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首页> 外文会议>SPIE Medical Imaging Conference >Analysis of DCE-MRI features in tumor and the surrounding stroma for prediction of Ki-67 proliferation status in breast cancer
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Analysis of DCE-MRI features in tumor and the surrounding stroma for prediction of Ki-67 proliferation status in breast cancer

机译：分析肿瘤和周围基质中的DCE-MRI特征以预测乳腺癌Ki-67的增殖状态

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Breast cancer, with its high heterogeneity, is the most common malignancies in women. In addition to the entire tumor itself, tumor microenvironment could also play a fundamental role on the occurrence and development of tumors. The aim of this study is to investigate the role of heterogeneity within a tumor and the surrounding stromal tissue in predicting the Ki-67 proliferation status of oestrogen receptor (ER)-positive breast cancer patients. To this end, we collected 62 patients imaged with preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for analysis. The tumor and the peritumoral stromal tissue were segmented into 8 shells with 5 mm width outside of tumor. The mean enhancement rate in the stromal shells showed a decreasing order if their distances to the tumor increase. Statistical and texture features were extracted from the tumor and the surrounding stromal bands, and multivariate logistic regression classifiers were trained and tested based on these features. An area under the receiver operating characteristic curve (AUC) were calculated to evaluate performance of the classifiers. Furthermore, the statistical model using features extracted from boundary shell next to the tumor produced AUC of 0.796±0.076, which is better than that using features from the other subregions. Furthermore, the prediction model using 7 features from the entire tumor produced an AUC value of 0.855±0.065. The classifier based on 9 selected features extracted from peritumoral stromal region showed an AUC value of 0.870±0.050. Finally, after fusion of the predictive model obtained from entire tumor and the peritumoral stromal regions, the classifier performance was significantly improved with AUC of 0.920. The results indicated that heterogeneity in tumor boundary and peritumoral stromal region could be valuable in predicting the indicator associated with prognosis.
机译：乳腺癌具有很高的异质性，是女性中最常见的恶性肿瘤。除了整个肿瘤本身之外，肿瘤微环境还可以在肿瘤的发生和发展中发挥重要作用。这项研究的目的是调查肿瘤和周围基质组织内异质性在预测雌激素受体（ER）阳性乳腺癌患者的Ki-67增殖状态中的作用。为此，我们收集了62例接受术前动态对比增强磁共振成像（DCE-MRI）成像的患者进行分析。将肿瘤和肿瘤周围基质组织切成8个壳，在肿瘤外5mm宽。如果它们与肿瘤的距离增加，则间质壳中的平均增强率显示出递减的顺序。从肿瘤和周围的基质带中提取统计和纹理特征，并基于这些特征对多元逻辑回归分类器进行训练和测试。计算接收器工作特性曲线（AUC）下的面积以评估分类器的性能。此外，使用从肿瘤附近的边界壳提取的特征的统计模型产生的AUC为0.796±0.076，这优于使用其他子区域的特征的AUC。此外，使用来自整个肿瘤的7个特征的预测模型产生的AUC值为0.855±0.065。基于从肿瘤周围基质区域提取的9个选定特征的分类器显示的AUC值为0.870±0.050。最后，融合从整个肿瘤和肿瘤周围间质区域获得的预测模型后，AUC为0.920，分类器性能显着提高。结果表明，肿瘤边界和肿瘤周围基质区域的异质性可能在预测与预后相关的指标方面具有重要价值。

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