Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes
Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes
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DOI：
10.1007/s11307-019-01383-w
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发表时间：
2020-04-01
影响因子：
3.1
通讯作者：
Pinker, Katja
中科院分区：
文献类型：
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作者：
Leithner, Doris;Bernard-Davila, Blanca;Pinker, Katja
目的比较注解分割方法，评价放射组学分析应用于扩散加权成像(DWI)评价乳腺癌受体状态和分子亚型的价值。在这项IRB批准的符合HIPAA标准的回顾性研究中，91例经图像引导乳腺活检证实的未经治疗的乳腺癌患者(管腔A，n=49；管腔B，n=8；人表皮生长因子受体2[HER2]丰富，n=11；三阴性[TN]，n=23)在3T进行了多参数磁共振成像(MRI)，采用动态对比增强MRI、T2加权和DW成像。在高b值DW图像上手动分割病变，分割感兴趣区传播到表观扩散系数(ADC)图。此外，在仅在ADC图上可以识别病变的亚组(n=79)中，这些病变也被直接分割在那里。为了提取放射组学特征，提取和分析了以下特征：一阶直方图(HIS)、共生矩阵(COM)、游程矩阵(RLM)、绝对梯度、自回归模型(ARM)、离散Haar小波变换(WAV)和病变几何。特征选择采用Fisher、错误概率和平均相关系数、互信息系数等指标。线性判别分析和k近邻分类与留一交叉验证用于受体状态和分子亚型的两两区分。组织病理学结果被认为是金标准。结果对于在DWI和分割ROI上分割的病变，将其传播到ADC图上，获得了以下分类准确率：管腔B相对于HER2强化，94.7%(基于COM特征)；管腔B与其他，92.3%(COM，HIS)；HER2强化与其他，90.1%(RLM，COM)。对于直接在ADC图上分割的病变，获得了更好的结果，其分类准确率如下：管腔B与HER2增强，100%(COM，WAV)；管腔A与管腔B，91.5%(COM，WAV)；管腔B与其他，91.1%(WAV，ARM，COM)。结论DWI的放射组学特征结合ADC图可以评估乳腺癌的受体状态和分子亚型，诊断准确率高。当可以在ADC图上进行乳腺肿瘤分割时，可以获得更好的分类精度。
Purpose To compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping. Procedures In this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naive breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification with leave-one-out cross-validation was applied for pairwise differentiation of receptor status and molecular subtyping. Histopathologic results were considered the gold standard. Results For lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM). Conclusions Radiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.