Time-Resolved Fluorescence and Diffuse Reflectance (TRF-DR) Spectroscopy for Heterogeneous Breast Tissue Classification and Tumor Margin Assessment.
Re-excision rates remain high for early-stage breast cancer patients due to challenges in margin delineation during surgery, such as poorly defined tumor boundaries.
- Sensitivity 77%
- Specificity 68%
APA
Lad J, Dao E, et al. (2026). Time-Resolved Fluorescence and Diffuse Reflectance (TRF-DR) Spectroscopy for Heterogeneous Breast Tissue Classification and Tumor Margin Assessment.. Journal of biophotonics, 19(2), e70230. https://doi.org/10.1002/jbio.70230
MLA
Lad J, et al.. "Time-Resolved Fluorescence and Diffuse Reflectance (TRF-DR) Spectroscopy for Heterogeneous Breast Tissue Classification and Tumor Margin Assessment.." Journal of biophotonics, vol. 19, no. 2, 2026, pp. e70230.
PMID
41622347
Abstract
Re-excision rates remain high for early-stage breast cancer patients due to challenges in margin delineation during surgery, such as poorly defined tumor boundaries. Our group has developed a time-resolved fluorescence and diffuse reflectance (TRF-DR) spectroscopy system to assess tumor boundaries where tissue composition is heterogeneous. This study assesses system feasibility for classifying tumor in heterogeneous regions using weighted logistic regression and principal component analysis (PCA). A total of 4818 measurements from 73 frozen patient ex vivo samples were evaluated. Tissue measurement areas (1 mm × 1 mm) were pathologist-assigned percentages of low (< 25%), medium (25%-< 75%), and high (≥ 75%) for tumor, fibroglandular, or adipose composition. The model trained with four principal components (PCs) achieved sensitivity of 77% and specificity of 68%, comparable to current clinical techniques while being fast, portable, and economical. The TRF-DR system with weighted logistic regression demonstrates potential as an intraoperative margin assessment tool.
MeSH Terms
Humans; Breast Neoplasms; Female; Margins of Excision; Time Factors; Spectrometry, Fluorescence; Breast; Principal Component Analysis; Middle Aged