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首页> 外文会议>SPIE Medical Imaging Conference >Quantitative diffusion weighted imaging parameters in tumor and peritumoral stroma for prediction of molecular subtypes in breast cancer
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Quantitative diffusion weighted imaging parameters in tumor and peritumoral stroma for prediction of molecular subtypes in breast cancer

机译：肿瘤肿瘤和骨质基质中的定量扩散加权成像参数，用于预测乳腺癌分子亚型的预测

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Breast cancer can be classified into four molecular subtypes of Luminal A, Luminal B, HER2 and Basal-like, which have significant differences in treatment and survival outcomes. We in this study aim to predict immunohistochemistry (IHC) determined molecular subtypes of breast cancer using image features derived from tumor and peritumoral stroma region based on diffusion weighted imaging (DWI). A dataset of 126 breast cancer patients were collected who underwent preoperative breast MR1 with a 3T scanner. The apparent diffusion coefficients (ADCs) were recorded from DWI, and breast image was segmented into regions comprising the tumor and the surrounding stromal. Statistical characteristics in various breast tumor and peritumoral regions were computed, including mean, minimum, maximum, variance, interquartile range, range, skewness, and kurtosis of ADC values. Additionally, the difference of features between each two regions were also calculated. The univariate logistic based classifier was performed for evaluating the performance of the individual features for discriminating subtypes. For multi-class classification, multivariate logistic regression model was trained and validated. The results showed that the tumor boundary and proximal peritumoral stroma region derived features have a higher performance in classification compared to that of the other regions. Furthermore, the prediction model using statistical features, difference features and all the features combined from these regions generated AUC values of 0.774, 0.796 and 0.811, respectively. The results in this study indicate that ADC feature in tumor and peritumoral stromal region would be valuable for estimating the molecular subtype in breast cancer.
机译：乳腺癌可以分为Luminal A型的4个分子亚型，管腔B，HER2和基底样，其具有在治疗和存活结果显著差异。我们在本研究目的是预测免疫组织化学（IHC）来确定使用图像乳腺癌的分子亚型从肿瘤及瘤周间质区导出的特征基于弥散加权成像（DWI）。 126例乳腺癌患者的数据集收集谁术前乳房MR1用3T扫描仪。表观扩散系数（ADC）的自DWI录，和乳房图像被分段成包括所述肿瘤区域和周围的基质。在各种乳腺肿瘤和肿瘤周围区域的统计特性进行了计算，包括平均值，最大值，最小值，方差，四分位数间距，范围，偏度和ADC值的峰度。另外，也进行了计算的各两个区域之间的特征的差。评估的用于区分亚型个别功能的性能进行了单变量逻辑基础的分类。对于多类分类，多因素Logistic回归模型进行训练和验证。结果表明，肿瘤边界和近端瘤周基质区衍生特征具有在分类的更高的性能相比于其它区域。此外，预测模型中使用的统计特征，区别特征和所有从生成的0.774，0.796分别和0.811，AUC值这些区域合并的功能。在这项研究的结果表明，在肿瘤和肿瘤周围的基质区域ADC特征将是在乳腺癌估计分子亚型有价值。

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