Structured light imaging for breast-conserving surgery, part I: optical scatter and color analysis

Maloney, Benjamin W.; Streeter, Samuel S.; McClatchy, David M.; Pogue, Brian W.; Rizzo, Elizabeth J.; Wells, Wendy A.; Paulsen, Keith D.

doi:10.1117/1.jbo.24.9.096002

Cited by 9 publications

(13 citation statements)

References 19 publications

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“…A summary of all enrolled tissues can be found in part I of this paper. 15 For the texture analysis here, the enrolled tissue RoIs were sampled to create square subimages of constant size. The sampling process resulted in a subset of the original dataset being considered for texture analysis.…”

Section: Study Protocolmentioning

confidence: 99%

“…In addition, biopsies are performed prior to BCS procedures, and using this a priori tissue information could improve tissue margin diagnostic accuracy by reducing the number of possible malignant tissue subtypes. 15 Wide FOV (10 0 − 10 2 cm 2 ) structured light imaging, also known as spatial frequency-domain imaging (SFDI), involves illuminating tissue samples with one-dimensional (1-D) sinusoidal patterns at different spatial frequencies (f x ) and optical wavelengths (λ). Using a diffuse or subdiffuse light transport model, SFDI data can determine the optical absorption and scattering properties of biological tissue.…”

Section: Introductionmentioning

confidence: 99%

“…Photons are intrinsically confined to surface tissue and are most sensitive to large, single backscattering events with the angle and intensity of reflectance dependent on cellular ultrastructures. 20 In this regime, the modality is termed high spatial frequency (HSF) structured light imaging 15,21 or subdiffuse SFDI (sd-SFDI). 22 Previous studies have investigated diffuse and subdiffuse SFDI for BCS specimen margin assessment using optical properties.…”

Section: Introductionmentioning

confidence: 99%

“…[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. Optical scatter and color properties have demonstrated potential for breast tumor margin assessment.…”

Section: Introductionmentioning

confidence: 99%

“…However, both types of properties are inherently pixel-based, lacking spatial context between pixels. 15 Furthermore, optical property quantification necessitates significant computation, requiring either nonlinear least squares fitting of data to an appropriate light transport model or the generation of an empirical look-up table using a Monte Carlo model. 16,25 The process can be slow, 22 and modelbased inversion fitting can introduce additional errors.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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 ∼4 × 4 mm 2 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 (p-value < 0.05) 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 f x ¼ 1.37 mm −1 , optical wavelength of λ ¼ 490 nm) 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 (cm 2) BCS tumor margin assessment relative to pixel-based optical scatter or color properties alone.

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Section: Study Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Biophotonic technologies for assessment of breast tumor surgical margins—A review

Balasundaram

Krafft

Zhang

et al. 2020

Journal of Biophotonics

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Breast conserving surgery (BCS) offering similar surgical outcomes as mastectomy while retaining breast cosmesis is becoming increasingly popular for the management of early stage breast cancers. However, its association with reoperation rates of 20% to 40% following incomplete tumor removal warrants the need for a fast and accurate intraoperative surgical margin assessment tool that offers cellular, structural and molecular information of the whole specimen surface to a clinically relevant depth. Biophotonic technologies are evolving to qualify as such an intraoperative tool for clinical assessment of breast cancer surgical margins at the microscopic and macroscopic scale. Herein, we review the current research in the application of biophotonic technologies such as photoacoustic imaging, Raman spectroscopy, multimodal multiphoton imaging, diffuse optical imaging and fluorescence imaging using medically approved dyes for breast cancer detection and/or tumor subtype differentiation toward intraoperative assessment of surgical margins in BCS specimens, and possible challenges in their route to clinical translation.

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Evaluating Receptor-Specific Fresh Specimen Staining for Tumor Margin Detection in Clinical Breast Specimens

et al. 2023

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Purpose Reliable and rapid identification of tumor in the margins of breast specimens during breast-conserving surgery to reduce repeat surgery rates is an active area of investigation. Dual-stain difference imaging (DDSI) is one of many approaches under evaluation for this application. This technique aims to topically apply fluorescent stain pairs (one targeted to a receptor-of-interest and the other a spectrally distinct isotype), image both stains, and compute a normalized difference image between the two channels. Prior evaluation and optimization in a variety of preclinical models produced encouraging diagnostic performance. Herein, we report on a pilot clinical study which evaluated HER2-targeted DDSI on 11 human breast specimens. Procedures Gross sections from 11 freshly excised mastectomy specimens were processed using a HER2-receptor-targeted DDSI protocol shortly after resection. After staining with the dual-probe protocol, specimens were imaged on a fluorescence scanner, followed by tissue fixation for hematoxylin and eosin and anti-HER2 immunohistochemical staining. Receiver operator characteristic curves and area under the curve (AUC) analysis were used to assess diagnostic performance of the resulting images. Performance values were also compared to expression level determined from IHC staining. Results Eight of the 11 specimens presented with distinguishable invasive ductal carcinoma and/or were not affected by an imaging artifact. In these specimens, the DDSI technique provided an AUC = 0.90 ± 0.07 for tumor-to-adipose tissue and 0.81 ± 0.15 for tumor-to-glandular tissue, which was significantly higher than AUC values recovered from images of the targeted probe alone. DDSI values and diagnostic performance did not correlate with HER2 expression level, and tumors with low HER2 expression often produced high AUC, suggesting that even the low expression levels were enough to help distinguish tumor. Conclusions The results from this preliminary study of rapid receptor-specific staining in human specimens were consistent with prior preclinical results and demonstrated promising diagnostic potential.

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Structured light imaging for breast-conserving surgery, part I: optical scatter and color analysis

Cited by 9 publications

References 19 publications

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

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

Biophotonic technologies for assessment of breast tumor surgical margins—A review

Evaluating Receptor-Specific Fresh Specimen Staining for Tumor Margin Detection in Clinical Breast Specimens

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