An evaluation of intelligent prognostic systems for colorectal cancer

Anand, Sarabjot Singh; Smith, Ann E.; Hamilton, Peter; Anand, Jacek Sein; Hughes, John G.; Bartels, Peter H.

doi:10.1016/s0933-3657(98)00052-9

Cited by 34 publications

(33 citation statements)

References 11 publications

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“…Our aim to predict an overall probability of non-recurrence for an individual patient is in line with the suggestion of Anand et al [2] to construct prognostic models with a directly useful prognostic estimate for a single patient. The idea of weighting the patients is similar to the one proposed by Ripley and Ripley [22].…”

Section: Related Workmentioning

confidence: 59%

“…Anand et al [2] stress the need to develop the prognostic models that would, instead of hazard or survival functions, explicitly provide a prognostic estimate for an individual patient. They compare regression trees, k-nearest neighbors (k-NN) and a regression variant of an artificial neural network to model the patient's survival time after being diagnosed with colorectal cancer.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning for survival analysis: a case study on recurrence of prostate cancer

Zupan

Demšar

Kattan

et al. 2000

Artificial Intelligence in Medicine

112

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Machine learning techniques have recently received considerable attention, especially when used for the construction of prediction models from data. Despite their potential advantages over standard statistical methods, like their ability to model non-linear relationships and construct symbolic and interpretable models, their applications to survival analysis are at best rare, primarily because of the difficulty to appropriately handle censored data. In this paper we propose a schema that enables the use of classification methods -including machine learning classifiers -for survival analysis. To appropriately consider the follow-up time and censoring, we propose a technique that, for the patients for which the event did not occur and have short follow-up times, estimates their probability of event and assigns them a distribution of outcome accordingly. Since most machine learning techniques do not deal with outcome distributions, the schema is implemented using weighted examples. To show the utility of the proposed technique, we investigate a particular problem of building prognostic models for prostate cancer recurrence, where the sole prediction of the probability of event (and not its probability dependency on time) is of interest. A case study on preoperative and postoperative prostate cancer recurrence prediction shows that by incorporating this weighting technique the machine learning tools stand beside modern statistical methods and may, by inducing symbolic recurrence models, provide further insight to relationships within the modeled data.

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Section: Related Workmentioning

confidence: 59%

Section: Related Workmentioning

confidence: 99%

Machine learning for survival analysis: a case study on recurrence of prostate cancer

Zupan

Demšar

Kattan

et al. 2000

Artificial Intelligence in Medicine

112

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show abstract

“…These included logistic regression, decision trees, and ANNs. These models were chosen because of their value, as described in recent reports (1)(2)(3)(4). The models were built by use of SAS ® Enterprise Miner software.…”

Section: Predictive Modelsmentioning

confidence: 99%

“…In particular, this is being done to predict the clinical outcome for patients with colorectal cancer (CRC) (1)(2)(3)(4).…”

Section: Introductionmentioning

confidence: 99%

Comparison of the predictive qualities of three prognostic models of colorectal cancer

Anderson¹

2010

Front Biosci

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Most discoveries of cancer biomarkers involve construction of a single model to determine predictions of survival. 'Data-mining' techniques, such as artificial neural networks (ANNs), perform better than traditional methods, such as logistic regression. In this study, the quality of multiple predictive models built on a molecular data set for colorectal cancer (CRC) was evaluated. Predictive models (logistic regressions, ANNs, and decision trees) were compared, and the effect of techniques for variable selection on the predictive quality of these models was investigated. The Kolmogorov-Smirnoff (KS) statistic was used to compare the models. Overall, the logistic regression and ANN methods outperformed use of a decision tree. In some instances (e.g., for a model that included 'all variables without tumor stage' and use of a decision tree for variable selection), the ANN marginally outperformed logistic regression, although the difference between the accuracy of the KS statistic was minimal (0.80 versus 0.82). Regardless of the variable(s) and the methods for variable selection, all three predictive models identified survivors and non-survivors with the same level of statistical accuracy. KeywordsArtificial neural networks; Colorectal cancer; Decision trees; Kolmogorov-Smirnoff statistic; Logistic regression; Predictive models INTRODUCTIONResearchers are now examining the methodology for predicting the survival or disease recurrence in cancer patients by use of data-mining techniques, such as artificial neural networks (ANNs), decision trees, and k-nearest neighbor (k-NN). In particular, this is being done to predict the clinical outcome for patients with colorectal cancer (CRC) (1-4).Send correspondence to: Upender Manne, Department of Pathology, University of Alabama at Birmingham, 515B1-Kracke Building 619, 19 th Street South, Birmingham, AL, manne@uab.edu. of the TNM components of tumor staging variables by themselves in an ANN significantly increased the predictive accuracy of the variables when compared to a model for survival analysis with the same variables. The predictive accuracy of the variables used in the models was measured by the area-under-the-ROC curve. The ANN increased the predictive accuracy of the model by 44-74%. Also, ANNs were used by this group to build predictive models for breast cancer survival; they found that, compared to the TNM staging, the ANN provided better predictive accuracy (5, 6). Therefore, it is not clear whether the improved predictive capacity was a reflection of the ANN method, or whether the variables such as positive lymph nodes and p53 status contributed to improving the predictability of a model. Furthermore, the predictive accuracy of a model is a factor of the variables in the model, and the technique for variable selection determines the quality of a model (7). NIH Public Access Author ManuscriptFront Biosci (Elite EdANNs and other data mining tools are called 'black-box' techniques, since the logic used to determine the final model is not transparent....

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“…The analyzing of CRC data and prediction of CRC was made on statistical methods and recently artificial neural network (ANN) (Bottaci et al, 1997;Anand et al, 1999;Grumett et al, 2003;Lee et al, 2004;Kyung-Joong and Sung-Bae, 2004;Bittern et al, 2005;Ahmed, 2005;Alladi et al, 2008;Fathy, 2011;Gohari et al, 2011). ANN models are flexible and nonlinear methods that allow better fit to the data and leads to accurate prediction (Bishop, 1997).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Prediction of Distant Metastasis in Colorectal Cancer

Biglarian

Bakhshi²,

Gohari³

et al. 2012

Asian Pacific Journal of Cancer Prevention

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An evaluation of intelligent prognostic systems for colorectal cancer

Cited by 34 publications

References 11 publications

Machine learning for survival analysis: a case study on recurrence of prostate cancer

Machine learning for survival analysis: a case study on recurrence of prostate cancer

Comparison of the predictive qualities of three prognostic models of colorectal cancer

Artificial Neural Network for Prediction of Distant Metastasis in Colorectal Cancer

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