Artificial Neural Networks Applied to Survival Prediction in Breast Cancer

Abstract

In this study, we evaluated the accuracy of a neural network in predicting 5-, 10- and 15-year breast-cancer-specific survival. A series of 951 breast cancer patients was divided into a training set of 651 and a validation set of 300 patients. Eight variables were entered as input to the network: tumor size, axillary nodal status, histological type, mitotic count, nuclear pleomorphism, tubule formation, tumor necrosis and age. The area under the ROC curve (AUC) was used as a measure of accuracy of the prediction models in generating survival estimates for the patients in the independent validation set. The AUC values of the neural network models for 5-, 10- and 15-year breast-cancer-specific survival were 0.909, 0.886 and 0.883, respectively.The corresponding AUC values for logistic regression were 0.897, 0.862 and 0.858. Axillary lymph node status (N0 vs. N+) predicted 5-year survival with a specificity of 71% and a sensitivity of 77%. The sensitivity of the neural network model was 91% at this specificity level. The rate of false predictions at 5 years was 82/300 for nodal status and 40/300 for the neural network. When nodal status was excluded from the neural network model, the rate of false predictions increased only to 49/300 (AUC 0.877). An artificial neural network is very accurate in the 5-, 10- and 15-year breast-cancer-specific survival prediction. The consistently high accuracy over time and the good predictive performance of a network trained without information on nodal status demonstrate that neural networks can be important tools for cancer survival prediction.

Estimating potential evapotranspiration from limited weather data over Gangetic West Bengal, India: a neurocomputing approach

“…Otherwise, the weights are adjusted to reduce this difference. In the present paper, the ANN model was constructed with a commercially available computer program (NeuralWare) using a modified cascade method together with an adaptive gradient learning rule (Lundin et al, 1999). The cascade mode of construction involves adding hidden nodes, one or more than one at a time, and always connecting all the previous nodes to the current node.…”

“…It should be mentioned that there is no strict rule to decide the ratio of training and test cases. A survey of the ANN literature found that the ratios 1 : 1 (Chattopadhyay and Chattopadhyay, 2008a), 7 : 3 (Lundin et al, 1999) and 3 : 1 (Perez et al, 2000) are frequently used in ANN applications. In the present paper an approach similar to that of Lundin et al (1999) has been adopted after examining the other approaches.…”

“…A survey of the ANN literature found that the ratios 1 : 1 (Chattopadhyay and Chattopadhyay, 2008a), 7 : 3 (Lundin et al, 1999) and 3 : 1 (Perez et al, 2000) are frequently used in ANN applications. In the present paper an approach similar to that of Lundin et al (1999) has been adopted after examining the other approaches. Each row of the training matrix contains four columns, which correspond to the temperature of the ambient air, vapour pressure of the ambient air, and rainfall as the predictors, and the PET as the predictand.…”

Accuracy Evaluation of C4.5 and Naive Bayes Classifiers Using Attribute Ranking Method

“…Lundin et al used artificial neural network and logistic regression models to predict 5 , 10 , 15 year breast cancer survival 8 . Pendharkar et al used several data mining techniques for exploring patterns in breast cancer 9 .…”

Cancer classification based on gene expression using neural networks

“…In breast cancer, area under the ROC curve (AUC) can be used as a measurement of accuracy when evaluating survival rate estimates for 5, 10, and 15 years in patients (Lundin et al, 1999).…”