Background: The advantages of 3D reconstruction, immersive virtual reality (VR) and 3D printing in abdominal surgery have been enunciated for many years, but still today their application in routine clinical practice is almost nil. We investigate their feasibility, user appreciation and clinical impact. Material and methods: Fifteen patients undergoing pancreatic, hepatic or renal surgery were studied realizing a 3D reconstruction of target anatomy. Then, an immersive VR environment was developed to import 3D models, and some details of the 3D scene were printed. All the phases of our workflow employed open-source software and low-cost hardware, easily implementable by other surgical services. A qualitative evaluation of the three approaches was performed by 20 surgeons, who filled in a specific questionnaire regarding a clinical case for each organ considered. Results: Preoperative surgical planning and intraoperative guidance was feasible for all patients included in the study. The vast majority of surgeons interviewed scored their quality and usefulness as very good. Conclusions: Despite extra time, costs and efforts necessary to implement these systems, the benefits shown by the analysis of questionnaires recommend to invest more resources to train physicians to adopt these technologies routinely, even if further and larger studies are still mandatory.

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.