Deep Neural Network-Based Prediction of the Risk of Advanced Colorectal Neoplasia

被引:3
|
作者
Min, Jun Ki [1 ]
Yang, Hyo-Joon [2 ,3 ]
Kwak, Min Seob [1 ]
Cho, Chang Woo [4 ]
Kim, Sangsoo [4 ]
Ahn, Kwang-Sung [5 ]
Park, Soo-Kyung [2 ,3 ]
Cha, Jae Myung [1 ]
Park, Dong Il [2 ,3 ]
机构
[1] Kyung Hee Univ, Kyung Hee Univ Hosp Gangdong, Dept Internal Med, Sch Med, Seoul, South Korea
[2] Sungkyunkwan Univ, Kangbuk Samsung Hosp, Dept Internal Med, Div Gastroenterol,Sch Med, Seoul, South Korea
[3] Sungkyunkwan Univ, Kangbuk Samsung Hosp, Gastrointestinal Canc Ctr, Sch Med, Seoul, South Korea
[4] Soongsil Univ, Dept Bioinformat, Seoul, South Korea
[5] PDXen Biosyst Inc, Funct Genome Inst, Seoul, South Korea
基金
新加坡国家研究基金会;
关键词
Colorectal neoplasms; Deep learning; Neural networks; Prediction; Mass screening; OPERATING CHARACTERISTIC CURVES; DIABETIC-RETINOPATHY; VALIDATION; MORTALITY; CANCER; STRATIFICATION; CLASSIFICATION; STRATEGIES; LIKELIHOOD; DERIVATION;
D O I
10.5009/gnl19334
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
Background/Aims: Risk prediction models using a deep neural network (DNN) have not been reported to predict the risk of advanced colorectal neoplasia (ACRN). The aim of this study was to compare DNN models with simple clinical score models to predict the risk of ACRN in colorectal cancer screening. Methods: Databases of screening colonoscopy from Kangbuk Samsung Hospital (n=121,794) and Kyung Hee University Hospital at Gangdong (n=3,728) were used to develop DNN-based prediction models. Two DNN models, the Asian-Pacific Colorectal Screening (APCS) model and the Korean Colorectal Screening (KCS) model, were developed and compared with two simple score models using logistic regression methods to predict the risk of ACRN. The areas under the receiver operating characteristic curves (AUCs) of the models were compared in internal and external validation databases. Results: In the internal validation set, the AUCs of DNN model 1 and the APCS score model were 0.713 and 0.662 (p<0.001), respectively, and the AUCs of DNN model 2 and the KCS score model were 0.730 and 0.667 (p<0.001), respectively. However, in the external validation set, the prediction performances were not significantly different between the two DNN models and the corresponding APCS and KCS score models (both p>0.1). Conclusions: Simple score models for the risk prediction of ACRN are as useful as DNN-based models when input variables are limited. However, further studies on this issue are warranted to predict the risk of ACRN in colorectal cancer screening because DNN-based models are currently under improvement.
引用
收藏
页码:85 / 91
页数:7
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