Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions.
Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions.
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DOI：
10.1038/s43856-021-00024-0
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Shepherd, John A
中科院分区：
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作者：
Leong, Lambert T;Malkov, Serghei;Drukker, Karen;Niell, Bethany L;Sadowski, Peter;Wolfgruber, Thomas;Greenwood, Heather I;Joe, Bonnie N;Kerlikowske, Karla;Giger, Maryellen L;Shepherd, John A
虽然乳房成像，如全视野数字乳房X光照相和数字乳房断层扫描有助于降低乳腺癌死亡率，但存在特异性低的问题，导致不必要的活检。诊断决策中使用的基本信息主要基于病变的形态。我们探索了一种称为三室乳房(3CB)的双能量成分乳房成像技术，以展示添加成分信息如何提高恶性肿瘤检测。接受乳腺影像报告和数据系统(BI-RADS)诊断为4级或5级并计划进行乳腺活检的女性被连续招募进行标准乳房X光检查和3CB成像。计算机辅助检测(CAD)软件被用来为所有活检的病变指定基于形态学的恶性预测。使用3CB成像计算所有病变的成分特征，神经网络用成分评估CAD预测，以预测新的恶性肿瘤概率。将CAD和神经网络预测与活检病理进行比较。将3CB成分信息添加到CAD改进了对恶性肿瘤的预测，导致在坚持测试集上的接收器操作特征曲线(AUC)下的面积为0.81(可信区间(CI)为0.74-0.88)，而仅CAD软件实现的AUC为0.69(CI为0.60-0.78)。我们还发现，浸润性乳腺癌具有独特的成分特征，与周围组织相比，其特征是脂肪含量减少，水分和蛋白质含量增加。在临床上，3CB可能提供更高的预测恶性肿瘤的准确性，并为探索成分乳房成像生物标记物提供一条可行的途径。Leong et al.使用一种名为三室乳房成像的双能量乳房X光照相技术来研究乳房成分并检测恶性病变。将成分信息与基于形态的计算机辅助诊断相结合，可以提高乳腺癌的检测能力。乳腺癌是通过乳房X光照相检查发现的。这项研究探索了一种特殊的乳房X光检查技术，以获取有关乳腺癌和非癌症乳腺组织成分的信息。这项技术除了提供标准乳房X光检查提供的形状特征外，还提供了脂肪(脂肪)、水分和蛋白质含量的测量。将有关组织成分的信息添加到其形状特征中，可以提高区分侵袭性癌症组织和未受影响的环境的能力。浸润性乳腺癌组织也被发现与其他怀疑患有癌症的非浸润性、非癌性乳腺癌组织相比，表现出较低的脂肪、较高的蛋白质和较高的水分含量。我们的发现强调了在决定是否需要对可疑组织进行活组织检查时，包括乳房组织成分的附加价值。
While breast imaging such as full-field digital mammography and digital breast tomosynthesis have helped to reduced breast cancer mortality, issues with low specificity exist resulting in unnecessary biopsies. The fundamental information used in diagnostic decisions are primarily based in lesion morphology. We explore a dual-energy compositional breast imaging technique known as three-compartment breast (3CB) to show how the addition of compositional information improves malignancy detection. Women who presented with Breast Imaging-Reporting and Data System (BI-RADS) diagnostic categories 4 or 5 and who were scheduled for breast biopsies were consecutively recruited for both standard mammography and 3CB imaging. Computer-aided detection (CAD) software was used to assign a morphology-based prediction of malignancy for all biopsied lesions. Compositional signatures for all lesions were calculated using 3CB imaging and a neural network evaluated CAD predictions with composition to predict a new probability of malignancy. CAD and neural network predictions were compared to the biopsy pathology. The addition of 3CB compositional information to CAD improves malignancy predictions resulting in an area under the receiver operating characteristic curve (AUC) of 0.81 (confidence interval (CI) of 0.74–0.88) on a held-out test set, while CAD software alone achieves an AUC of 0.69 (CI 0.60–0.78). We also identify that invasive breast cancers have a unique compositional signature characterized by reduced lipid content and increased water and protein content when compared to surrounding tissues. Clinically, 3CB may potentially provide increased accuracy in predicting malignancy and a feasible avenue to explore compositional breast imaging biomarkers. Leong et al. use a dual-energy mammography technique termed three-compartment breast imaging to study breast composition and detect malignant lesions. Combining compositional information with morphology-based computer-aided diagnosis improves breast cancer detection. Breast cancers are detected by mammography. This study explored the use of a particular kind of mammography technique to obtain information about the composition of cancerous and non-cancerous breast tissue. This technique provided measures of lipid (fat), water, and protein content in addition to shape characteristics provided from standard mammography. Adding information about the composition of the tissue to its shape characteristics resulted in an increased ability to distinguish invasive cancerous tissue from unaffected surroundings. Invasive breast cancer tissues were also found to exhibit lower lipid, higher protein and higher water content when compared to other non-invasive, non-cancerous breast tissues in which cancer was suspected. Our findings highlight the added value of including the composition of breast tissue when deciding if biopsy of the suspicious tissue is warranted.