Evidence map›Paper›PMID 38858653›Full record

ArticleBMC cancer2024

Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China.

Shaobo Zhang, Hongxia Xu, Wei Li, Jiuwei Cui, Qingchuan Zhao, Zengqing Guo, Junqiang Chen, Qinghua Yao, Suyi Li, Ying He and 4 more

Abstract readMulticenter StudyValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Shaobo ZhangDepartment of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, China.
Hongxia XuDepartment of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China.
Wei LiCancer Center of the First Hospital of Jilin University, Changchun, Jilin, 130021, China.
Jiuwei CuiCancer Center of the First Hospital of Jilin University, Changchun, Jilin, 130021, China.
Qingchuan ZhaoDepartment of Digestive Diseases, Xijing Hospital, Fourth Military Medical University, Xi'an, Shanxi, 710032, China.
Zengqing GuoDepartment of Medical Oncology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian, 350014, China.
Junqiang ChenDepartment of Gastrointestinal Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, China.
Qinghua YaoDepartment of Integrated Traditional Chinese and Western Medicine, Zhejiang Cancer Hospital and Key Laboratory of Traditional Chinese Medicine Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China.
Suyi LiDepartment of Nutrition and Metabolism of Oncology, Affiliated Provincial Hospital of Anhui Medical University, Hefei, Anhui, 230031, China.
Ying HeDepartment of Clinical Nutrition, Chongqing General Hospital, Chongqing, 400014, China.
Qiuge QiaoDepartment of General Surgery, Second Hospital (East Hospital), Hebei Medical University, Shijiazhuang, Hebei, 050000, China.
Yongdong FengDepartment of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Hanping ShiDepartment of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100054, China. shihp@ccmu.edu.cn.
Chunhua SongDepartment of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, China. sch16@zzu.edu.cn.

Funding

National Key Research and Development Program of China 2022YFC2009600
6 · The paper itself

Abstract

backgroundInflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators.

methodsPatients' baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients.

resultsA 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis.

conclusionThe proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients.

Indexed as

Biomarkers, TumorNomogramsStomach NeoplasmsAdultAgedChinaCohort StudiesFemaleHumansInflammationKaplan-Meier EstimateMachine LearningMaleMiddle AgedNeoplasm StagingPrognosisBiomarkers, TumorGastric cancerInflammatory biomarkersMachine learningOverall survivalPrognosis

Identifiers

PMID38858653
PMCPMC11163779

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