Predicting response to adjuvant and radiation therapy in patients with early stage breast carcinoma.

BACKGROUND

Screening and surveillance is increasing the detection of early stage breast carcinoma. The ability to predict accurately the response to adjuvant therapy (chemotherapy or tamoxifen therapy) or postlumpectomy radiation therapy in these patients can be vital to their survival, because this prediction determines the best postsurgical therapy for each patient.

METHODS

This study evaluated data from 226 patients with TNM Stage I and early Stage II breast carcinoma and included the variables p53 and c-erbB-2 (HER-2/neu). The area under the receiver operating characteristic curve (Az) was the measure of predictive accuracy. The prediction endpoints were 5- and 10-year overall survival.

RESULTS

For Stage I and early Stage II patients, the 5- and 10-year predictive accuracy of the TNM staging system were at chance level, i.e., no better than flipping a coin. Both the 5- and 10-year artificial neural networks (ANNs) were very accurate--significantly more so than the TNM staging system (Az 5-year survival, TNM = 0.567, ANN = 0.758; P < 0.001; Az 10-year survival, TNM = 0.508, ANN = 0.894; P < 0.0001). For patients not receiving postsurgical therapy and for either chemotherapy or tamoxifen therapy, the ANNs containing p53 and c-erbB-2 and the number of positive lymph nodes were accurate predictors of survival (Az 5-year survival, 0.781, 0.789, and 0.720, respectively).

CONCLUSIONS

The molecular genetic variables p53 and c-erbB-2 and the number of positive lymph nodes are powerful predictors of survival, and using ANN statistical models is a powerful method for predicting responses to adjuvant therapy or radiation therapy in patients with breast carcinoma. ANNs with molecular genetic prognostic factors may improve therapy selection for women with early stage breast carcinoma.