Drukker Karen, Duewer Fred, Giger Maryellen L, Malkov Serghei, Flowers Chris I, Joe Bonnie, Kerlikowske Karla, Drukteinis Jennifer S, Li Hui, Shepherd John A
Department of Radiology, University of Chicago, Chicago, Illinois 60637.
Radiology Department, University of California, San Francisco, California 94143.
Med Phys. 2014 Mar;41(3):031915. doi: 10.1118/1.4866221.
To investigate whether biologic image composition of mammographic lesions can improve upon existing mammographic quantitative image analysis (QIA) in estimating the probability of malignancy.
The study population consisted of 45 breast lesions imaged with dual-energy mammography prior to breast biopsy with final diagnosis resulting in 10 invasive ductal carcinomas, 5 ductal carcinomain situ, 11 fibroadenomas, and 19 other benign diagnoses. Analysis was threefold: (1) The raw low-energy mammographic images were analyzed with an established in-house QIA method, "QIA alone," (2) the three-compartment breast (3CB) composition measure-derived from the dual-energy mammography-of water, lipid, and protein thickness were assessed, "3CB alone", and (3) information from QIA and 3CB was combined, "QIA + 3CB." Analysis was initiated from radiologist-indicated lesion centers and was otherwise fully automated. Steps of the QIA and 3CB methods were lesion segmentation, characterization, and subsequent classification for malignancy in leave-one-case-out cross-validation. Performance assessment included box plots, Bland-Altman plots, and Receiver Operating Characteristic (ROC) analysis.
The area under the ROC curve (AUC) for distinguishing between benign and malignant lesions (invasive and DCIS) was 0.81 (standard error 0.07) for the "QIA alone" method, 0.72 (0.07) for "3CB alone" method, and 0.86 (0.04) for "QIA+3CB" combined. The difference in AUC was 0.043 between "QIA + 3CB" and "QIA alone" but failed to reach statistical significance (95% confidence interval [-0.17 to + 0.26]).
In this pilot study analyzing the new 3CB imaging modality, knowledge of the composition of breast lesions and their periphery appeared additive in combination with existing mammographic QIA methods for the distinction between different benign and malignant lesion types.
研究乳腺病变的生物图像组成在估计恶性概率方面是否能改进现有的乳腺X线定量图像分析（QIA）。
研究人群包括45例在乳腺活检前接受双能乳腺X线摄影成像的乳腺病变，最终诊断结果为10例浸润性导管癌、5例导管原位癌、11例纤维腺瘤和19例其他良性诊断。分析分为三个方面：（1）使用既定的内部QIA方法对原始低能乳腺X线图像进行分析，即“单独QIA”；（2）评估源自双能乳腺X线摄影的水、脂质和蛋白质厚度的三室乳腺（3CB）组成测量值，即“单独3CB”；（3）将QIA和3CB的信息相结合，即“QIA + 3CB”。分析从放射科医生指示的病变中心开始，其他方面则完全自动化。QIA和3CB方法的步骤包括病变分割、特征描述以及随后在留一法交叉验证中对恶性病变进行分类。性能评估包括箱线图、布兰德 - 奥特曼图和受试者操作特征（ROC）分析。
“单独QIA”方法区分良性和恶性病变（浸润性癌和导管原位癌）的ROC曲线下面积（AUC）为0.81（标准误差0.07），“单独3CB”方法为0.72（0.07），“QIA + 3CB”组合为0.86（0.04）。“QIA + 3CB”与“单独QIA”之间的AUC差异为0.043，但未达到统计学显著性（95%置信区间[-0.17至+0.26]）。
在这项分析新的3CB成像模式的初步研究中，乳腺病变及其周边组成的知识与现有的乳腺X线QIA方法相结合，在区分不同良性和恶性病变类型方面似乎具有相加作用。
