Light scattering measured with spatial frequency domain imaging can predict stromal versus epithelial proportions in surgically resected breast tissue

Abstract

.This study aims to determine if light scatter parameters measured with spatial frequency domain imaging (SFDI) can accurately predict stromal, epithelial, and adipose fractions in freshly resected, unstained human breast specimens. An explicit model was developed to predict stromal, epithelial, and adipose fractions as a function of light scattering parameters, which was validated against a quantitative analysis of digitized histology slides for N=31 specimens using leave-one-out cross-fold validation. Specimen mean stromal, epithelial, and adipose volume fractions predicted from light scattering parameters strongly correlated with those calculated from digitized histology slides (r=0.90, 0.77, and 0.91, respectively, p-value <1×10−6). Additionally, the ratio of predicted epithelium to stroma classified malignant specimens with a sensitivity and specificity of 90% and 81%, respectively, and also classified all pixels in malignant lesions with 63% and 79%, at a threshold of 1. All specimens and pixels were classified as malignant, benign, or fat with 84% and 75% accuracy, respectively. These findings demonstrate how light scattering parameters acquired with SFDI can be used to accurately predict and spatially map stromal, epithelial, and adipose proportions in fresh unstained, human breast tissue, and suggest that these estimations could provide diagnostic value.

Structured light imaging for breast-conserving surgery, part II: texture analysis and classification

“…They demonstrated pixel-level classification accuracy of 75% and specimen-level accuracy of 84%. 24 Beyond breast tissue characterization, recent work by Rowland et al 41 used SFDI reflectance data at multiple spatial frequencies to predict burn severity in a porcine model using a cubic support vector machine (SVM) classifier. At 24 h, they demonstrated 92.5% accuracy classifying burn severity.…”

“…22 Previous studies have investigated diffuse and subdiffuse SFDI for BCS specimen margin assessment using optical properties. [22][23][24] The work presented here represents part II of a two-part paper and used the largest SFDI dataset of fresh BCS tissue specimens published to date with the most extensive categorization of benign and malignant tissue subtypes. The complete dataset was introduced in part I of this paper, 15 which focused on optical scatter and color property quantification.…”

Spatial frequency domain imaging in 2019: principles, applications, and perspectives

“…109,110 Recently, the development of subdiffuse SFDI has shown an even stronger potential for highlighting structural changes related to scattering and the phase function moments on freshly excised human specimen. 52,111,112 Nandy et al have extended this concept of in vitro tissue analysis for in vivo ovarian cancer applications 113 as well as colon pathologies. 114 The other arm of activity is focusing on treatment/monitoring side.…”

Imaging sub-diffuse optical properties of cancerous and normal skin tissue using machine learning-aided spatial frequency domain imaging

“… 2 These optical properties have been found to be useful in many medical applications such as detecting burn wound severity, 3 monitoring blood occlusions, 4 , 5 and aiding in cancer diagnostics. 6 – 9 …”