Image-guided real-time optical biopsy in oncologic surgery: a systematic review of the role of confocal microscopy in the era of digital surgery.

Fiche publication

Date publication

août 2025

Journal

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology

Auteurs

Membres identifiés du Cancéropôle Est :
Pr MARESCAUX Jacques

Tous les auteurs :
Pavone M, Macellari N, Innocenzi C, Ciancia M, Sighinolfi MC, Nero C, Lecointre L, Moro F, Costantini B, Rocco B, Testa AC, Marescaux J, Seeliger B, Fanfani F, Fagotti A, Querleu D, Bizzarri N

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/40876359

Résumé

Confocal microscopy (CM) is rapidly emerging as a key imaging technology within the evolving paradigm of digital surgery, offering real-time, high-resolution visualization of tissue microarchitecture. Its diverse modalities-including confocal laser endomicroscopy (CLE), probe-based CLE (pCLE), fluorescence confocal microscopy (FCM), and reflectance confocal microscopy (RCM)-enable near-histological evaluation of tissue either directly in vivo or on freshly excised specimens ex vivo. This systematic review, conducted according to PRISMA guidelines and registered in PROSPERO (CRD420251033452), examined 33 clinical studies and 13 ongoing trials exploring intraoperative applications of CM in oncologic surgery. CM has been most commonly utilized in dermatologic oncology, followed by head and neck, neurosurgical, and breast cancers, with emerging applications in gastrointestinal, gynecologic, pediatric, and urologic fields. RCM was the most frequently employed modality, followed by CLE, pCLE, and FCM. Notably, a significant proportion of studies reported diagnostic sensitivity, specificity, and accuracy values exceeding 80 %, confirming CM's reliability in detecting malignancies and evaluating surgical margins. The majority of studies utilized CM intraoperatively, while a smaller subset applied it ex vivo or in both contexts. A growing number of reports describe the integration of CM with artificial intelligence (AI) for automated image analysis, a development that holds promise for standardizing interpretation and enhancing intraoperative decision-making. As part of a broader shift toward image-guided and digitally assisted surgery, CM may serve as a powerful tool to increase precision, reduce operative time, and improve oncologic outcomes. These findings underscore the need for further validation and adoption of CM as a complementary method to traditional histopathology in surgical oncology.