Colleoni M, Bagnardi V, Rotmensz N, Dellapasqua S, Viale G, Pruneri G, Veronesi P, Torrisi R, Luini A, Intra M, Galimberti V, Montagna E, Goldhirsch A
Department of Medicine, Division of Medical Oncology, European Institute of Oncology, Milan, Italy.
Ann Oncol. 2009 Jul;20(7):1178-84. doi: 10.1093/annonc/mdn747. Epub 2009 Feb 13.
We aimed to predict disease-free survival (DFS) in patients who failed to achieve a pathologic complete remission (pCR) after preoperative chemotherapy (PC).
Data from 577 patients treated with PC and operated at the European Institute of Oncology (EIO) were used to develop a nomogram using Cox proportional hazards regression model based on both categorical (pT, positive nodes, human epidermal growth factor receptor 2 (HER2) status, vascular invasion) and continuous histological variables (estrogen receptors and Ki-67 expression) at surgery. The nomogram was tested on a second patient cohort (343 patients) treated in other institutions and subsequently operated at the EIO.
The nomogram for DFS based on both categorical and continuous variables had good discrimination in the training and the validation sets (concordance indices 0.73, 0.67).
The use of a nomogram based on the degree of selected histopathological variables can predict DFS and might help in the adjuvant therapeutic algorithm design.
我们旨在预测术前化疗（PC）后未达到病理完全缓解（pCR）的患者的无病生存期（DFS）。
来自欧洲肿瘤研究所（EIO）接受PC治疗并接受手术的577例患者的数据，用于基于手术时的分类变量（pT、阳性淋巴结、人表皮生长因子受体2（HER2）状态、血管侵犯）和连续组织学变量（雌激素受体和Ki-67表达），使用Cox比例风险回归模型制定列线图。该列线图在其他机构治疗并随后在EIO接受手术的第二个患者队列（343例患者）中进行了测试。
基于分类变量和连续变量的DFS列线图在训练集和验证集中具有良好的区分度（一致性指数分别为0.73、0.67）。
使用基于所选组织病理学变量程度的列线图可以预测DFS，并可能有助于辅助治疗算法的设计。
