ArticlePhysics in medicine and biology2017
Automated detection of breast cancer in resected specimens with fluorescence lifetime imaging.
Article in Physics in medicine and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 48 citations in OpenAlex.
- Factors associated with discordance between pathological and radiological tumor size and risk of re-excision in breast-conserving surgery: a post-hoc analysis within a randomized trial.Breast cancer research and treatment · 2026Trial
- Intraoperative Mapping of Parathyroid Glands Using Fluorescence Lifetime Imaging.The Journal of surgical research · 2021Trial
- Development of solid-state fluorescence lifetime standards for clinical applications using dyed epoxy resins.Journal of biomedical optics · 2026Article
- Article
- Fluorescence Lifetime Imaging Techniques-A Review on Principles, Applications and Clinical Relevance.Journal of biophotonics · 2025Review
- Identification of colorectal malignancies enabled by phasor-based autofluorescence lifetime macroimaging and ensemble learning.Biophotonics discovery · 2025Article
- Fluorescence lifetime imaging with distance and ranging using a miniaturised SPAD system.Scientific reports · 2024Article
- Assessing the impact of extracellular matrix fiber orientation on breast cancer cellular metabolism.Cancer cell international · 2024Article
- Dual excitation spectral autofluorescence lifetime and reflectance imaging for fast macroscopic characterization of tissues.Biomedical optics express · 2024Article
- Deformable multi-modal image registration for the correlation between optical measurements and histology images.Journal of biomedical optics · 2024Article
- Macroscopic inelastic scattering imaging using a hyperspectral line-scanning system identifies invasive breast cancer in lumpectomy and mastectomy specimens.Journal of biomedical optics · 2024Article
- Applications of machine learning in time-domain fluorescence lifetime imaging: a review.Methods and applications in fluorescence · 2024Review
- Combined fluorescence lifetime and surface topographical imaging of biological tissue.Biomedical optics express · 2024Article
- Probing organoid metabolism using fluorescence lifetime imaging microscopy (FLIM): The next frontier of drug discovery and disease understanding.Advanced drug delivery reviews · 2023Review
- Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning.Journal of medical imaging (Bellingham, Wash.) · 2023Article
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- Article
- Deep learning in macroscopic diffuse optical imaging.Journal of biomedical optics · 2022Review
- Metabolic Changes in Maternal and Cord Blood in One Case of Pregnancy-Associated Breast Cancer Seen by Fluorescence Lifetime Imaging Microscopy.Diagnostics (Basel, Switzerland) · 2021Article
- Multispectral fluorescence lifetime imaging device with a silicon avalanche photodetector.Optics express · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Re-excision rates for breast cancer lumpectomy procedures are currently nearly 25% due to surgeons relying on inaccurate or incomplete methods of evaluating specimen margins. The objective of this study was to determine if cancer could be automatically detected in breast specimens from mastectomy and lumpectomy procedures by a classification algorithm that incorporated parameters derived from fluorescence lifetime imaging (FLIm). This study generated a database of co-registered histologic sections and FLIm data from breast cancer specimens (N = 20) and a support vector machine (SVM) classification algorithm able to automatically detect cancerous, fibrous, and adipose breast tissue. Classification accuracies were greater than 97% for automated detection of cancerous, fibrous, and adipose tissue from breast cancer specimens. The classification worked equally well for specimens scanned by hand or with a mechanical stage, demonstrating that the system could be used during surgery or on excised specimens. The ability of this technique to simply discriminate between cancerous and normal breast tissue, in particular to distinguish fibrous breast tissue from tumor, which is notoriously challenging for optical techniques, leads to the conclusion that FLIm has great potential to assess breast cancer margins. Identification of positive margins before waiting for complete histologic analysis could significantly reduce breast cancer re-excision rates.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.