Abstract
.Subdiffuse spatial frequency domain imaging (sd-SFDI) data of 42 freshly excised, bread-loafed tumor resections from breast-conserving surgery (BCS) were evaluated using texture analysis and a machine learning framework for tissue classification. Resections contained 56 regions of interest (RoIs) determined by expert histopathological analysis. RoIs were coregistered with sd-SFDI data and sampled into subimage samples of confirmed and homogeneous histological categories. Sd-SFDI reflectance textures were analyzed using gray-level co-occurrence matrix pixel statistics, image primitives, and power spectral density curve parameters. Texture metrics exhibited statistical significance () between three benign and three malignant tissue subtypes. Pairs of benign and malignant subtypes underwent texture-based, binary classification with correlation-based feature selection. Classification performance was evaluated using fivefold cross-validation and feature grid searching. Classification using subdiffuse, monochromatic reflectance (illumination spatial frequency of , optical wavelength of ) achieved accuracies ranging from 0.55 (95% CI: 0.41 to 0.69) to 0.95 (95% CI: 0.90 to 1.00) depending on the benign–malignant diagnosis pair. Texture analysis of sd-SFDI data maintains the spatial context within images, is free of light transport model assumptions, and may provide an alternative, computationally efficient approach for wide field-of-view () BCS tumor margin assessment relative to pixel-based optical scatter or color properties alone.
Highlights
For Breast-conserving surgery (BCS) to be effective, excised tissue margins must be clear of malignancy
Several techniques have been proposed for improved BCS margin assessment, but significant limitations associated with each approach have prevented their widespread adoption.[7]
Recent work by McClatchy et al.[22] provides an in-depth discussion related to Subdiffuse spatial frequency domain imaging (sd-spatial frequency-domain imaging (SFDI)) scatter as a contrast mechanism
Summary
Breast-conserving surgery (BCS) in combination with radiation therapy is the most common treatment for stage I and II breast cancer.[1,2] For BCS to be effective, excised tissue margins must be clear of malignancy (i.e., negative margins). 15% to 35% of BCS patients require a second surgery due to incomplete initial excision (i.e., one or more positive margins) as determined by histopathological analysis.[3,4,5,6,7,8] Identification of cancer at the margin is a spatial detection problem, filtered visually by pathology technicians and pathologists performing labor-intensive searches of tissue sections. Several techniques have been proposed for improved BCS margin assessment, but significant limitations associated with each approach have prevented their widespread adoption.[7] Common techniques include electrical impedance;[9,10] diffuse reflectance[11] and Raman spectroscopic point-sampling;[12] touch-prep cytology;[13] and frozen section pathology.[14] Importantly, point-sampling methods lack a comprehensive and/or practical approach to wide field-of-view (FOV) detection and are inherently timeconsuming. Touch-prep cytology and frozen section pathology are resource-intensive to process even a subsection of a BCS
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