Machine learning prediction of axillary lymph node metastasis in breast cancer: 2D versus 3D radiomic features.
Machine learning prediction of axillary lymph node metastasis in breast cancer: 2D versus 3D radiomic features.
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DOI：
10.1002/mp.14538
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发表时间：
2020-12
期刊：
影响因子：
3.8
通讯作者：
Wu S
中科院分区：
文献类型：
--
作者：
Arefan D;Chai R;Sun M;Zuley ML;Wu S
本研究的目的是在术前应用乳腺DCE-MRI放射组学来区分腋窝淋巴结(ALN)状态，并比较二维(2D)和三维(3D)分析的效果。一项回顾性研究包括154例经病理证实的乳腺癌患者，其中80例有ALN转移，74例无ALN转移。所有MRI扫描均在3.0Tesla扫描机上完成，增强后7个时相依次采集，时间分辨率为60 S，分别从2D单层(即代表性层)和3D肿瘤体积中提取MRI的放射学特征。建立了几个机器学习分类器，并使用2D或3D分析进行比较，以区分ALN状态的阳性和阴性。采用自举检验、最小绝对收缩选择算子、受试者工作特征(ROC)曲线分析等统计检验方法，进行多次重复的独立检验和10倍交叉验证。二维和三维分析的ROC曲线下面积(AUC)分别为0.81(95%可信区间：0.80~0.83)和0.82(95%可信区间：0.81~0.82)，准确率分别为79%和80%。线性判别分析(LDA)分类器获得了最高的分类性能。对于几个测试的机器学习分类器，2D和3D分析之间的AUC差异在统计学上没有显著意义(均为P&gT；0.05)。乳腺MRI节段性肿块的放射学特征与ALN状态相关。在测试的机器学习分类器中，对3D肿瘤体积的单独放射学分析显示出与对单个代表性切片的2D分析相似的效果。
The purpose of this study was to distinguish axillary lymph node (ALN) status using preoperative breast DCE-MRI radiomics and compare the effects of two-dimensional (2D) and three-dimensional (3D) analysis. A retrospective study including 154 breast cancer patients all confirmed by pathology; 80 with ALN metastasis and 74 without. All MRI scans were achieved at a 3.0 Tesla scanner with 7 post-contrast MR phases sequentially acquired with a temporal resolution of 60 s. MRI radiomic features were extracted separately from a 2D single slice (i.e., the representative slice) and the 3D tumor volume. Several machine learning classifiers were built and compared using 2D or 3D analysis to distinguish positive vs negative ALN status. We performed independent test and 10-fold cross validation with multiple repetitions, and used bootstrap test, least absolute shrinkage selection operator, and receiver operating characteristic (ROC) curve analysis as statistical tests. The highest area under the ROC curve (AUC) was 0.81 (95% confidence intervals [CI]: 0.80–0.83) and 0.82 (95% CI: 0.81–0.82) for 2D and 3D analysis, respectively; the corresponding accuracy was 79% and 80%. The linear discriminant analysis (LDA) classifier achieved the highest classification performance. None of the AUC differences between 2D and 3D analysis was statistically significant for the several tested machine learning classifiers (all P> 0.05). Radiomic features from segmented tumor region in breast MRI were associated with ALN status. The separate radiomic analysis on 3D tumor volume showed a similar effect to the 2D analysis on the single representative slice in the tested machine learning classifiers.
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