Artificial neural networks applied to survival prediction in breast cancer.
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.
Lundin
M
M
Department of Oncology, University of Helsinki, Finland.
Lundin
J
J
Burke
H B
HB
Toikkanen
S
S
Pylkkänen
L
L
Joensuu
H
H
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000401:mortality* / Q000473:pathology; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D016019:Survival Analysis; D015996:Survival Rate