Evidence map›Paper›PMID 36090636›Full record

ArticleTranslational lung cancer research2022

Survival of 7,311 lung cancer patients by pathological stage and histological classification: a multicenter hospital-based study in China.

Siyi He, He Li, Maomao Cao, Dianqin Sun, Fan Yang, Xinxin Yan, Shaoli Zhang, Yutong He, Lingbin Du, Xibin Sun and 7 more

Abstract read
In one paragraph

Article in Translational lung cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing 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

34 citing papers in PubMed.

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  14. Lung Cancer Outcomes in Vietnam: A 6-Year Retrospective Study.Journal of immunotherapy and precision oncology · 2025
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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

17 authors.

Siyi HeOffice of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China.
He LiOffice of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China.
Maomao CaoOffice of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China.
Dianqin SunOffice of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China.
Fan YangOffice of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China.
Xinxin YanOffice of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China.
Shaoli ZhangOffice of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China.
Yutong HeCancer Institute, the Fourth Hospital of Hebei Medical University/the Tumor Hospital of Hebei Province, Shijiazhuang, China.
Lingbin DuZhejiang Cancer Center, Hangzhou, China.
Xibin SunHenan Cancer Hospital, Zhengzhou, China.
Ning WangBeijing Cancer Hospital, Beijing, China.
Min ZhangHubei Cancer Hospital, Wuhan, China.
Kuangrong WeiZhongshan Cancer Hospital, Zhongshan, China.
Lin LeiDepartment of Cancer Prevention and Control, Shenzhen Center for Chronic Disease Control, Shenzhen, China.
Changfa XiaOffice of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China.
Ji PengDepartment of Cancer Prevention and Control, Shenzhen Center for Chronic Disease Control, Shenzhen, China.
Wanqing ChenOffice of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Representative prognostic data by clinical characteristics for lung cancer is not yet available in China. This study aimed to calculate the survival of lung cancer patients with different pathological evaluations, explore their predictive effects and provide information for prognosis improvement. Methods: In this multicenter cohort study, primary lung cancer patients diagnosed in 17 hospitals at three distinct levels in China between 2011-2013 were enrolled and followed up till 2020. Overall survival and lung cancer specific survival were calculated by Kaplan-Meier method. Cox proportional hazards model was applied to assess the effects of predictors of lung cancer survival. Results: Of all the 7,311 patients, the 5-year overall and lung cancer specific survival rates were 37.0% and 41.6%, respectively. For lung cancer patients at stages I, II, III, and IV, the 5-year overall survival rates were 76.9%, 56.1%, 32.6%, and 21.4%, respectively; the lung cancer specific survival rates were 82.3%, 59.7%, 37.2%, and 26.4%, respectively. Differences of survival for each stage remained significant between histological classifications (P<0.01). The 5-year overall survival rates for patients with squamous cell carcinoma, adenocarcinoma (AC), and small cell carcinoma were 36.9%, 43.3% and 27.9%, respectively; the corresponding disease-specific rates were 41.5%, 48.6% and 31.0%, respectively. Such differences were non-statistically significant at advanced stages (P=0.09). After multivariate adjustments, stage and classification remained independent predictors for the survival of lung cancer. Conclusions: The prognosis of lung cancer varied with the pathological stages and histological classifications, and had room for improvement. Stage was the strongest predictor, so efforts on early detection and treatment are needed.

Indexed as

histological classificationLung neoplasmsmulticenter studyneoplasm stagingsurvival analysis

Identifiers

PMID36090636
PMCPMC9459622

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LicenceCC BY-NC-ND
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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.