Martin Michael A, Meyricke Ramona, O'Neill Terry, Roberts Steven
School of Finance and Applied Statistics, Australian National University, Canberra ACT, 0200, Australia.
BMC Cancer. 2006 Apr 20;6:98. doi: 10.1186/1471-2407-6-98.
A critical choice facing breast cancer patients is which surgical treatment--mastectomy or breast conserving surgery (BCS)--is most appropriate. Several studies have investigated factors that impact the type of surgery chosen, identifying features such as place of residence, age at diagnosis, tumor size, socio-economic and racial/ethnic elements as relevant. Such assessment of "propensity" is important in understanding issues such as a reported under-utilisation of BCS among women for whom such treatment was not contraindicated. Using Western Australian (WA) data, we further examine the factors associated with the type of surgical treatment for breast cancer using a classification tree approach. This approach deals naturally with complicated interactions between factors, and so allows flexible and interpretable models for treatment choice to be built that add to the current understanding of this complex decision process.
Data was extracted from the WA Cancer Registry on women diagnosed with breast cancer in WA from 1990 to 2000. Subjects' treatment preferences were predicted from covariates using both classification trees and logistic regression.
Tumor size was the primary determinant of patient choice, subjects with tumors smaller than 20 mm in diameter preferring BCS. For subjects with tumors greater than 20 mm in diameter factors such as patient age, nodal status, and tumor histology become relevant as predictors of patient choice.
Classification trees perform as well as logistic regression for predicting patient choice, but are much easier to interpret for clinical use. The selected tree can inform clinicians' advice to patients.
乳腺癌患者面临的一个关键选择是哪种手术治疗——乳房切除术还是保乳手术(BCS)——最为合适。多项研究调查了影响手术选择类型的因素,确定了诸如居住地、诊断时年龄、肿瘤大小、社会经济和种族/民族因素等特征具有相关性。这种“倾向”评估对于理解诸如在不适合保乳手术的女性中保乳手术使用不足等问题很重要。利用西澳大利亚州(WA)的数据,我们使用分类树方法进一步研究与乳腺癌手术治疗类型相关的因素。这种方法自然地处理了因素之间复杂的相互作用,因此可以构建灵活且可解释的治疗选择模型,加深对这一复杂决策过程的当前理解。
从WA癌症登记处提取1990年至2000年在WA被诊断为乳腺癌的女性的数据。使用分类树和逻辑回归从协变量预测受试者的治疗偏好。
肿瘤大小是患者选择的主要决定因素,直径小于20毫米的肿瘤患者更喜欢保乳手术。对于直径大于20毫米的肿瘤患者,患者年龄、淋巴结状态和肿瘤组织学等因素成为患者选择的相关预测因素。
在预测患者选择方面,分类树与逻辑回归表现相当,但在临床应用中更容易解释。所选的树可以为临床医生给患者的建议提供参考。