Nonlinear discriminant analysis and prognostic factor classification in node-negative primary breast cancer using probabilistic neural networks.
BACKGROUND
We used non-linear kernel discriminant analysis (KDA) to predict the outcome of 134 axillary node-negative primary breast cancer patients not treated with adjuvant therapy in a non censored database.


METHODS
Posterior probabilities of relapse at 5 years were estimated using probabilistic neural networks (PNN) and a cross-validation (leave-one-out) technique to avoid overfitting the data. A stepwise method was used to construct the models to define the best combination of risk factors among eleven prognostic factors: age, menopausal status, Scarff-Bloom-Richardson grade, clinical tumor size, pathological tumor size, estrogen and progesterone receptor status, urokinase-type plasminogen activator, p53 protein level, c-erbB-2 protein and epidermal growth factor receptor. The different variables were tested individually and in combination to determine their prognostic power using a ROC indicator, which measures the separation between the probability distributions of the output neuron activations under the null hypothesis (no recurrence at 5 years) and under the alternative hypothesis (recurrence at 5 years).


RESULTS
The best predictive one-dimensional model was obtained with uPA (ROC indicator = 0.75). A two-factor model including uPA and clinical tumor size (T) gave the best discrimination between recurrence and non recurrence at 5 years (ROC indicator = 0.84). Additional variables did not improve the accuracy of the prediction. The uPA-T model generated a map useful in predicting the posterior probability of cancer recurrence in a given patient. This representation allows the entire database to be easily visualized and each patient can be compared with the entire database.


CONCLUSIONS
This is a powerful approach to analyze the impact of prognostic factors and it could find clinical applications in breast cancer.
AVENTI, Marseille, France.
J M
JM
Le Goff
L
L
Lavayssière
J
J
Rouëssé
F
F
Spyratos
D000367:Age Factors; D000465:Algorithms; D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000032:analysis; D001943:Breast Neoplasms / Q000737:chemistry / Q000401:mortality* / Q000473:pathology; D016208:Databases, Factual; D016002:Discriminant Analysis; D066246:ErbB Receptors / Q000032:analysis; D004967:Estrogens; D005260:Female; D006801:Humans; D008593:Menopause; D009363:Neoplasm Proteins / Q000032:analysis; D009364:Neoplasm Recurrence, Local / Q000453:epidemiology*; D009376:Neoplasms, Hormone-Dependent / Q000737:chemistry / Q000401:mortality / Q000473:pathology; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011374:Progesterone; D011379:Prognosis; D012372:ROC Curve; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000032:analysis; D011960:Receptors, Estrogen / Q000032:analysis; D011980:Receptors, Progesterone / Q000032:analysis; D012307:Risk Factors; D016896:Treatment Outcome; D016159:Tumor Suppressor Protein p53 / Q000032:analysis; D014568:Urokinase-Type Plasminogen Activator / Q000032:analysis
D014408:Biomarkers, Tumor; D066246:ErbB Receptors; D004967:Estrogens; D009363:Neoplasm Proteins; D011374:Progesterone; D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; D016159:Tumor Suppressor Protein p53; D014568:Urokinase-Type Plasminogen Activator