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Updated: May 8, 2026
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Multispectral Real-time Fluorescence Imaging for Intraoperative Detection of the Sentinel Lymph Node in Gynecologic Oncology
Published on: October 20, 2010
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From Image-Guided Surgery to Computer-Assisted Real-Time Diagnosis with Hyperspectral and Multispectral Imaging: A
Chiara Innocenzi1,2,3, Matteo Pavone1,2,3,4,5, Barbara Seeliger3,4,5,6
1Dipartimento di Scienze per la Salute Della Donna e del Bambino, UOC Ginecologia Oncologica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy.
Diagnostics (Basel, Switzerland)
|February 27, 2026
Summary
Spectral imaging offers real-time, non-contact tissue analysis for gynecologic oncology surgery. This technology shows high sensitivity and specificity, aiding in accurate cancer detection and margin assessment.
Area of Science:
- Medical Imaging
- Oncology
- Gynecology
Background:
- Intraoperative image guidance is crucial in gynecologic oncology for accurate malignant tissue identification and negative resection margins.
- Emerging technologies like spectral imaging can enhance decision-making by providing real-time tissue composition and physiological status data.
- Spectral imaging operates without tissue contact, contrast agents, staining, or freezing, offering a non-invasive approach.
Purpose of the Study:
- To systematically review the clinical applications of spectral imaging in gynecologic oncology.
- To evaluate its utility in decision support and diagnostic performance for tissue classification.
- To synthesize data processing frameworks used in conjunction with spectral imaging.
Main Methods:
- A systematic review adhering to PRISMA guidelines was conducted.
- Searches were performed across PubMed, Google Scholar, Embase, ClinicalTrials.gov, and Scopus databases until September 2025.
- Studies reporting data on spectral imaging in gynecologic oncology were included.
Main Results:
- Twenty-nine studies and two clinical trials met the inclusion criteria, primarily focusing on cervical neoplasia (58.6%) and ovarian cancer (24.1%).
- Overall sensitivity ranged from 75-100% and specificity from 30-99%, with high sensitivity for cervical lesions (79-100%) and ovarian cancer (81-100%).
- Forty-four percent of studies utilized data interpretation algorithms, predominantly machine learning (84.6%).
Conclusions:
- Spectral imaging, augmented by computational methods, demonstrates significant promise in gynecologic disease diagnostics.
- It provides functional and molecular information surpassing standard visual assessment capabilities.
- This technology can improve intraoperative decision-making and patient outcomes in gynecologic oncology.
