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Spectral discrimination of breast pathologies in situ using spatial frequency domain imaging
- Source :
- Laughney, AM; Krishnaswamy, V; Rizzo, EJ; Schwab, MC; Barth J, RJ; Cuccia, DJ; et al.(2013). Spectral discrimination of breast pathologies in situ using spatial frequency domain imaging. Breast Cancer Research, 15(4). doi: 10.1186/bcr3455. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/2s2416sd, Breast Cancer Research : BCR, Laughney, Ashley M; Krishnaswamy, Venkataramanan; Rizzo, Elizabeth J; Schwab, Mary C; Barth, Richard J; Cuccia, David J; et al.(2013). Spectral discrimination of breast pathologies in situ using spatial frequency domain imaging. Breast Cancer Research, 15(4), R61. doi: http://dx.doi.org/10.1186/bcr3455. Retrieved from: http://www.escholarship.org/uc/item/4r82b0js
- Publication Year :
- 2013
- Publisher :
- eScholarship, University of California, 2013.
-
Abstract
- Introduction 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. Methods 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. Results 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. Conclusions 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.
- Subjects :
- Adult
medicine.medical_specialty
Pathology
medicine.medical_treatment
Biopsy
BCS/BCT
Breast Neoplasms
Spatial frequency domain imaging
Diagnostic pathology
Mastectomy, Segmental
01 natural sciences
Sensitivity and Specificity
010309 optics
03 medical and health sciences
0302 clinical medicine
Breast cancer
Breast-conserving surgery/therapy
Near-infrared spectroscopy
0103 physical sciences
medicine
Biomarkers, Tumor
Humans
Aged
Medicine(all)
Aged, 80 and over
Spectroscopy, Near-Infrared
medicine.diagnostic_test
business.industry
Carcinoma in situ
Lumpectomy
Reproducibility of Results
Middle Aged
medicine.disease
Prognosis
Fibroadenoma
Immunohistochemistry
3. Good health
Tumor Burden
030220 oncology & carcinogenesis
Histopathology
Female
Spatial frequency
Radiology
Neoplasm Grading
business
Mastectomy
Carcinoma in Situ
Research Article
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Laughney, AM; Krishnaswamy, V; Rizzo, EJ; Schwab, MC; Barth J, RJ; Cuccia, DJ; et al.(2013). Spectral discrimination of breast pathologies in situ using spatial frequency domain imaging. Breast Cancer Research, 15(4). doi: 10.1186/bcr3455. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/2s2416sd, Breast Cancer Research : BCR, Laughney, Ashley M; Krishnaswamy, Venkataramanan; Rizzo, Elizabeth J; Schwab, Mary C; Barth, Richard J; Cuccia, David J; et al.(2013). Spectral discrimination of breast pathologies in situ using spatial frequency domain imaging. Breast Cancer Research, 15(4), R61. doi: http://dx.doi.org/10.1186/bcr3455. Retrieved from: http://www.escholarship.org/uc/item/4r82b0js
- Accession number :
- edsair.doi.dedup.....890f7b9ea833514d439c35f437ccfb4f