Evidence map›Paper›PMID 33046018›Full record

ArticleBMC cancer2020

ECCDIA: an interactive web tool for the comprehensive analysis of clinical and survival data of esophageal cancer patients.

Jingcheng Yang, Jun Shang, Qian Song, Zuyi Yang, Jianing Chen, Ying Yu, Leming Shi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Jingcheng YangState Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, 200438, China.
Jun ShangState Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, 200438, China.
Qian SongState Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, 200438, China.
Zuyi YangDepartment of Hematology and Oncology, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, 310015, Zhejiang, China.
Jianing ChenMedical College of Soochow University, Suzhou, 215000, Jiangsu, China.
Ying YuState Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, 200438, China. ying_yu@fudan.edu.cn.
Leming ShiState Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai, 200438, China. lemingshi@fudan.edu.cn.

Funding

111 Project B13016National Key R&D Project of China 2017YFC0907502National Key R&D Project of China 2017YFF0204600National Key R&D Project of China 2018YFE0201600National Natural Science Foundation of China 31720103909Shanghai Municipal Science and Technology Major Project 2017SHZDZX01
6 · The paper itself

Abstract

backgroundEsophageal cancer (EC) is considered as one of the deadliest malignancies with respect to incidence and mortality rate, and numerous risk factors may affect the prognosis of EC patients. For better understanding of the risk factors associated with the onset and prognosis of this malignancy, we develop an interactive web-based tool for the convenient analysis of clinical and survival characteristics of EC patients.

methodsThe clinical data were obtained from The Surveillance, Epidemiology, and End Results (SEER) database. Seven analysis and visualization modules were built with Shiny.

resultsThe Esophageal Cancer Clinical Data Interactive Analysis (ECCDIA, http://webapps.3steps.cn/ECCDIA/ ) was developed to provide basic data analysis, visualization, survival analysis, and nomogram of the overall group and subgroups of 77,273 EC patients recorded in SEER. The basic data analysis modules contained distribution analysis of clinical factor ratios, Sankey plot analysis for relationships between clinical factors, and a map for visualizing the distribution of clinical factors. The survival analysis included Kaplan-Meier (K-M) analysis and Cox analysis for different subgroups of EC patients. The nomogram module enabled clinicians to precisely predict the survival probability of different subgroups of EC patients.

conclusionECCDIA provides clinicians with an interactive prediction and visualization tool for visualizing invaluable clinical and prognostic information of individual EC patients, further providing useful information for better understanding of esophageal cancer.

Indexed as

Esophageal NeoplasmsFemaleHumansInternetMalePrognosisSEER ProgramSurvival AnalysisTelemedicineClinical data miningEsophageal cancerNomogramSEERSurvival analysis

Identifiers

PMID33046018
PMCPMC7552344

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