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Editorial Comment on: Development, Validation, and Head-to-Head Comparison of Logistic Regression-Based Nomograms and Artificial Neural Network Models Predicting Prostate Cancer on Initial Extended Biopsy
european urology 54 (2008) 601–611
Editorial Comment on: Development, Validation, and Head-to-Head Comparison of Logistic Regression-Based Nomograms and Artificial Neural Network Models Predicting Prostate Cancer on Initial Extended Biopsy Michael W. Kattan [email protected] Current evidence is quite strong to suggest that modern regression models, as can be represented by nomograms, predict at least as well as artificial neural networks. I was a big fan of neural networks but began to lose interest when real-world data sets produced disappointing results for me [1]. Schwarzer has written some nice reviews of the studies that compared neural networks with regression models and also concluded that neural networks were not living up to their ‘‘hype’’ [2]. In theory, at times neural networks should outpredict regression models [3], but these types of data may not be commonly encountered in practice. As a result, the additional complexity of the neural network is not often being compensated for by improved predictive accuracy. Kawakami et al [4] have produced two new nomograms that performed better than several competitors when applied to validation data from Japan. It is nice to see progress being made with these new models, and hopefully, this predictive advantage will be maintained in further validation
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analyses, particularly when data from other countries are used. Having said that, I think it might be good to move toward nomograms that detect only prostate cancers that are desirable to detect (ie, not indolent). Perhaps detecting only high-grade cancers is a worthy approach [5].
References [1] Kattan MW. Comparison of Cox regression with other methods for determining prediction models and nomograms. J Urol 2003;170(suppl):S6–10. [2] Schwarzer G, Schumacher M. Artificial neural networks for diagnosis and prognosis in prostate cancer. Semin Urol Oncol 2002;20:89–95. [3] Kattan MW. Statistical prediction models, artificial neural networks, and the sophism ‘‘I am a patient, not a statistic’’. J Clin Oncol 2002;20:885–7. [4] Kawakami S, Numao N, Okubo Y, et al. Development, validation, and head-to-head comparison of logistic regression-based nomograms and artificial neural network models predicting prostate cancer on initial extended biopsy. Eur Urol 2008;54:601–11. [5] Nam R, Toi A, Klotz L, et al. Assessing individual risk for prostate cancer. J Clin Oncol 2007;25:3582–8.
DOI: 10.1016/j.eururo.2008.01.018 DOI of original article: 10.1016/j.eururo.2008.01.017
Editorial Comment on: Development, Validation, and Head-to-Head Comparison of Logistic Regression-Based Nomograms and Artificial Neural Network Models Predicting Prostate Cancer on Initial Extended Biopsy Michael W. Kattan [email protected] Current evidence is quite strong to suggest that modern regression models, as can be represented by nomograms, predict at least as well as artificial neural networks. I was a big fan of neural networks but began to lose interest when real-world data sets produced disappointing results for me [1]. Schwarzer has written some nice reviews of the studies that compared neural networks with regression models and also concluded that neural networks were not living up to their ‘‘hype’’ [2]. In theory, at times neural networks should outpredict regression models [3], but these types of data may not be commonly encountered in practice. As a result, the additional complexity of the neural network is not often being compensated for by improved predictive accuracy. Kawakami et al [4] have produced two new nomograms that performed better than several competitors when applied to validation data from Japan. It is nice to see progress being made with these new models, and hopefully, this predictive advantage will be maintained in further validation
611
analyses, particularly when data from other countries are used. Having said that, I think it might be good to move toward nomograms that detect only prostate cancers that are desirable to detect (ie, not indolent). Perhaps detecting only high-grade cancers is a worthy approach [5].
References [1] Kattan MW. Comparison of Cox regression with other methods for determining prediction models and nomograms. J Urol 2003;170(suppl):S6–10. [2] Schwarzer G, Schumacher M. Artificial neural networks for diagnosis and prognosis in prostate cancer. Semin Urol Oncol 2002;20:89–95. [3] Kattan MW. Statistical prediction models, artificial neural networks, and the sophism ‘‘I am a patient, not a statistic’’. J Clin Oncol 2002;20:885–7. [4] Kawakami S, Numao N, Okubo Y, et al. Development, validation, and head-to-head comparison of logistic regression-based nomograms and artificial neural network models predicting prostate cancer on initial extended biopsy. Eur Urol 2008;54:601–11. [5] Nam R, Toi A, Klotz L, et al. Assessing individual risk for prostate cancer. J Clin Oncol 2007;25:3582–8.
DOI: 10.1016/j.eururo.2008.01.018 DOI of original article: 10.1016/j.eururo.2008.01.017