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Jiang FN, Dai LJ, Wu YD, Yang SB, Liang YX, Zhang X, Zou CY, He RQ, Xu XM, Zhong WD. The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks. J Chin Med Assoc 2020; 83:471-477. [PMID: 32217993 DOI: 10.1097/jcma.0000000000000299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Prostate cancer (PCa) is the most common malignancy seen in men and the second leading cause of cancer-related death in males. The incidence and mortality associated with PCa has been rapidly increasing in China recently. METHODS Multiple diagnostic models of human PCa were developed based on Taylor database by combining the artificial neural networks (ANNs) to enhance the ability of PCa diagnosis. Genetic algorithm (GA) is used to select feature genes as numerical encoded parameters that reflect cancer, metastatic, or normal samples. Back propagation (BP) neural network and learning vector quantization (LVQ) neural network were used to build different Cancer/Normal, Primary/Metastatic, and Gleason Grade diagnostic models. RESULTS The performance of these modeling approaches was evaluated by predictive accuracy (ACC) and area under the receiver operating characteristic curve (AUC). By observing the statistically significant parameters of the three training sets, our Cancer/Normal, Primary/Metastatic, and Gleason Grade models' with ACC and AUC can be drawn (97.33%, 0.9832), (99.17%, 0.9952), and (90.48%, 0.8742), respectively. CONCLUSION These results indicated that our diagnostic models of human PCa based on Taylor database combining the feature gene expression profiling data and artificial intelligence algorithms might act as a powerful tool for diagnosing PCa. Gleason Grade diagnostic models were used as novel prognostic diagnosis models for biochemical recurrence-free survival and overall survival, which might be helpful in the prognostic diagnosis of PCa in patients.
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Affiliation(s)
- Fu-Neng Jiang
- Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Li-Jun Dai
- Laboratory Animal Center, Guangzhou Medical University, Guangzhou, China
| | - Yong-Ding Wu
- Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
- Department of Urology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Sheng-Bang Yang
- Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Yu-Xiang Liang
- Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xin Zhang
- Guangzhou HYY Precision&Translation Medicine Institute, Guangzhou, China
| | - Cui-Yun Zou
- Guangzhou HYY Precision&Translation Medicine Institute, Guangzhou, China
| | - Ren-Qiang He
- Department of Urology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Xiao-Ming Xu
- Department of Urology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Wei-De Zhong
- Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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Wang Q, Wei J, Chen Z, Zhang T, Zhong J, Zhong B, Yang P, Li W, Cao J. Establishment of multiple diagnosis models for colorectal cancer with artificial neural networks. Oncol Lett 2019; 17:3314-3322. [PMID: 30867765 PMCID: PMC6396131 DOI: 10.3892/ol.2019.10010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2018] [Accepted: 09/13/2018] [Indexed: 12/13/2022] Open
Abstract
The current study aimed to develop multiple diagnosis models for colorectal cancer (CRC) based on data from The Cancer Genome Atlas database and analysis with artificial neural networks in order to enhance CRC diagnosis methods. A genetic algorithm and mean impact value were used to select genes to be used as numerical encoded parameters to reflect cancer metastasis or aggression. Back propagation and learning vector quantization neural networks were used to build four diagnosis models: Cancer/Normal, M0/M1, carcinoembryonic antigen (CEA) <5/≥5 and Clinical stage I-II/III-IV. The performance of each model was evaluated by predictive accuracy (ACC), the area under the receiver operating characteristic curve (AUC) and a 10-fold cross-validation test. The ACC and AUC of the Cancer/Normal, M0/M1, CEA and Clinical stage models were 100%, 1.000; 87.14%, 0.670; 100%, 1.000; and 100%, 1.000, respectively. The 10-fold cross-validation test of the ACC values and sensitivity for each test were 93.75-99.39%, 1.0000; 80.58-88.24%, 0.9286-1.0000; 67.21-92.31%, 0.7091-1.0000; and 59.13-68.85%, 0.6017-0.6585, respectively. The diagnosis models developed in the current study combined gene expression profiling data and artificial intelligence algorithms to create tools for improved diagnosis of CRC.
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Affiliation(s)
- Qiang Wang
- Department of General Surgery, Guangzhou Digestive Disease Centre, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, Guangzhou, Guangdong 510000, P.R. China
| | - Jianchang Wei
- Department of General Surgery, Guangzhou Digestive Disease Centre, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, Guangzhou, Guangdong 510000, P.R. China
| | - Zhuanpeng Chen
- Department of General Surgery, Guangzhou Digestive Disease Centre, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, Guangzhou, Guangdong 510000, P.R. China
| | - Tong Zhang
- Department of General Surgery, Guangzhou Digestive Disease Centre, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, Guangzhou, Guangdong 510000, P.R. China
| | - Junbin Zhong
- Department of General Surgery, Guangzhou Digestive Disease Centre, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, Guangzhou, Guangdong 510000, P.R. China
| | - Bingzheng Zhong
- Department of General Surgery, Guangzhou Digestive Disease Centre, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, Guangzhou, Guangdong 510000, P.R. China
| | - Ping Yang
- Department of General Surgery, Guangzhou Digestive Disease Centre, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, Guangzhou, Guangdong 510000, P.R. China
| | - Wanglin Li
- Department of General Surgery, Guangzhou Digestive Disease Centre, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, Guangzhou, Guangdong 510000, P.R. China
| | - Jie Cao
- Department of General Surgery, Guangzhou Digestive Disease Centre, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, Guangzhou, Guangdong 510000, P.R. China
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SONG Y, TAKATSUKI K, SEKIGUCHI T, FUNATSU T, SHOJI S, TSUNODA M. Retention and Bandwidth Predictions by Fast Gradient Elution Chromatography Using a Pillar Array Column. CHROMATOGRAPHY 2016. [DOI: 10.15583/jpchrom.2016.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Affiliation(s)
- Yanting SONG
- Graduate School of Pharmaceutical Sciences, University of Tokyo
- Key Laboratory of Tropic Biological Resources, Minister of Education; College of Marine Science, Hainan University
| | | | | | - Takashi FUNATSU
- Graduate School of Pharmaceutical Sciences, University of Tokyo
| | - Shuichi SHOJI
- Major in Nano-Science and Nano-Engineering, Waseda University
| | - Makoto TSUNODA
- Graduate School of Pharmaceutical Sciences, University of Tokyo
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