Evidence map›Paper›PMID 39171125›Full record

ArticleChinese medical journal pulmonary and critical care medicine2023

Changing profile of lung cancer clinical characteristics in China: Over 8-year population-based study.

Kandi Xu, Hao Wang, Simin Li, Lishu Zhao, Xinyue Liu, Yujin Liu, Li Ye, Xiaogang Liu, Linfeng Li, Yayi He

Registry-linked trialAbstract read
In one paragraph

Article in Chinese medical journal pulmonary and critical care medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05423236 (Clinical Characteristics of Lung Cancer in China), which is not on this map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
–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.

NCT05423236 unknown statusnot on this map

Clinical Characteristics of Lung Cancer in China: 8-Year Population-Based Study

TypeobservationalSponsorShanghai Pulmonary Hospital, Shanghai, ChinaRan2022 to 2023Enrolled119,785ConditionsLung Cancer
3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Review
  6. Moderated mediating effects of perceived loneliness and economic burden between social support and mental health for lung cancer patients.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
    Article
  7. Article
  8. Observational
  9. CAR-T therapy: Advances in respiratory diseases.Chinese medical journal pulmonary and critical care medicine · 2025
    Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. 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.

Kandi XuDepartment of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China.
Hao WangSchool of Medicine, Tongji University, Shanghai 200092, China.
Simin LiYidu Cloud Technology Inc., Beijing 100089, China.
Lishu ZhaoDepartment of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China.
Xinyue LiuDepartment of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China.
Yujin LiuDepartment of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China.
Li YeDepartment of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China.
Xiaogang LiuDepartment of Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China.
Linfeng LiYidu Cloud Technology Inc., Beijing 100089, China.
Yayi HeDepartment of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Although examinations and therapies for bronchial lung cancer, also called lung cancer (LC), have become more effective and precise, the morbidity and mortality of LC remain high worldwide. Describing the changing profile of LC characteristics over time is indispensable. This study aimed to understand the changes in real-world settings of LC and its characteristics in China. Methods: In this study, 119,785 patients were enrolled from 2012 to 2020 in the Shanghai Pulmonary Hospital. The patients' medical records were extracted from the hospital's database. Demographic characteristics, general clinicopathological information, and blood coagulation indices at the initial diagnoses were analyzed using the Kruskal-Wallis, Nemenyi, chi-squared, and Bonferroni tests. Changes in demographic characteristics during the 8-year study period, namely dynamic changes among different stages and different pathological types, were evaluated. Results: The percentages of female (from 38.50% [323/839] in 2012 to 48.29% [5112/10,585] in 2020) and non-smoking LC (from 69.34% [475/685] to 80.48% [8055/10,009]) patients increased significantly during the study period, with a trend toward a younger age at diagnosis (from 3.58% [30/839] to 8.99% [952/10,585]). Over the study period, the proportion and absolute number of lung adenocarcinoma cases increased (from 67.97% [433/637] to 76.31% [6606/8657]) while the proportion of lung squamous cell carcinoma decreased (from 21.19% [135/637] to 12.08% [1046/8657]). Comprehensive driver gene mutation examination became more common, and epidermal growth factor receptor ( Conclusions: This study comprehensively depicted the changing characteristics of Chinese LC patients over an 8-year period to provide preliminary insights into LC treatment.Trial registration: ClinicalTrials.gov, NCT05423236.

Indexed as

Clinical characteristicsCross-sectional studyLarge population-based studyLung cancerReal-world study

Identifiers

PMID39171125
PMCPMC11332861

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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.