对比度增强型乳腺磁共振成像的放射组学分析,用于优化乳腺癌虚拟预后生物标记物的建模。

IF 1.3 Q4 ONCOLOGY
Dogan S. Polat, Y. Xi, Keith Hulsey, Matthew Lewis, B. Dogan
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The objective was to investigate the role of magnetic resonance imaging (MRI) radiomics for yielding virtual prognostic biomarkers (ER, HER2 expression, tumor grade, molecular subtype, and T-stage).\n\n\nMaterials and Methods\nPatients with primary invasive breast cancer who underwent dynamic contrast-enhanced (DCE) breast MRI between July 2013 and July 2016 in a single center were retrospectively reviewed. Age, N-stage, grade, ER and HER2 status, and Ki-67 (%) were recorded. DCE images were segmented and Haralick texture features were extracted. The Bootstrap Lasso feature selection method was used to select a small subset of optimal texture features. Classification of the performance of the final model was assessed with the area under the receiver operating characteristic curve (AUC).\n\n\nResults\nMedian age of patients (n = 209) was 49 (21-79) years. 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引用次数: 0

摘要

目的乳腺癌的临床分期和结节状态与其他病理生物标志物(如雌激素受体(ER)、孕激素受体或人表皮生长因子受体 2(HER2)受体状态和肿瘤分级)相结合,对患者的管理具有最重要的临床意义。准确预测这些参数有助于避免不必要的干预,包括不必要的手术。研究旨在探讨磁共振成像(MRI)放射组学在产生虚拟预后生物标志物(ER、HER2表达、肿瘤分级、分子亚型和T期)方面的作用。材料与方法回顾性研究了2013年7月至2016年7月期间在一个中心接受动态对比增强(DCE)乳腺磁共振成像检查的原发性浸润性乳腺癌患者。记录了年龄、N期、分级、ER和HER2状态以及Ki-67(%)。对 DCE 图像进行分割并提取 Haralick 纹理特征。采用 Bootstrap Lasso 特征选择法选出一小部分最佳纹理特征。结果患者(209 人)的平均年龄为 49(21-79)岁。该模型区分 N0 与 N1-N3 的敏感性、特异性、阳性预测值、阴性预测值和准确性分别为:71%、79%、76%、74%、75% [AUC = 0.78(95% 置信区间 (CI) 0.72-0.85)],区分 N0-N1 与 N2-N3 的敏感性、特异性、阳性预测值、阴性预测值和准确性分别为:81%、59%、24%、95%、62% [AUC = 0.74(95% 置信区间 (CI) 0.63-0.85)],区分 HER2(+)和 HER2(-)的比例分别为 79%、48%、34%、87%、56% [AUC = 0.64 (95% CI 0.54-0.73)],核分级高(2-3级)与核分级低(1级)的比例分别为56%、88%、96%、29%、61%[AUC=0.71(95% CI 0.63-0.80)];ER(+)与ER(-)的比例分别为[AUC=0.67(95% CI 0.59-0.76)]。结论使用磁共振成像对比纹理的定量放射组学有望识别侵袭性高级别、结节阳性的三阴性乳腺癌,并与较高的核分级、较高的T分期和N阳性分期有很好的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Radiomics Analysis of Contrast-Enhanced Breast MRI for Optimized Modelling of Virtual Prognostic Biomarkers in Breast Cancer.
Objective Breast cancer clinical stage and nodal status are the most clinically significant drivers of patient management, in combination with other pathological biomarkers, such as estrogen receptor (ER), progesterone receptor or human epidermal growth factor receptor 2 (HER2) receptor status and tumor grade. Accurate prediction of such parameters can help avoid unnecessary intervention, including unnecessary surgery. The objective was to investigate the role of magnetic resonance imaging (MRI) radiomics for yielding virtual prognostic biomarkers (ER, HER2 expression, tumor grade, molecular subtype, and T-stage). Materials and Methods Patients with primary invasive breast cancer who underwent dynamic contrast-enhanced (DCE) breast MRI between July 2013 and July 2016 in a single center were retrospectively reviewed. Age, N-stage, grade, ER and HER2 status, and Ki-67 (%) were recorded. DCE images were segmented and Haralick texture features were extracted. The Bootstrap Lasso feature selection method was used to select a small subset of optimal texture features. Classification of the performance of the final model was assessed with the area under the receiver operating characteristic curve (AUC). Results Median age of patients (n = 209) was 49 (21-79) years. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the model for differentiating N0 vs N1-N3 was: 71%, 79%, 76%, 74%, 75% [AUC = 0.78 (95% confidence interval (CI) 0.72-0.85)], N0-N1 vs N2-N3 was 81%, 59%, 24%, 95%, 62% [AUC = 0.74 (95% CI 0.63-0.85)], distinguishing HER2(+) from HER2(-) was 79%, 48%, 34%, 87%, 56% [AUC = 0.64 (95% CI 0.54-0.73)], high nuclear grade (grade 2-3) vs low grade (grades 1) was 56%, 88%, 96%, 29%, 61% [AUC = 0.71 (95% CI 0.63-0.80)]; and for ER (+) vs ER(-) status the [AUC=0.67 (95% CI 0.59-0.76)]. Radiomics performance in distinguishing triple-negative vs other molecular subtypes was [0.60 (95% CI 0.49-0.71)], and Luminal A [0.66 (95% CI 0.56-0.76)]. Conclusion Quantitative radiomics using MRI contrast texture shows promise in identifying aggressive high grade, node positive triple negative breast cancer, and correlated well with higher nuclear grades, higher T-stages, and N-positive stages.
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