Related Experiment Video
Updated: May 27, 2026

Multispectral Real-time Fluorescence Imaging for Intraoperative Detection of the Sentinel Lymph Node in Gynecologic Oncology
Published on: October 20, 2010
Bridging preclinical and clinical fluorescence-guided surgery with advanced cancer vision goggles
Haini Zhang1,2, Xiao Xu1, Christopher Ta1
1Department of Biomedical Engineering, The University of Texas Southwestern Medical Center, Dallas, TX, USA.
Abstract:
Near-infrared (NIR) fluorescence-guided surgery (FGS) is limited by operator-dependent acquisition and non-uniform datasets, hindering quantitative comparison between users, devices, and institutions. To address these limitations, we evaluated an advanced wearable Cancer Vision Goggles (CVG) platform that standardizes imaging via dual green-pointer alignment, enabling reproducible acquisition geometry. The preclinical component benchmarked imaging standardization, quantitative robustness, and agreement with established systems, whereas the clinical arm assessed feasibility and performance relative to an FDA-approved system. Performance was evaluated using quantitative endpoints, including tumor-to-nontumor ratio (TNR), normalized intensity maps (NIMs), and Sørensen-Dice (Dice) coefficient spatial overlap. CVG achieved comparable or superior tumor contrast with high spatial overlap, as confirmed by these quantitative analysis metrics. Unlike handheld systems, CVG maintained stable fluorescence detection with no significant change in tumor-to-nontumor ratio from 10 to 60 cm, enabling reproducible quantitative imaging over a broad working-distance range. Extension to human tumors from patients injected with an NIR molecular probe (ClinicalTrials.gov: NCT05576974, 04/08/2025) demonstrated performance equivalent to that of an established FGS system with a substantial footprint in the operating room. In addition, CVG provided practical advantages through standardized single-operator acquisition, reduced operator-dependent variability relative to handheld or cart-based imaging, and quantitative real-time threshold-based visualization. These findings establish a quantitatively validated wearable platform that standardizes FGS from preclinical benchmarking to clinically relevant tumor assessment.
Insights
A new wearable Cancer Vision Goggles (CVG) platform standardizes near-infrared fluorescence-guided surgery (FGS). This technology ensures reproducible imaging and quantitative analysis, improving tumor detection and surgical assessment.
Area of Science:
- Medical Imaging
- Surgical Technology
- Oncology
Background:
- Near-infrared fluorescence-guided surgery (FGS) faces challenges with operator variability and inconsistent data.
- Standardization is crucial for quantitative comparison across different users, devices, and institutions.
Purpose of the Study:
- To evaluate a wearable Cancer Vision Goggles (CVG) platform for standardized and quantitative FGS.
- To benchmark CVG's performance in preclinical models and assess its feasibility and effectiveness in clinical settings.
Main Methods:
- Utilized dual green-pointer alignment for reproducible acquisition geometry in the CVG platform.
- Benchmarked preclinical imaging standardization, quantitative robustness, and agreement with existing systems.
- Assessed clinical feasibility and performance against an FDA-approved FGS system using quantitative metrics like TNR, NIMs, and Dice coefficient.
Main Results:
- CVG demonstrated comparable or superior tumor contrast and high spatial overlap.
- Maintained stable fluorescence detection and quantitative imaging over a wide working distance (10-60 cm).
- Clinical evaluation showed performance equivalent to established FGS systems, with practical advantages in single-operator acquisition and reduced variability.
Conclusions:
- The Cancer Vision Goggles platform offers a quantitatively validated, standardized approach to FGS.
- CVG reduces operator dependency, enabling reproducible quantitative imaging from preclinical studies to clinical tumor assessment.
- This wearable technology enhances surgical precision and data reliability in fluorescence-guided procedures.

