Laughney Ashley M, Krishnaswamy Venkataramanan, Rizzo Elizabeth J, Schwab Mary C, Barth Richard J, Cuccia David J, Tromberg Bruce J, Paulsen Keith D, Pogue Brian W, Wells Wendy A
Breast Cancer Res. 2013;15(4):R61. doi: 10.1186/bcr3455.
Nationally, 25% to 50% of patients undergoing lumpectomy for local management of breast cancer require a secondary excision because of the persistence of residual tumor. Intraoperative assessment of specimen margins by frozen-section analysis is not widely adopted in breast-conserving surgery. Here, a new approach to wide-field optical imaging of breast pathology in situ was tested to determine whether the system could accurately discriminate cancer from benign tissues before routine pathological processing.
Spatial frequency domain imaging (SFDI) was used to quantify near-infrared (NIR) optical parameters at the surface of 47 lumpectomy tissue specimens. Spatial frequency and wavelength-dependent reflectance spectra were parameterized with matched simulations of light transport. Spectral images were co-registered to histopathology in adjacent, stained sections of the tissue, cut in the geometry imaged in situ. A supervised classifier and feature-selection algorithm were implemented to automate discrimination of breast pathologies and to rank the contribution of each parameter to a diagnosis.
Spectral parameters distinguished all pathology subtypes with 82% accuracy and benign (fibrocystic disease, fibroadenoma) from malignant (DCIS, invasive cancer, and partially treated invasive cancer after neoadjuvant chemotherapy) pathologies with 88% accuracy, high specificity (93%), and reasonable sensitivity (79%). Although spectral absorption and scattering features were essential components of the discriminant classifier, scattering exhibited lower variance and contributed most to tissue-type separation. The scattering slope was sensitive to stromal and epithelial distributions measured with quantitative immunohistochemistry.
SFDI is a new quantitative imaging technique that renders a specific tissue-type diagnosis. Its combination of planar sampling and frequency-dependent depth sensing is clinically pragmatic and appropriate for breast surgical-margin assessment. This study is the first to apply SFDI to pathology discrimination in surgical breast tissues. It represents an important step toward imaging surgical specimens immediately ex vivo to reduce the high rate of secondary excisions associated with breast lumpectomy procedures.
在全国范围内，因局部治疗乳腺癌而接受肿块切除术的患者中，有25%至50%因残留肿瘤持续存在而需要二次切除。在保乳手术中，通过冰冻切片分析对标本边缘进行术中评估并未得到广泛应用。在此，测试了一种用于乳腺病理原位宽视野光学成像的新方法，以确定该系统在常规病理处理之前能否准确区分癌组织和良性组织。
使用空间频域成像（SFDI）对47个肿块切除组织标本表面的近红外（NIR）光学参数进行量化。空间频率和波长相关的反射光谱通过光传输的匹配模拟进行参数化。光谱图像与组织相邻染色切片中的组织病理学进行配准，这些切片是按照原位成像的几何形状切割的。实施了监督分类器和特征选择算法，以自动区分乳腺病理类型并对每个参数对诊断的贡献进行排名。
光谱参数以82%的准确率区分了所有病理亚型，以88%的准确率区分了良性（纤维囊性疾病、纤维腺瘤）和恶性（导管原位癌、浸润性癌以及新辅助化疗后部分治疗的浸润性癌）病理类型，具有高特异性（93%）和合理的敏感性（79%）。尽管光谱吸收和散射特征是判别分类器的重要组成部分，但散射表现出较低的方差，并且对组织类型分离的贡献最大。散射斜率对通过定量免疫组织化学测量的基质和上皮分布敏感。
SFDI是一种新的定量成像技术，可进行特定组织类型的诊断。其平面采样和频率相关深度传感的结合在临床上是实用的，适用于乳腺手术边缘评估。本研究首次将SFDI应用于手术乳腺组织的病理鉴别。这代表了朝着立即对离体手术标本进行成像迈出的重要一步，以降低与乳腺肿块切除术相关的高二次切除率。
