Abstract
BACKGROUND. Accurate estimation of outcome in patients with malignant disease is an important component of the clinical decision-making process. To create a comprehensive prognostic model for esophageal carcinoma, artificial neural networks (ANNs) were applied to the analysis of a range of patient-related and tumor-related variables. METHODS. Clinical and pathologic data were collected from 418 patients with esophageal carcinoma who underwent resection with curative intent. A data base that included 199 variables was constructed. Using ANN-based sensitivity analysis, the optimal combination of variables was determined to allow creation of a survival prediction model. The accuracy (area under the receiver operator characteristic curve [AUR]) of this ANN model subsequently was compared with the accuracy of the conventional statistical technique: linear discriminant analysis (LDA). RESULTS. The optimal ANN models for predicting outcomes at 1 year and 5 years consisted of 65 variables (AUR = 0.883) and 60 variables (AUR = 0.884), respectively. These filtered, optimal data sets were significantly more accurate (P <0.0001) than the original data set of 199 variables. The majority of ANN models demonstrated improved accuracy compared with corresponding LDA models for 1-year and 5-year survival predictions. Furthermore, ANN models based on the optimal data set were superior predictors of survival compared with a model based solely on TNM staging criteria (P <0.0001). CONCLUSIONS. ANNs can be used to construct a highly accurate prognostic model for patients with esophageal carcinoma. Sensitivity analysis based on ANNs is a powerful tool for seeking optimal data sets.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1596-1605 |
| Number of pages | 10 |
| Journal | Cancer |
| Volume | 103 |
| Issue number | 8 |
| DOIs | |
| State | Published - Apr 15 2005 |
| Externally published | Yes |
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Keywords
- Artificial neural network
- Esophageal carcinoma
- Prognosis
- Sensitivity analysis
- Survival
ASJC Scopus subject areas
- Cancer Research
- Oncology
Cite this
Prediction of survival in patients with esophageal carcinoma using artificial neural networks. / Sato, Fumiaki; Shimada, Yutaka; Selaru, Florin; Shibata, David; Maeda, Masato; Watanabe, Go; Mori, Yuriko; Stass, Sanford A.; Imamura, Masayuki; Meltzer, Stephen.
In: Cancer, Vol. 103, No. 8, 15.04.2005, p. 1596-1605.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Prediction of survival in patients with esophageal carcinoma using artificial neural networks
AU - Sato, Fumiaki
AU - Shimada, Yutaka
AU - Selaru, Florin
AU - Shibata, David
AU - Maeda, Masato
AU - Watanabe, Go
AU - Mori, Yuriko
AU - Stass, Sanford A.
AU - Imamura, Masayuki
AU - Meltzer, Stephen
PY - 2005/4/15
Y1 - 2005/4/15
N2 - BACKGROUND. Accurate estimation of outcome in patients with malignant disease is an important component of the clinical decision-making process. To create a comprehensive prognostic model for esophageal carcinoma, artificial neural networks (ANNs) were applied to the analysis of a range of patient-related and tumor-related variables. METHODS. Clinical and pathologic data were collected from 418 patients with esophageal carcinoma who underwent resection with curative intent. A data base that included 199 variables was constructed. Using ANN-based sensitivity analysis, the optimal combination of variables was determined to allow creation of a survival prediction model. The accuracy (area under the receiver operator characteristic curve [AUR]) of this ANN model subsequently was compared with the accuracy of the conventional statistical technique: linear discriminant analysis (LDA). RESULTS. The optimal ANN models for predicting outcomes at 1 year and 5 years consisted of 65 variables (AUR = 0.883) and 60 variables (AUR = 0.884), respectively. These filtered, optimal data sets were significantly more accurate (P <0.0001) than the original data set of 199 variables. The majority of ANN models demonstrated improved accuracy compared with corresponding LDA models for 1-year and 5-year survival predictions. Furthermore, ANN models based on the optimal data set were superior predictors of survival compared with a model based solely on TNM staging criteria (P <0.0001). CONCLUSIONS. ANNs can be used to construct a highly accurate prognostic model for patients with esophageal carcinoma. Sensitivity analysis based on ANNs is a powerful tool for seeking optimal data sets.
AB - BACKGROUND. Accurate estimation of outcome in patients with malignant disease is an important component of the clinical decision-making process. To create a comprehensive prognostic model for esophageal carcinoma, artificial neural networks (ANNs) were applied to the analysis of a range of patient-related and tumor-related variables. METHODS. Clinical and pathologic data were collected from 418 patients with esophageal carcinoma who underwent resection with curative intent. A data base that included 199 variables was constructed. Using ANN-based sensitivity analysis, the optimal combination of variables was determined to allow creation of a survival prediction model. The accuracy (area under the receiver operator characteristic curve [AUR]) of this ANN model subsequently was compared with the accuracy of the conventional statistical technique: linear discriminant analysis (LDA). RESULTS. The optimal ANN models for predicting outcomes at 1 year and 5 years consisted of 65 variables (AUR = 0.883) and 60 variables (AUR = 0.884), respectively. These filtered, optimal data sets were significantly more accurate (P <0.0001) than the original data set of 199 variables. The majority of ANN models demonstrated improved accuracy compared with corresponding LDA models for 1-year and 5-year survival predictions. Furthermore, ANN models based on the optimal data set were superior predictors of survival compared with a model based solely on TNM staging criteria (P <0.0001). CONCLUSIONS. ANNs can be used to construct a highly accurate prognostic model for patients with esophageal carcinoma. Sensitivity analysis based on ANNs is a powerful tool for seeking optimal data sets.
KW - Artificial neural network
KW - Esophageal carcinoma
KW - Prognosis
KW - Sensitivity analysis
KW - Survival
UR - http://www.scopus.com/inward/record.url?scp=20144387857&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=20144387857&partnerID=8YFLogxK
U2 - 10.1002/cncr.20938
DO - 10.1002/cncr.20938
M3 - Article
C2 - 15751017
AN - SCOPUS:20144387857
VL - 103
SP - 1596
EP - 1605
JO - Cancer
JF - Cancer
SN - 0008-543X
IS - 8
ER -