Department of Community Health Sciences, Foothills Medical Centre, University of Calgary, Calgary, Alberta, Canada.
Department of Oncology, University of Calgary, Tom Baker Cancer Centre, Calgary, Alberta, Canada.
JCO Clin Cancer Inform. 2022 Feb;6:e2100055. doi: 10.1200/CCI.21.00055.
The optimal characteristics among patients with breast cancer to recommend neoadjuvant chemotherapy is an active area of clinical research. We developed and compared several approaches to developing prediction models for pathologic complete response (pCR) among patients with breast cancer in Alberta.
The study included all patients with breast cancer who received neoadjuvant chemotherapy in Alberta between 2012 and 2014 identified from the Alberta Cancer Registry. Patient, tumor, and treatment data were obtained through primary chart review. pCR was defined as no residual invasive tumor at surgical excision in breast or axilla. Two types of prediction models for pCR were built: (1) expert model: variables selected on the basis of oncologists' opinions and (2) data-driven model: variables selected by trained machine. These model types were fit using logistic regression (LR), random forests (RF), and gradient-boosted trees (GBT). We compared the models using area under the receiver operating characteristic curve and integrated calibration index, and internally validated using bootstrap resampling.
A total of 363 cases were included in the analyses, of which 86 experienced pCR. The RF and GBT fits yielded higher optimism-corrected area under the receiver operating characteristic curves compared with LR for the expert (RF: 0.70; GBT: 0.69; LR: 0.65) and data-driven models (RF: 0.71; GBT: 0.68; LR: 0.64). The LR fit yielded the lowest integrated calibration indices for the expert (LR: 0.037; GBT: 0.05; RF: 0.10) and data-driven models (LR: 0.026; GBT: 0.06; RF: 0.099).
Our models demonstrated predictive ability for pCR using routinely collected clinical and demographic variables. We show that machine learning fit methods can be used to optimize models for pCR prediction. We also show that additional variables beyond clinical expertise do not considerably improve predictive ability and may not be of value on the basis of the burden of data collection.
推荐新辅助化疗的乳腺癌患者的最佳特征是临床研究的一个活跃领域。我们开发并比较了几种方法，以在艾伯塔省建立预测乳腺癌患者病理完全缓解（pCR）的模型。
这项研究包括 2012 年至 2014 年间在艾伯塔癌症登记处确定的在艾伯塔省接受新辅助化疗的所有乳腺癌患者。通过主要图表审查获得患者、肿瘤和治疗数据。pCR 定义为在乳房或腋窝的外科切除标本中无残留浸润性肿瘤。建立了两种类型的 pCR 预测模型：（1）专家模型：根据肿瘤学家的意见选择变量；（2）数据驱动模型：通过训练的机器选择变量。使用逻辑回归（LR）、随机森林（RF）和梯度提升树（GBT）拟合这些模型类型。我们使用接受者操作特征曲线下的面积和综合校准指数比较模型，并通过引导重采样进行内部验证。
共纳入 363 例患者进行分析，其中 86 例患者达到 pCR。RF 和 GBT 拟合的专家（RF：0.70；GBT：0.69；LR：0.65）和数据驱动模型（RF：0.71；GBT：0.68；LR：0.64）的接受者操作特征曲线下的校正后面积均高于 LR。LR 拟合的专家（LR：0.037；GBT：0.05；RF：0.10）和数据驱动模型（LR：0.026；GBT：0.06；RF：0.099）的综合校准指数最低。
我们的模型使用常规收集的临床和人口统计学变量显示出对 pCR 的预测能力。我们表明，机器学习拟合方法可用于优化 pCR 预测模型。我们还表明，除临床专业知识外，额外的变量并不能显著提高预测能力，并且根据数据收集的负担，可能没有价值。
