Interactive online consensus survival tool for esophageal squamous cell carcinoma prognosis analysis

Wang, Qiang; Wang, Fengling; Lv, Jiajia; Xin, Junfang; Xie, Longxiang; Zhu, Wan; Tang, Yaxun; Li, Yongqiang; Zhao, Xianzhi; Wang, Yunlong; Li, Xianzhe; Guo, Xiangqian

doi:10.3892/ol.2019.10440

Cited by 26 publications

(28 citation statements)

References 42 publications

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“…Therefore, building benchmark databases for model validation is urgently needed. One solution is to start building cancer patients' databases for prognosis analysis [101][102][103][104][105][106][107][108][109][110].…”

Section: Challenges In the Application Of Deep Learning In Cancer Promentioning

confidence: 99%

The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

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264

161

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Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.

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Section: Challenges In the Application Of Deep Learning In Cancer Promentioning

confidence: 99%

The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

Self Cite

264

161

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“…LOGpc is a web server that contains a large number of datasets for survival analysis, which provides 13 types of survival terms for 28,098 cancer patients from 26 types of malignant tumors, including OSlms, OSblca, OSkirc and other 23 online prognostic tools (14)(15)(16)(17)(18)(19)(20)(21). These patient samples were collected mainly from TCGA and GEO cohorts.…”

Section: Logpcmentioning

confidence: 99%

Comprehensive Review of Web Servers and Bioinformatics Tools for Cancer Prognosis Analysis

Zheng

Zhang

et al. 2020

Front. Oncol.

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Prognostic biomarkers are of great significance to predict the outcome of patients with cancer, to guide the clinical treatments, to elucidate tumorigenesis mechanisms, and offer the opportunity of identifying therapeutic targets. To screen and develop prognostic biomarkers, high throughput profiling methods including gene microarray and next-generation sequencing have been widely applied and shown great success. However, due to the lack of independent validation, only very few prognostic biomarkers have been applied for clinical practice. In order to cross-validate the reliability of potential prognostic biomarkers, some groups have collected the omics datasets (i.e., epigenetics/transcriptome/proteome) with relative follow-up data (such as OS/DSS/PFS) of clinical samples from different cohorts, and developed the easy-to-use online bioinformatics tools and web servers to assist the biomarker screening and validation. These tools and web servers provide great convenience for the development of prognostic biomarkers, for the study of molecular mechanisms of tumorigenesis and progression, and even for the discovery of important therapeutic targets. Aim to help researchers to get a quick learning and understand the function of these tools, the current review delves into the introduction of the usage, characteristics and algorithms of tools, and web servers, such as LOGpc, KM plotter, GEPIA, TCPA, OncoLnc, PrognoScan, MethSurv, SurvExpress, UALCAN, etc., and further help researchers to select more suitable tools for their own research. In addition, all the tools introduced in this review can be reached at http://bioinfo.henu.edu.cn/WebServiceList.html.

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“…The OSeac server is developed as we previously described (17)(18)(19), and hosted in a windows server and adopts Appache Tomcat as web application server. Use HTML and JSP for the front end page and server side code is compiled to Java.…”

Section: Development Of Oseacmentioning

confidence: 99%

“…However, these biomarkers need independent further validation to increase their sensitivity and specificity before clinical application. The advanced bioinformatic methods and resources have been developed for breast cancer, bladder cancer, esophageal squamous cell carcinoma, leiomyosarcoma, and lung cancer to analyze the prognostic abilities of genes (14)(15)(16)(17)(18)(19), and greatly facilitate the development of cancer prognostic biomarkers. However, there is a lack of prognostic analysis system for EAC.…”

Section: Introductionmentioning

confidence: 99%