ArticleBiomedical optics express2020
Real-time diagnosis and visualization of tumor margins in excised breast specimens using fluorescence lifetime imaging and machine learning.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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.
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Who cites it
31 citing papers in PubMed, 61 citations in OpenAlex.
- 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
- Imaging Through Scattering Tissue Using Near Infra-Red and a Convolutional Autoencoder.Sensors (Basel, Switzerland) · 2026Article
- Raman spectral band imaging for the diagnostics and classification of canine and feline cutaneous tumors.The veterinary quarterly · 2025Article
- Optical express-biopsy of gliomas using macroscopic fluorescence lifetime imaging.Biomedical optics express · 2025Article
- Optical diagnostics of liver tumors using an autofluorescence lifetime probe.Biomedical optics express · 2025Article
- Imaging Through Scattering Tissue Based on NIR Multispectral Image Fusion Technique.Sensors (Basel, Switzerland) · 2025Article
- Identification of colorectal malignancies enabled by phasor-based autofluorescence lifetime macroimaging and ensemble learning.Biophotonics discovery · 2025Article
- Development of a deep learning-based model for guiding a dissection during robotic breast surgery.Breast cancer research : BCR · 2025Article
- Advancements in real-time oncology diagnosis: harnessing AI and image fusion techniques.Frontiers in oncology · 2025Review
- Dual excitation spectral autofluorescence lifetime and reflectance imaging for fast macroscopic characterization of tissues.Biomedical optics express · 2024Article
- Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging.Scientific reports · 2024Article
- Applications of machine learning in time-domain fluorescence lifetime imaging: a review.Methods and applications in fluorescence · 2024Review
- Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images.Npj imaging · 2024Article
- Rapid azoospermia classification by stimulated Raman scattering and second harmonic generation microscopy.Biomedical optics express · 2023Article
- Probing organoid metabolism using fluorescence lifetime imaging microscopy (FLIM): The next frontier of drug discovery and disease understanding.Advanced drug delivery reviews · 2023Review
- Advancing Multi-Dimensional Vision: AI-Driven Imaging Using Unique Photodetectors with Integrated Surface Nanostructures.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Suppression of natural lens fluorescence in fundus autofluorescence measurements: review of hardware solutions.Biomedical optics express · 2022Review
- Intraoperative delineation of p16+ oropharyngeal carcinoma of unknown primary origin with fluorescence lifetime imaging: Preliminary report.Head & neck · 2022Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Tumor-free surgical margins are critical in breast-conserving surgery. In up to 38% of the cases, however, patients undergo a second surgery since malignant cells are found at the margins of the excised resection specimen. Thus, advanced imaging tools are needed to ensure clear margins at the time of surgery. The objective of this study was to evaluate a random forest classifier that makes use of parameters derived from point-scanning label-free fluorescence lifetime imaging (FLIm) measurements of breast specimens as a means to diagnose tumor at the resection margins and to enable an intuitive visualization of a probabilistic classifier on tissue specimen. FLIm data from fresh lumpectomy and mastectomy specimens from 18 patients were used in this study. The supervised training was based on a previously developed registration technique between autofluorescence imaging data and cross-sectional histology slides. A pathologist's histology annotations provide the ground truth to distinguish between adipose, fibrous, and tumor tissue. Current results demonstrate the ability of this approach to classify the tumor with 89% sensitivity and 93% specificity and to rapidly (∼ 20 frames per second) overlay the probabilistic classifier overlaid on excised breast specimens using an intuitive color scheme. Furthermore, we show an iterative imaging refinement that allows surgeons to switch between rapid scans with a customized, low spatial resolution to quickly cover the specimen and slower scans with enhanced resolution (400
Identifiers
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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.