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Updated: May 8, 2026
Multispectral Real-time Fluorescence Imaging for Intraoperative Detection of the Sentinel Lymph Node in Gynecologic Oncology
Published on: October 20, 2010
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.
Abstract:
Background: There is a need for intraoperative image guidance in gynecologic oncologic surgery to provide accurate identification of malignant tissue and ensure negative resection margins. Emerging imaging technologies can complement standard histopathology and reshape intraoperative decision-making. Spectral imaging can extract information on tissue composition and physiological status in real time, without the need for tissue contact, contrast agents, staining, or freezing. This systematic review synthesizes its current clinical applications in gynecologic oncology, decision support utility, and diagnostic performance with data processing frameworks for tissue classification. Materials and Methods: This systematic review (PROSPERO: CRD420251032899) adhered to PRISMA guidelines. PubMed, Google Scholar, Embase, ClinicalTrials.gov, and Scopus databases were searched until September 2025. Manuscripts reporting data on spectral imaging in gynecologic oncology were included in the analysis. Results: Twenty-nine studies and two clinical trials met the inclusion criteria. Most of them focused on cervical neoplasia (n = 17, 58.6%) and ovarian cancer (n = 7, 24.1%) detection, followed by assessment of the fallopian tubes (n = 2, 6.9%), endometrium (n = 1, 3.4%), and vulvar skin (n = 2, 6.9%). Using final pathology as the gold standard, overall specificity ranged from 30 to 99%, and overall sensitivity from 75 to 100%, with particularly high sensitivity for cervical lesions (79-100%) and ovarian cancer (81-100%). Among the included studies, thirteen (44.8%) used data interpretation algorithms, of which eleven (84.6%) applied machine learning, one (7.7%) deep learning, and one (7.7%) combined both. Conclusions: Spectral imaging, supported by computational methods, has shown promising results in the diagnostic evaluation of gynecologic disease by providing functional and molecular information beyond the capacities of standard visual assessment.
