Cheng Skye Hongiun, Horng Cheng-Fang, Clarke Jennifer L, Tsou Mei-Hua, Tsai Stella Y, Chen Chii-Ming, Jian James J, Liu Mei-Chin, West Mike, Huang Andrew T, Prosnitz Leonard R
Department of Radiation Oncology, Koo Foundation Sun Yat-Sen Cancer Center, Taipei, Taiwan.
Int J Radiat Oncol Biol Phys. 2006 Apr 1;64(5):1401-9. doi: 10.1016/j.ijrobp.2005.11.015. Epub 2006 Feb 10.
To develop clinical prediction models for local regional recurrence (LRR) of breast carcinoma after mastectomy that will be superior to the conventional measures of tumor size and nodal status.
Clinical information from 1,010 invasive breast cancer patients who had primary modified radical mastectomy formed the database of the training and testing of clinical prognostic and prediction models of LRR. Cox proportional hazards analysis and Bayesian tree analysis were the core methodologies from which these models were built. To generate a prognostic index model, 15 clinical variables were examined for their impact on LRR. Patients were stratified by lymph node involvement (<4 vs. >or =4) and local regional status (recurrent vs. control) and then, within strata, randomly split into training and test data sets of equal size. To establish prediction tree models, 255 patients were selected by the criteria of having had LRR (53 patients) or no evidence of LRR without postmastectomy radiotherapy (PMRT) (202 patients).
With these models, patients can be divided into low-, intermediate-, and high-risk groups on the basis of axillary nodal status, estrogen receptor status, lymphovascular invasion, and age at diagnosis. In the low-risk group, there is no influence of PMRT on either LRR or survival. For intermediate-risk patients, PMRT improves LR control but not metastases-free or overall survival. For the high-risk patients, however, PMRT improves both LR control and metastasis-free and overall survival.
The prognostic score and predictive index are useful methods to estimate the risk of LRR in breast cancer patients after mastectomy and for estimating the potential benefits of PMRT. These models provide additional information criteria for selection of patients for PMRT, compared with the traditional selection criteria of nodal status and tumor size.
开发乳房切除术后乳腺癌局部区域复发（LRR）的临床预测模型，该模型将优于肿瘤大小和淋巴结状态的传统测量方法。
1010例行原发性改良根治性乳房切除术的浸润性乳腺癌患者的临床信息构成了LRR临床预后和预测模型训练及测试的数据库。Cox比例风险分析和贝叶斯树分析是构建这些模型的核心方法。为生成预后指数模型，研究了15个临床变量对LRR的影响。患者按淋巴结受累情况（<4个与≥4个）和局部区域状态（复发与对照）分层，然后在各层内随机分为大小相等的训练数据集和测试数据集。为建立预测树模型，根据有LRR（53例患者）或无乳房切除术后放疗（PMRT）且无LRR证据（202例患者）的标准选择了255例患者。
利用这些模型，可根据腋窝淋巴结状态、雌激素受体状态、淋巴管浸润和诊断时年龄将患者分为低、中、高风险组。在低风险组中，PMRT对LRR或生存率均无影响。对于中度风险患者，PMRT可改善局部区域控制，但不能改善无转移生存率或总生存率。然而，对于高风险患者，PMRT可改善局部区域控制以及无转移生存率和总生存率。
预后评分和预测指数是估计乳房切除术后乳腺癌患者LRR风险以及估计PMRT潜在益处的有用方法。与淋巴结状态和肿瘤大小的传统选择标准相比，这些模型为选择PMRT患者提供了额外的信息标准。
