Prediction of Oncotype DX recurrence score using deep multi-layer perceptrons in estrogen receptor-positive, HER2-negative breast cancer
Prediction of Oncotype DX recurrence score using deep multi-layer perceptrons in estrogen receptor-positive, HER2-negative breast cancer
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DOI：
10.1007/s12282-020-01100-4
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发表时间：
2020-05-08
期刊：
影响因子：
4
通讯作者：
Devalland, Christine
中科院分区：
文献类型：
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作者：
Baltres, Aline;Al Masry, Zeina;Devalland, Christine
Oncotype DX（ODX）是一种多基因表达特征，设计用于雌激素受体（ER）阳性和人表皮生长因子受体2（HER2）阴性乳腺癌患者，以预测复发评分（RS）和化疗（CT）获益。我们的研究的目的是开发一个预测工具的三个RS的类别的基础上，深层多层感知器（DMLP），只使用形态免疫组织学变量。我们对来自三家法国医院的320名接受ODX检测的患者进行了回顾性队列研究。记录临床病理特征。我们使用Matlab软件建立了一个有监督的机器学习分类模型，其中152例用于训练，168例用于测试。使用三个分类器来学习ODX的三个风险类别，即低风险、中等风险和高风险。实验结果分别提供了三种风险类别的曲线下面积（AUC）：0.63 [95%置信区间：（0.5446，0.7154），p < 0.001]，0.59 [95%置信区间：（0.5031，0.6769），p < 0.001]，0.75 [95%置信区间：（0.6184，0.8816），p < 0.001]。DMLP和ODX之间的实际RS和预测RS之间的一致率范围为53%至56%。低、中合并危险组的符合率为85%。我们开发了一种预测机器学习模型，可以帮助定义患者的RS。此外，我们综合组织病理学数据和DMLP结果来选择用于ODX测试的肿瘤。因此，该过程允许更相关地使用组织病理学数据，并优化和增强该信息。
Oncotype DX (ODX) is a multi-gene expression signature designed for estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer patients to predict the recurrence score (RS) and chemotherapy (CT) benefit. The aim of our study is to develop a prediction tool for the three RS's categories based on deep multi-layer perceptrons (DMLP) and using only the morphoimmunohistological variables. We performed a retrospective cohort of 320 patients who underwent ODX testing from three French hospitals. Clinico-pathological characteristics were recorded. We built a supervised machine learning classification model using Matlab software with 152 cases for the training and 168 cases for the testing. Three classifiers were used to learn the three risk categories of the ODX, namely the low, intermediate, and high risk. Experimental results provide the area under the curve (AUC), respectively, for the three risk categories: 0.63 [95% confidence interval: (0.5446, 0.7154), p < 0.001], 0.59 [95% confidence interval: (0.5031, 0.6769), p < 0.001], 0.75 [95% confidence interval: (0.6184, 0.8816), p < 0.001]. Concordance rate between actual RS and predicted RS ranged from 53 to 56% for each class between DMLP and ODX. The concordance rate of low and intermediate combined risk group was 85%. We developed a predictive machine learning model that could help to define patient's RS. Moreover, we integrated histopathological data and DMLP results to select tumor for ODX testing. Thus, this process allows more relevant use of histopathological data, and optimizes and enhances this information.