基于神经网络的胃癌预后预测工具:全球回顾性研究。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2023-07-18 DOI:10.1186/s13040-023-00335-z
Wei Li, Minghang Zhang, Siyu Cai, Liangliang Wu, Chao Li, Yuqi He, Guibin Yang, Jinghui Wang, Yuanming Pan
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引用次数: 0

摘要

背景:胃贲门癌(GCC)近年来发病率明显上升,预后较差。有必要将GCC与其他胃部位癌的预后进行比较,建立有效的基于神经网络的预后模型来预测GCC患者的生存。方法:在基于人群的队列研究中,我们首先纳入了来自监测、流行病学和最终结果(SEER)数据(n = 31,397)以及来自不同医院的中国公开数据(n = 1049)的临床特征。然后根据诊断时间将SEER数据分为两组,训练组(2010-2014年诊断为GCC的患者,n = 4414)和测试组(2015年诊断为GCC的患者,n = 957)。选择年龄、性别、病理、肿瘤、淋巴结和转移(TNM)分期、肿瘤大小、是否手术、是否放疗、是否化疗和恶性肿瘤史作为预测临床特征。利用列车队列进行基于神经网络的预后预测模型,并通过自身和测试队列的验证。采用受试者工作特性曲线下面积(AUC)评价模型性能。结果:SEER数据库中GCC患者的预后差于非GCC (NGCC)患者,而在中国数据中并不差。模型共纳入5371例患者,遵循纳入和排除标准。基于神经网络的预后预测模型对GCC总生存期(OS)的预测效果令人满意,在训练队列中AUC为0.7431(95%置信区间CI为0.7423-0.7439),在测试队列中AUC为0.7419 (95% CI为0.7411-0.7428)。结论:与非GCC患者相比,GCC患者的生存时间确实存在差异。本研究开发的基于神经网络的预后预测工具是一种新颖而有前途的用于GCC患者临床结果分析的软件。
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Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study.

Backgrounds: The incidence of gastric cardiac cancer (GCC) has obviously increased recently with poor prognosis. It's necessary to compare GCC prognosis with other gastric sites carcinoma and set up an effective prognostic model based on a neural network to predict the survival of GCC patients.

Methods: In the population-based cohort study, we first enrolled the clinical features from the Surveillance, Epidemiology and End Results (SEER) data (n = 31,397) as well as the public Chinese data from different hospitals (n = 1049). Then according to the diagnostic time, the SEER data were then divided into two cohorts, the train cohort (patients were diagnosed as GCC in 2010-2014, n = 4414) and the test cohort (diagnosed in 2015, n = 957). Age, sex, pathology, tumor, node, and metastasis (TNM) stage, tumor size, surgery or not, radiotherapy or not, chemotherapy or not and history of malignancy were chosen as the predictive clinical features. The train cohort was utilized to conduct the neural network-based prognostic predictive model which validated by itself and the test cohort. Area under the receiver operating characteristics curve (AUC) was used to evaluate model performance.

Results: The prognosis of GCC patients in SEER database was worse than that of non GCC (NGCC) patients, while it was not worse in the Chinese data. The total of 5371 patients were used to conduct the model, following inclusion and exclusion criteria. Neural network-based prognostic predictive model had a satisfactory performance for GCC overall survival (OS) prediction, which owned 0.7431 AUC in the train cohort (95% confidence intervals, CI, 0.7423-0.7439) and 0.7419 in the test cohort (95% CI, 0.7411-0.7428).

Conclusions: GCC patients indeed have different survival time compared with non GCC patients. And the neural network-based prognostic predictive tool developed in this study is a novel and promising software for the clinical outcome analysis of GCC patients.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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