Evidence map›Paper›PMID 38994138›Full record

ArticleWorld journal of gastrointestinal oncology2024

Unveiling the secrets of gastrointestinal mucous adenocarcinoma survival after surgery with artificial intelligence: A population-based study.

Jie Song, Xiang-Xiu Yan, Fang-Liang Zhang, Yong-Yi Lei, Zi-Yin Ke, Fang Li, Kai Zhang, Yu-Qi He, Wei Li, Chao Li and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of gastrointestinal oncology, 2024. 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. 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

11 authors.

Jie SongDepartment of Gastroenterology, Dongying People's Hospital, Dongying Hospital of Shandong Provincial Hospital Group, Dongying 257000, Shandong Province, China.
Xiang-Xiu YanDepartment of Gastroenterology, Dongying People's Hospital, Dongying Hospital of Shandong Provincial Hospital Group, Dongying 257000, Shandong Province, China.
Fang-Liang ZhangGastrointestinal Surgery Department, Suining Central Hospital, Suining 629000, Sichuan Province, China.
Yong-Yi LeiObstetrical Department, Suining Central Hospital, Suining 629000, Sichuan Province, China.
Zi-Yin KeSchool of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, Guangdong Province, China.
Fang LiDepartment of Pathology, Aerospace Center Hospital, Peking University Aerospace School of Clinical Medicine, Beijing 100049, China.
Kai ZhangGeneral Department, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China.
Yu-Qi HeDepartment of Gastroenterology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China.
Wei LiDepartment of Thoracic Surgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, Sichuan Province, China.
Chao LiDepartment of Gastroenterology, Aerospace Center Hospital, Peking University Aerospace School of Clinical Medicine, Beijing 100049, China.
Yuan-Ming PanCancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China. peterfpan2020@mail.ccmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundResearch on gastrointestinal mucosal adenocarcinoma (GMA) is limited and controversial, and there is no reference tool for predicting postoperative survival.

aimTo investigate the prognosis of GMA and develop predictive model.

methodsFrom the Surveillance, Epidemiology, and End Results database, we collected clinical information on patients with GMA. After random sampling, the patients were divided into the discovery (70% of the total, for model training), validation (20%, for model evaluation), and completely blind test cohorts (10%, for further model evaluation). The main assessment metric was the area under the receiver operating characteristic curve (AUC). All collected clinical features were used for Cox proportional hazard regression analysis to determine factors influencing GMA's prognosis.

resultsThis model had an AUC of 0.7433 [95% confidence intervals (95%CI): 0.7424-0.7442] in the discovery cohort, 0.7244 (GMA: 0.7234-0.7254) in the validation cohort, and 0.7388 (95%CI: 0.7378-0.7398) in the test cohort. We packaged it into Windows software for doctors' use and uploaded it. Mucinous gastric adenocarcinoma had the worst prognosis, and these were protective factors of GMA: Regional nodes examined [hazard ratio (HR): 0.98, 95%CI: 0.97-0.98,

conclusionThe deep learning-based tool developed can accurately predict the overall survival of patients with GMA postoperatively. Combining surgery, chemotherapy, and adequate lymph node dissection during surgery can improve patient outcomes.

Indexed as

Clinical toolDeep learningGastrointestinal mucous adenocarcinomaOverall survivalSurgery

Identifiers

PMID38994138
PMCPMC11236227

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.