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 re sponse to adjuvant therapy (chemotherapy or tamoxifen therapy) or post lumpectomy radiation therapy in these patients can be vital to their s urvival, because this prediction determines the best postsurgical ther apy for each patient. METHODS, This study evaluated data from 226 pati ents 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 receive r operating characteristic curve (At) was the measure of predictive ac curacy. 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 (At 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 chemot herapy or tamoxifen therapy, the ANNs containing p53 and c-erbB-2 and the number of positive lymph nodes were accurate predictors of surviva l (At 5-year survival, 0.781, 0.789, and 0.720, respectively). CONCLUS IONS, 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 carci noma. ANNs with molecular genetic prognostic factors may improve thera py selection for women with early stage breast carcinoma. (C) 1998 Ame rican Cancer Society.