Automated classification of breast pathology using local measures of broadband reflectance

. We demonstrate that morphological features pertinent to a tissue’s pathology may be ascertained from localized measures of broadband reflectance, with a mesoscopic resolution (100-μ m lateral spot size) that permits scanning of an entire margin for residual disease. The technical aspects and optimization of a k -nearest neighbor classifier for automated diagnosis of pathologies are presented, and its efficacy is validated in 29 breast tissue specimens. When discriminating between benign and malignant pathologies, a sensitivity and specificity of 91 and 77% was achieved. Furthermore, detailed subtissue-type analysis was performed to consider how diverse pathologies influence scattering response and overall classification efficacy. The increased sensitivity of this technique may render it useful to guide the surgeon or pathologist where to sample pathology for microscopic assessment.

我们证明，与组织病理学相关的形态学特征可以通过宽带反射的局部测量来确定，其介观分辨率（横向光斑尺寸为100μm）允许对整个边缘进行残留疾病扫描。介绍了用于病理自动诊断的k - 近邻分类器的技术方面和优化，并在29个乳腺组织标本中验证了其有效性。在区分良性和恶性病理时，实现了91%的灵敏度和77%的特异性。此外，进行了详细的子组织类型分析，以考虑不同的病理如何影响散射响应和整体分类效果。这种技术提高的灵敏度可能使其有助于指导外科医生或病理学家在何处对病理进行取样以进行显微镜评估。