University of Pennsylvania, Department of Physics and Astronomy, Philadelphia, Pennsylvania, United States.
University of California Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California, United States.
J Biomed Opt. 2021 Feb;26(2). doi: 10.1117/1.JBO.26.2.026004.
Current imaging paradigms for differential diagnosis of suspicious breast lesions suffer from high false positive rates that force patients to undergo unnecessary biopsies. Diffuse optical spectroscopic imaging (DOSI) noninvasively probes functional hemodynamic and compositional parameters in deep tissue and has been shown to be sensitive to contrast between normal and malignant tissues.
DOSI methods are under investigation as an adjunct to mammography and ultrasound that could reduce false positive rates and unnecessary biopsies, particularly in radiographically dense breasts.
We performed a retrospective analysis of 212 subjects with suspicious breast lesions who underwent DOSI imaging. Physiological tissue parameters were z-score normalized to the patient's contralateral breast tissue and input to univariate logistic regression models to discriminate between malignant tumors and the surrounding normal tissue. The models were then used to differentiate malignant lesions from benign lesions.
Models incorporating several individual hemodynamic parameters were able to accurately distinguish malignant tumors from both the surrounding background tissue and benign lesions with area under the curve (AUC) ≥0.85. Z-score normalization improved the discriminatory ability and calibration of these predictive models relative to unnormalized or ratio-normalized data.
Findings from a large subject population study show how DOSI data normalization that accounts for normal tissue heterogeneity and quantitative statistical regression approaches can be combined to improve the ability of DOSI to diagnose malignant lesions. This improved diagnostic accuracy, combined with the modality's inherent logistical advantages of portability, low cost, and nonionizing radiation, could position DOSI as an effective adjunct modality that could be used to reduce the number of unnecessary invasive biopsies.
目前用于可疑乳腺病变鉴别诊断的成像方法存在较高的假阳性率，导致患者需要接受不必要的活检。漫射光学光谱成像（DOSI）可无创探测深部组织的功能血流和成分参数，并且已被证明对正常组织和恶性组织之间的对比度敏感。
DOSI 方法正在作为乳腺 X 线摄影和超声的辅助手段进行研究，这可能会降低假阳性率和不必要的活检率，特别是在 X 线照相致密的乳房中。
我们对 212 例可疑乳腺病变患者进行了 DOSI 成像的回顾性分析。生理组织参数经 z 分数归一化为患者对侧乳房组织，并输入单变量逻辑回归模型，以区分恶性肿瘤与周围正常组织。然后，这些模型用于区分恶性病变与良性病变。
纳入多个血流动力学参数的模型能够准确区分恶性肿瘤与周围背景组织和良性病变，曲线下面积（AUC）≥0.85。z 分数归一化提高了这些预测模型的判别能力和校准能力，优于未归一化或比率归一化的数据。
来自大样本人群研究的结果表明，如何结合 DOSI 数据归一化来解释正常组织的异质性和定量统计回归方法，可以提高 DOSI 诊断恶性病变的能力。这种提高的诊断准确性，结合该方法固有的便携性、低成本和非电离辐射等逻辑优势，可使 DOSI 成为一种有效的辅助手段，用于减少不必要的有创活检数量。
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