Hilf R, Feldstein M L, Gibson S L, Savlov E D
Cancer. 1982 Nov 1;50(9):1734-8. doi: 10.1002/1097-0142(19821101)50:9<1734::aid-cncr2820500914>3.0.co;2-v.
A logistic regression model, utilizing the activities of certain selected glycolytic enzymes and ER status measured on primary or recurrent lesions, has been applied to predict for response to combination chemotherapy regimens administered to women with advanced breast cancer. The clinical outcome of response or no response was evaluated retrospectively using criteria employed by cooperative group protocols. In 93 cases, 58/61 patients classified as nonresponders and 22/32 patients demonstrating objective responses would have been correctly designated, based on the 50% estimated probability as the level for separation of responders from nonresponders. The overall predictive accuracy of this model was 86%, with apparently greater accuracy for prediction of lack of response. Addition of estrogen receptor status to the model imparted no gain in accuracy of prediction. Application of this model to a prospective study is warranted.
一种逻辑回归模型，利用在原发性或复发性病变上测量的某些选定糖酵解酶的活性和雌激素受体状态，已被用于预测晚期乳腺癌女性接受联合化疗方案后的反应。使用合作组方案所采用的标准对反应或无反应的临床结果进行回顾性评估。在93例病例中，基于将反应者与无反应者分开的估计概率水平为50%，58/61被分类为无反应者的患者和22/32表现出客观反应的患者将被正确指定。该模型的总体预测准确率为86%，对无反应预测的准确率明显更高。将雌激素受体状态添加到模型中并没有提高预测准确率。有必要将该模型应用于前瞻性研究。
