Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model.
Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model.
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DOI：
10.1158/1078-0432.ccr-20-2017
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发表时间：
2021-02-15
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通讯作者：
Dale AM
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作者：
Andreassen MMS;Rodríguez-Soto AE;Conlin CC;Vidić I;Seibert TM;Wallace AM;Zare S;Kuperman J;Abudu B;Ahn GS;Hahn M;Jerome NP;Østlie A;Bathen TF;Ojeda-Fournier H;Goa PE;Rakow-Penner R;Dale AM
扩散加权磁共振成像（DW - MRI）是一种无需造影剂的成像方式，已被证明能够区分预先定义的乳腺良性和恶性病变。然而，在临床环境中，DW - MRI在区分癌症与所有其他乳腺组织体素方面的效果尚不清楚。在此，我们利用新开发的三分量多b值DW - MRI模型的信号贡献，探索区分癌症与健康乳腺组织的体素层面能力。
来自两个数据集（n = 81和n = 25）的经病理证实的乳腺癌患者接受了多b值DW - MRI检查。将三分量信号贡献C1和C2及其乘积C1C2，以及信号分数F1、F2和F1F2与最大b值图像（DWImax）、常规表观扩散系数（ADC）和表观扩散峰度（Kapp）所定义的图像进行比较。通过在80%灵敏度下的假阳性率（FPR80）和受试者工作特征（ROC）曲线下面积（AUC）来评估区分癌症与健康乳腺组织的能力。
两个数据集的C1C2平均FPR80为0.016（95%置信区间 = 0.008 - 0.024），C1为0.136（95%置信区间 = 0.092 - 0.180），C2为0.068（95%置信区间 = 0.049 - 0.087），F1F2为0.462（95%置信区间 = 0.425 - 0.499），F1为0.832（95%置信区间 = 0.797 - 0.868），F2为0.176（95%置信区间 = 0.150 - 0.203），DWImax为0.159（95%置信区间 = 0.114 - 0.204），ADC为0.731（95%置信区间 = 0.692 - 0.770），Kapp为0.684（95%置信区间 = 0.660 - 0.709）。C1C2的平均ROC AUC为0.984（95%置信区间 = 0.977 - 0.991）。
三分量模型的C1C2参数在临床上能够有效区分癌症与健康乳腺组织，优于其他DW - MRI方法，且无需预先定义病变。这种新的DW - MRI方法可作为标准护理动态对比增强MRI（DCE - MRI）的无造影剂替代方法。
Diffusion-weighted magnetic resonance imaging (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between pre-defined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxel-wise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model. Pathology-proven breast cancer patients from two datasets (n=81 and n=25) underwent multi-b-value DW-MRI. The three-component signal contributions C1 and C2 and their product, C1C2, and signal fractions F1, F2 and F1F2 were compared to the image defined on maximum b-value (DWImax), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (Kapp). The ability to discriminate between cancer and healthy breast tissue was assessed by the false positive rate given a sensitivity of 80% (FPR80) and receiver operating characteristic (ROC) area under the curve (AUC). Mean FPR80 for both datasets was 0.016 (95%CI=0.008-0.024) for C1C2, 0.136 (95%CI=0.092-0.180) for C1, 0.068 (95%CI=0.049-0.087) for C2, 0.462 (95%CI=0.425-0.499) for F1F2, 0.832 (95%CI=0.797-0.868) for F1, 0.176 (95%CI=0.150-0.203) for F2, 0.159 (95%CI=0.114-0.204) for DWImax, 0.731 (95%CI=0.692-0.770) for ADC and 0.684 (95%CI=0.660-0.709) for Kapp. Mean ROC AUC for C1C2 was 0.984 (95%CI=0.977-0.991). The C1C2 parameter of the three-component model yields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating pre-defining lesions. This novel DW-MRI method may serve as non-contrast alternative to standard-of-care dynamic contrast-enhanced MRI (DCE-MRI).