research, 2025. 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

10 authors.

Zihan Chen *Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital (Fujian Branch of Fudan University Shanghai Cancer Center), Fuzhou, Fujian, China.
Zongwei Huang *Department Radiation oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital (Fujian Branch of Fudan University Shanghai Cancer Center), Fuzhou, 350014, Fujian, China.
Yuhui PanDepartment Radiation oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital (Fujian Branch of Fudan University Shanghai Cancer Center), Fuzhou, 350014, Fujian, China.
Youliang WengDepartment Radiation oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital (Fujian Branch of Fudan University Shanghai Cancer Center), Fuzhou, 350014, Fujian, China.
Zijie WuDepartment Radiation oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital (Fujian Branch of Fudan University Shanghai Cancer Center), Fuzhou, 350014, Fujian, China.
Jing WangDepartment Radiation oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital (Fujian Branch of Fudan University Shanghai Cancer Center), Fuzhou, 350014, Fujian, China.
Wenxi WuClinical Oncology School of Fujian Medical University, Fujian Cancer Hospital (Fujian Branch of Fudan University Shanghai Cancer Center), Fuzhou, Fujian, China.
Xinyi HongClinical Oncology School of Fujian Medical University, Fujian Cancer Hospital (Fujian Branch of Fudan University Shanghai Cancer Center), Fuzhou, Fujian, China.
Xin ChenDepartment Radiation oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital (Fujian Branch of Fudan University Shanghai Cancer Center), Fuzhou, 350014, Fujian, China.
Sufang QiuDepartment Radiation oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital (Fujian Branch of Fudan University Shanghai Cancer Center), Fuzhou, 350014, Fujian, China. sufangqiu@fjmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStudies on the epidemiological characteristics, treatment strategies and prognosis of nasal and paranasal sinus cancer are still relatively limited.

methodsThis study analyzed the age-adjusted incidence rates of nasal and paranasal sinus cancer from 1975 to 2020 using SEER database data. We conducted an in-depth examination of patients diagnosed between 2004 and 2015 with SEER*Stat software. A retrospective study from Fujian Provincial Cancer Hospital (2013-2020) provided an external validation set. Multiple imputation methods in R were used to address missing data. Survival analyses were performed using Kaplan-Meier and Cox proportional hazards models. Additionally, ten advanced machine learning models were utilized and evaluated in Python to predict patient survival outcomes.

resultsThis study analyzed data from 3,190 patients. The annual percent change (APC) in incidence rates per 100 000 person-years was 0.36 until 2012, subsequently decreasing to - 1.79. Among various predictive models, the gradient boosting classifier demonstrated superior performance with an area under the curve (AUC) of 0.699 and an accuracy rate of 0.708. Chemotherapy did not significantly influence overall mortality risk (HR = 0.93, 95% CI 0.82-1.05, P = 0.27). Chemotherapy showed potential benefits in specific patient subgroups.

conclusionsThis study revealed a declining trend in incidence rates beginning in 2012. The gradient boosting model demonstrated robust performance, playing a crucial role in predicting patient prognosis and the significance of chemotherapy.

Indexed as

Machine LearningAgedAged, 80 and overArea Under CurveDatasets as TopicFemaleHumansIncidenceMaleMiddle AgedParanasal Sinus NeoplasmsPrognosisChemotherapyEpidemiologyMachine learningNasal cavity and paranasal sinus cancersPrognosis

Identifiers

PMID40170185
PMCPMC11959763

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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.