Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers
Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers
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DOI：
10.1148/radiol.09090838
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发表时间：
2010-03-01
期刊：
影响因子：
19.7
通讯作者：
Newstead, Gillian M.
中科院分区：
文献类型：
--
作者：
Bhooshan, Neha;Giger, Maryellen L.;Newstead, Gillian M.
目的：评估计算机提取的动态对比材料增强 (DCE) 磁共振 (MR) 成像动力学和形态学特征在区分侵入性与非侵入性乳腺病变以及转移性与非转移性乳腺病变方面的表现。 材料和方法：在这项机构审查委员会批准的符合 HIPAA 的研究中，其中要求患者知情同意 放弃，回顾性收集乳房 MR 图像。这些图像是通过 1.5-T MR 装置在稳态序列中使用钆双酰胺增强的 T1 加权破坏梯度回忆采集获得的。乳腺 MR 成像数据库包含 132 个良性病变、71 个导管原位癌 (DCIS) 和 150 个浸润性导管癌 (IDC) 病变。 54 个 IDC 病灶与转移阳性淋巴结 (LN) 相关，64 个 IDC 病灶与阴性 LN 相关。病灶分割以及形态和动力学特征的提取由实验室开发的计算机工作站自动执行。首先使用逐步线性判别分析来选择特征，然后使用贝叶斯神经网络进行合并。通过受试者工作特征分析评估病变分类性能。结果：DCIS 与 IDC 病变的区分得出受试者工作特征曲线下面积 (AUC) 为 0.83 +/- 0.03（标准误差）。区分 IDC 和良性病变的 AUC 为 0.85 +/- 0.02，区分 DCIS 和良性病变的 AUC 为 0.79 +/- 0.03。与阳性 LN 相关的 IDC 病变和与阴性 LN 相关的 IDC 病变之间的区分产生的 AUC 为 0.82 +/- 0.04。区分与阳性淋巴结相关的 IDC 病变和良性病变的 AUC 为 0.86 +/- 0.03，区分与阴性淋巴结相关的 IDC 病变和良性病变的 AUC 为 0.83±6 0.03。结论：乳腺 DCE MR 成像描绘的病变的计算机辅助诊断从区分恶性和良性病变的任务扩展到区分恶性和良性病变的预后任务。 区分非侵袭性和侵袭性病变，区分转移性和非转移性病变，产生基于 MR 成像的预后标记。 (C) 北美放射学会，2010
Purpose: To assess the performance of computer- extracted dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging kinetic and morphologic features in the differentiation of invasive versus noninvasive breast lesions and metastatic versus nonmetastatic breast lesions.Materials and Methods: In this institutional review board-approved HIPAA-compliant study, in which the requirement for informed patient consent was waived, breast MR images were retrospectively collected. The images had been obtained with a 1.5-T MR unit by using a gadodiamide-enhanced T1-weighted spoiled gradient-recalled acquisition in the steady state sequence. The breast MR imaging database contained 132 benign, 71 ductal carcinoma in situ (DCIS), and 150 invasive ductal carcinoma (IDC) lesions. Fifty-four IDC lesions were associated with metastasis-positive lymph nodes (LNs), and 64 IDC lesions were associated with negative LNs. Lesion segmentation and extraction of morphologic and kinetic features were automatically performed by a laboratory-developed computer workstation. Features were first selected by using stepwise linear discriminant analysis and then merged by using Bayesian neural networks. Lesion classification performance was assessed with receiver operating characteristic analysis.Results: Differentiation of DCIS from IDC lesions yielded an area under the receiver operating characteristic curve (AUC) of 0.83 +/- 0.03 (standard error). AUCs were 0.85 +/- 0.02 for differentiation between IDC and benign lesions and 0.79 +/- 0.03 for differentiation between DCIS and benign lesions. Differentiation between IDC lesions associated with positive LNs and IDC lesions associated with negative LNs yielded an AUC of 0.82 +/- 0.04. AUCs were 0.86 +/- 0.03 for differentiation between IDC lesions associated with positive LNs and benign lesions and 0.83 6 0.03 for differentiation between IDC lesions associated with negative LNs and benign lesions.Conclusion: Computer-aided diagnosis of breast DCE MR imaging depicted lesions was extended from the task of discriminating between malignant and benign lesions to the prognostic tasks of distinguishing between noninvasive and invasive lesions and discriminating between metastatic and nonmetastatic lesions, yielding MR imaging-based prognostic markers. (C) RSNA, 2010