This study aims to evaluate the feasibility of video-based assessment rate of Critical Views of Safety criteria for sentinel lymph node dissection in endometrial and cervical cancer. Goal of these Critical Views of Safety is to help standardize the evaluation of surgical quality, improve the precision of sentinel lymph node identification, and lead to better patient outcomes. This international multi-center observational prospective study was conducted from April to September 2024. Surgical videos from patients with cervical and endometrial carcinoma undergoing minimally invasive sentinel lymph node dissection were collected. A total of 3 Critical Views of Safety criteria (lateral pararectal space, lateral paravesical space, internal iliac artery) were proposed based on the anatomical structures defined as mandatory to be identified before sentinel node dissection, according to previously published expert consensus. A total of 3 independent surgeons, blinded to each other's assessments, evaluated whether the proposed criteria were identifiable in the endoscopic surgical video to establish applicability (content validity) and inter-rater agreement (reliability). A total of 80 patients were enrolled, of these, 71 cases (88.8%) had videos suitable for annotation, 64 (90.1%) underwent sentinel lymph node dissection for endometrial cancer, and 7 (9.9%) for cervical cancer; the median age was 52 years (IQR 34-71) and median body mass index was 28.8 kg/m² (IQR 23.7-32.17). The lateral pararectal space was identified in 62% of videos, the lateral paravesical space in 94%, and the internal iliac artery in the 32%. Inter-rater reliability was high for the lateral pararectal and paravesical spaces (Fleiss κ of 0.90) and moderate for the internal iliac artery (Fleiss κ of 0.73). The low assessment rate of the internal iliac artery criteria should raise concerns about missing sentinel lymph nodes in the internal iliac and pre-sacral area. The assessment of such standardized safety criteria could potentially standardize the procedures, thereby improving adherence to guidelines. The introduction of the video assessment of these criteria lays the foundation for exploring the feasibility of artificial intelligence algorithms to automatically assess and document the Critical Views of Safety in surgical videos.

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.

Volatile organic compounds profiling has emerged as a promising approach for cancer detection. Electronic noses are portable sensor-based devices capable of recognizing volatile organic compound patterns in various biological matrices, offering a rapid, non-invasive, and cost-effective diagnostic alternative. The aim of this systematic review was to evaluate the diagnostic performance of electronic noses in gynecologic oncology and to delineate their technical characteristics. This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and was registered in International Prospective Register of Systematic Reviews (CRD420251122293). PubMed, Scopus, and Google Scholar were searched up to August 2025. Eligible studies included prospective investigations evaluating volatile organic compound analysis through electronic noses in ovarian, cervical, endometrial, and vulvar cancers, as well as high-grade squamous intraepithelial lesions, using histopathology as the reference standard. Diagnostic performance parameters were extracted, and the risk of bias was assessed with Quality Assessment of Diagnostic Accuracy Studies 2. No meta-analysis was performed due to study heterogeneity. Fifteen studies with a total of 1224 patients were included. Among them, 562 (45.9%) had gynecologic malignancies, and 662 (54.1%) served as controls. Ten studies (66.7%) investigated ovarian cancer, 4 (26.7%) cervical cancer, and 1 (6.7%) high-grade squamous intraepithelial lesions of the cervix; no studies evaluated endometrial or vulvar cancers. Biological matrices analyzed included breath (33.3%), urine (20%), tissue (20%), plasma (6.7%), genitourinary secretions (6.7%), or combined samples (6.7%). Reported diagnostic performance ranged from 71% to 97.7% for sensitivity, from 63% to 100% for specificity, and from 71% to 95% for accuracy across all cancer types. In ovarian cancer studies, sensitivity ranged from 71% to 97.7%, specificity from 63% to 91.4%, and accuracy from 71% to 87%. In cervical cancer studies, sensitivity ranged from 88% to 93%, and specificity from 85% to 100%. Electronic nose technologies show encouraging diagnostic accuracy in gynecologic oncology, particularly for cervical cancer, whereas performance in ovarian cancer remains more variable depending on the biological matrix and comparator group. Despite promising results, the lack of standardized protocols and the heterogeneity of current evidence limit immediate clinical translation. Larger, multicenter, and standardized studies are needed to validate their integration into diagnostic workflows.

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.