Edoardo Bindi

Background: Portal venous gas (PVG) is a recognized radiologic finding in necrotizing enterocolitis (NEC), but its value as a prognostic marker and indicator for surgical intervention remains controversial. Methods: This retrospective study analyzed data from infants with Bell stage II/III NEC admitted to “G. Salesi” Children’s Hospital NICU between 2013 and 2023. Patients were grouped by presence or absence of PVG on abdominal X-rays at diagnosis. Results: Among 57 infants, 25 (44%) had PVG. Gestational age and birth weight were comparable between groups. Bell stage III NEC was more frequent in PVG infants (64% vs 34%, P = 0.026). Surgery was performed in 56% of cases, more often in PVG patients (72% vs 44%, P = 0.033), who also had longer bowel resections (P = 0.039). Mortality was higher in the PVG group (32% vs 13%), with a significant difference in non-operated infants (43% vs 6%, P = 0.022). Postoperative short bowel syndrome was more frequent with PVG (22% vs 7%). Multivariate analysis identified Bell stage III NEC, not PVG, as significantly associated with surgery and mortality (P = 0.001). Conclusion: PVG in NEC is associated with more severe disease, higher surgical rates, and extensive intestinal resection.

As robotic surgery expands beyond the da Vinci platform, the relevance of new-generation systems to pediatric patients remains uncertain. This review examined the technical characteristics, applications, and perioperative outcomes of alternative multiport robotic platforms in children. PubMed/MEDLINE, Web of Science, Scopus, and the Cochrane Library were searched through 28 February 2026 in accordance with PRISMA 2020. Owing to clinical heterogeneity, findings were synthesized narratively, with Wilson 95% confidence intervals for key binary outcomes. Six studies reported 166 patients across 27 procedure types. Senhance accounted for 164 patients, while Hugo RAS and Hinotori were each represented by one patient. Ages ranged from 15 days to 17 years and weights from 3.8 to more than 100 kg. Senhance was the only platform used with 3-mm robotic instruments. Conversion or planned escalation occurred in 19 patients (11.4%; 95% CI, 7.5–17.2%). Four intraoperative complications were reported (2.4%; 95% CI, 0.9–6.0%). Across all reports, 20 patients experienced postoperative complications (12.0%; 95% CI, 7.9–17.9%). In the largest cohort, seven patients required reintervention (4.6%; 95% CI, 2.2–9.2%) and seven were readmitted (4.6%; 95% CI, 2.2–9.2%). No deaths or comparative pediatric studies were reported. Published experience remains sparse and is almost entirely limited to Senhance. Current evidence describes early clinical use without establishing comparative safety, effectiveness, or platform superiority. Prospective multicenter studies with standardized reporting are needed.

Pediatric surgery continues to incorporate minimally invasive techniques, image-guided procedures, artificial intelligence (AI), digital pathology, interventional radiology, and structured perioperative care

Introduction: Mediastinal tumors in children are rare and heterogeneous, and their surgical management is challenging due to limited working space and proximity to critical structures. While thoracoscopy has expanded, technical limitations persist in complex mediastinal dissections. Robot-assisted surgery may address these limitations, but its role in pediatric mediastinal tumor resection is not well defined. This systematic review aims to synthesize the available evidence on robot-assisted resection of mediastinal tumors in children, focusing on patient selection, technical feasibility, perioperative outcomes, and available oncologic results. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines and registered in PROSPERO (CRD420251245437). PubMed/MEDLINE, Web of Science, Scopus, and the Cochrane Library were searched up to 1 December 2025. Eligible studies included pediatric patients (0–18 years) undergoing robot-assisted resection of mediastinal tumors. Study selection, data extraction, and risk of bias assessment were performed independently by two reviewers. Due to heterogeneity and lack of comparative data, a qualitative narrative synthesis was undertaken. Results: Fifteen studies were included, comprising retrospective case series and case reports, including 215 children, 149 (69.3%) of whom were derived from a single retrospective study. The most frequent indications were localized neurogenic tumors, predominantly in the posterior mediastinum. Robot-assisted surgery was consistently performed with curative intent. The da Vinci platform was used in all cases, with reproducible port-placement strategies adapted to tumor location and patient size. Conversion to open surgery occurred in 11 (5.1%) cases, mainly due to bleeding or limited exposure, with no emergency conversions reported. Reported perioperative complications were uncommon, although complication reporting was heterogeneous across studies. No perioperative deaths were reported. Two disease-related deaths occurred during follow-up in patients with recurrent Ewing sarcoma. Long-term oncologic outcomes could not be adequately assessed because of heterogeneous reporting and limited follow-up. Conclusions: Robot-assisted resection of mediastinal tumors in children appears feasible in carefully selected cases treated at experienced centers. Current evidence mainly supports its use for localized tumors with favorable anatomy, particularly in the posterior mediastinum, while long-term oncologic adequacy remains insufficiently defined.

Background: Digital pathology (DP) combined with fluorescence confocal microscopy (FCM) allows rapid tissue assessment while preserving specimens. Artificial intelligence (AI) and large language models (LLMs) may enhance diagnostic workflows, but their role in pediatric surgical pathology is largely unexplored. Methods: We conducted a prospective, single-center study including 20 pediatric surgical cases with ex vivo FCM images acquired intraoperatively. Two commercially available LLMs, GPT-4V (AnPathology-Gpt) and Claude 3.7 Sonnet (AnPathology Project), were tested using structured prompts to generate diagnostic reports with and without immunohistochemistry (IHC) data, when available. Outputs were compared against the gold standard diagnosis by an experienced pediatric pathologist. Diagnostic performance was evaluated through accuracy, sensitivity, specificity, and Cohen’s kappa. A paired sub-analysis was performed for cases with IHC (n = 5), and a sensitivity analysis excluding IHC cases (n = 15) was conducted. Results: Across all 20 cases, AnPathology-Gpt achieved 85% accuracy, 100% sensitivity, 86% specificity, and κ = 0.78, while AnPathology Project reached 80% accuracy, 100% sensitivity, 57% specificity, and κ = 0.63. Both models correctly identified all 13 neoplastic cases, with errors limited to non-neoplastic lesions mimicking tumors. In the IHC sub-analysis, accuracy improved from 40% to 80% and sensitivity from 50% to 100% for both models, resolving two false negatives observed in the FCM-only evaluation. Sensitivity analysis excluding IHC confirmed consistency of the results. Conclusions: This pilot study demonstrates that multimodal LLMs can support accurate and rapid diagnosis in pediatric digital pathology. The addition of IHC improves performance in diagnostically complex cases. Larger multicenter studies are needed to validate these findings and to define the role of AI-assisted workflows in pediatric surgical pathology.