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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：肿瘤DCE-MRI特征分析及周边基质，以预测乳腺癌KI-67增殖状况

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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：具有高异质性的乳腺癌是女性中最常见的恶性肿瘤。除了整个肿瘤本身外，肿瘤微环境也可能对肿瘤的发生和发展起到基本作用。本研究的目的是探讨异质性在肿瘤内的作用和周围的基质组织在预测雌激素受体（ER） - 阳性乳腺癌患者的KI-67增殖状态方面。为此，我们收集了与术前动态对比度增强磁共振成像（DCE-MRI）成像的62名患者进行分析。将肿瘤和腹部基质组织分段为8个壳，在肿瘤外的5毫米宽度。基质壳中的平均增强速率在其对肿瘤增加的距离增加时显示出降低的顺序。从肿瘤和周围的基质带中提取统计和纹理特征，并且基于这些特征训练和测试多变量逻辑回归分类器。计算接收器操作特征曲线（AUC）的区域以评估分类器的性能。此外，使用从肿瘤旁边的边界壳中提取的特征产生的统计模型产生0.796±0.076的AUC，这比使用来自其他地区的特征更好。此外，使用来自整个肿瘤的7个特征的预测模型产生了0.855±0.065的AUC值。基于9所选特征的分类器，从Perutumoral基质区域提取的90°表示0.870±0.050的AUC值。最后，在融合从整个肿瘤和腹部基质区域获得的预测模型之后，随着0.920的AUC显着改善了分类器性能。结果表明，肿瘤边界和腹部基质区域中的异质性在预测与预后相关的指标方面可能是有价值的。

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