Non-invasive optical estimate of tissue composition to differentiate malignant from benign breast lesions: A pilot study.
Non-invasive optical estimate of tissue composition to differentiate malignant from benign breast lesions: A pilot study.
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DOI：
10.1038/srep40683
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发表时间：
2017-01-16
影响因子：
4.6
通讯作者：
Cubeddu R
中科院分区：
文献类型：
--
作者：
Taroni P;Paganoni AM;Ieva F;Pifferi A;Quarto G;Abbate F;Cassano E;Cubeddu R
目前正在研究几种技术作为筛查乳房X光检查的补充，以降低其假阳性率，但结果仍不足以得出结论。这项初步研究探索时域漫射光学成像作为辅助方法来分类非侵入性恶性与良性乳腺病变。我们对同一乳房病变组织和平均健康组织之间的组织成分（氧合血红蛋白和脱氧血红蛋白、脂质、水、胶原蛋白）和吸收特性的差异进行了评估，对从患有乳房病变的受试者身上收集的 7 个红色近红外波长（635-1060 nm）的光学图像采用扰动方法。然后利用离散 AdaBoost 程序（一种机器学习算法）根据光学衍生信息（组织成分或吸收）和从患者病史中获得的危险因素（年龄、体重指数、熟悉程度、产次、口服避孕药的使用和他莫昔芬的使用）对病变进行分类。尤其是胶原蛋白含量，被证明是最重要的歧视参数。基于本研究的初步结果，所提出的方法值得进一步研究。
Several techniques are being investigated as a complement to screening mammography, to reduce its false-positive rate, but results are still insufficient to draw conclusions. This initial study explores time domain diffuse optical imaging as an adjunct method to classify non-invasively malignant vs benign breast lesions. We estimated differences in tissue composition (oxy- and deoxyhemoglobin, lipid, water, collagen) and absorption properties between lesion and average healthy tissue in the same breast applying a perturbative approach to optical images collected at 7 red-near infrared wavelengths (635–1060 nm) from subjects bearing breast lesions. The Discrete AdaBoost procedure, a machine-learning algorithm, was then exploited to classify lesions based on optically derived information (either tissue composition or absorption) and risk factors obtained from patient’s anamnesis (age, body mass index, familiarity, parity, use of oral contraceptives, and use of Tamoxifen). Collagen content, in particular, turned out to be the most important parameter for discrimination. Based on the initial results of this study the proposed method deserves further investigation.