Belkacem Acidi

Medical 3D imaging allows surgeons to analyse liver anatomy through reconstructed models, supporting planning and decision-making. As these reconstructions enter operating room, it remains unclear how best to integrate them into surgical workflows, and which display modalities are most effective. After mapping current practices through user research, we investigated four approaches to presenting 3D reconstructions in simulated open-liver surgery. Pairs of surgeons diagnose cases and proposed hepatectomy incision lines under four conditions: (1) 3D reconstructions on a 2D monitor, (2) Augmented Reality (AR) overlay on a 2D monitor, (3) AR overlay via head-mounted display (HMD) for the main surgeon and 2D monitor for the assistant, and (4) AR overlay via HMDs for both surgeons. Results showed that the HMD+HMD condition was preferred for supporting anatomical understanding and collaboration. While accuracy was unaffected, findings emphasise the importance of shared perspective for effective teamwork using AR.

Background: Single port (SP) robotic surgery is the latest innovation in robotic surgery. Published evidence is limited and few series are available for SP robotic hepatectomy. The aim of this study was to investigate early feasibility of SP robotic hepatectomy in Europe. Methods: Between February and June 2025, data on true SP and SP plus one robotic hepatectomies were systematically collected at three participating centres. The study focussed on the description of the cohort, feasibility, surgical outcome in terms of blood loss, postoperative complications according to Clavien–Dindo (C–D), specific posthepatectomy complications according to the International Study Group of Liver Surgery (ISGLS) definitions and length of hospital stay (LOS). Results: A total of 12 SP and SP plus one robotic hepatectomies were performed. The cohort comprises six females and six males with a median age of 62 (23.38–78.46) years and a body mass index of 25.9 (21.1–36.3) kg/m ² . The series included seven non-anatomical/subsegment hepatectomies, two left lateral sectionectomies, two right hepatectomies and one segmentectomy. The median length of incision was 3.5 (2.7–4.7) cm plus a Pfannenstiel incision for larger specimen retrieval in two cases. An additional port (10 mm) was used in six cases. No procedure was converted to open, the median blood loss was 100 (0–580) ml. The median LOS was 3.5 days (1–8). According to the C–D classification, complications were categorized as follows: two grade I, two grade II and one grade IIIa complication. None of these met the criteria for ISGLS posthepatectomy complications. Follow-up at 30 days was uneventful in all cases. Conclusion: SP robotic hepatectomy is feasible when performed by experienced hepatobiliary surgeons. Larger series are needed to clearly define safety and the role of SP robotic hepatectomy within the armamentarium of hepatobiliary surgery and support the concept of tailored robotic-assisted surgery (T-RAS).

Introduction: Renal cell carcinoma (RCC) develops metastatic disease in 30–50% of patients during their disease course, with approximately one quarter presenting with metastases at diagnosis. While the lungs, liver, bones, brain, and adrenal glands are the most frequent metastatic sites, duodenal involvement is exceptionally rare. This uncommon presentation poses diagnostic and therapeutic challenges, particularly regarding the role of surgical resection in the metastatic setting. Objective: We aim to evaluate the clinical presentation, management strategies, and outcomes of patients with duodenal metastasis from RCC, with particular emphasis on the potential role of surgery, through a systematic review of the literature. Methods: A comprehensive electronic search of Medline, Embase, and Scopus was conducted according to PRISMA guidelines. The following MeSH terms were applied: Kidney Neoplasms [MeSH] AND Duodenal Neoplasms/metastasis [MeSH]. Eligible studies included original reports or case series describing RCC with duodenal metastasis. Demographic, clinical, surgical, and survival data were extracted and synthesized. Results: Of 89 records identified, 83 underwent full-text review and 51 met inclusion criteria, representing 55 patients. The median age at diagnosis was 64 years, and 80% of primary tumors arose from the right kidney. Nearly all patients (98%) were symptomatic, most commonly with upper gastrointestinal bleeding, anemia, or obstructive features. Pancreaticoduodenectomy was the predominant surgical approach, performed with curative intent in selected cases. Patients undergoing surgery achieved a 5-year overall survival of 70%, compared with 0% among non-operated patients. Conclusions: Duodenal metastasis from RCC remains an uncommon entity, limiting the strength of available evidence. Nevertheless, our findings suggest that surgical management—when feasible and decided within a multidisciplinary framework—can provide meaningful survival benefit and should be considered as a complement to contemporary systemic therapies for metastatic RCC

(1) Background: Somatostatin receptor 2 (SSTR2), a G protein-coupled receptor, is overexpressed in multiple malignancies, including hepatocellular carcinoma (HCC). While SSTR2 has traditionally been viewed as an inhibitory receptor involved in suppressing hormone secretion and cell proliferation, emerging evidence suggests a more complex role in cancer biology. However, the functional implications of SSTR2 expression in HCC remain poorly understood. This study aimed to systematically investigate the molecular landscape associated with SSTR2 expression in HCC and evaluate its potential as a therapeutic target. (2) Methods: SSTR2 expression patterns across 22 tumor types were assessed using TNMplot, and its expression in HCC was further validated through The Human Protein Atlas. Integrative analysis of transcriptomic profiles, protein expression data, and somatic copy number alterations was performed using data from The Cancer Genome Atlas (TCGA) to stratify HCC patients by SSTR2 expression levels. Gene Ontology (GO) enrichment analysis was conducted via SRplot to uncover biological processes and signaling pathways associated with SSTR2. Kaplan–Meier survival analyses were performed using GEO datasets to determine the prognostic significance of SSTR2 expression. (3) Results: SSTR2 is moderately expressed in the majority of HCC tumors. Elevated SSTR2 expression correlates with significantly poorer overall and disease-specific survival. High SSTR2 levels are associated with activation of oncogenic signaling cascades related to cell proliferation, epithelial-to-mesenchymal transition (EMT), angiogenesis, and metastasis. Additionally, SSTR2 expression is positively correlated with several receptor tyrosine kinases and oncogenes implicated in HCC progression. (4) Conclusions: Our findings suggest that SSTR2 is not merely a passive biomarker but may contribute to HCC pathogenesis through modulation of oncogenic pathways. These data support the rationale for further development of SSTR2-directed therapeutic strategies to inhibit tumor growth and invasion in HCC patients.

Nesrine Mekhenane, Clement Cormi, Arnaud Allemang--Trivalle, Belkacem Acidi, Daniel Cherqui, Eric Vibert, Marc-Antoine Allard. Ann Hepatobiliary Pancreat Surg -0001;0:. https://doi.org/10.14701/ahbps.25-089

Objective: The clinical aim of this work is to predict intraoperative LRC from preoperative CT scans only. Summary of Background Data: Liver resection (LR) is the most prevalent curative treatment for primary liver cancer, yet overall mortality/morbidity rates remain elevated. The conventional definition and classification of LR complexity (LRC) lack inclusion of the disease-induced 3D anatomical surgery complexity. Methods: 3D models of the organ, tumors and blood vessels were generated from Deep Learning models trained on patients CT scans. The surgeons’ expertise on which anatomical factors lead to LRC was translated into a new anatomical frame of reference around the Hepatic Central Zone (HCZ). A fully automatic pipeline to generate the HCZ and quantify the tumors position relative to it was assessed. An AI model was then trained to predict LRC from a patient cohort for whom LRC was annotated at the end of each surgery. The AI-prediction was finally compared to prediction of surgeons that only saw the patient preoperative CT scan. Results: The 3D reconstructions are successfully evaluated on benchmark datasets. The HCZ is accurately generated for a variety of atypical vascular anatomies (dice score 82±4.6%). The automatic pipeline is successfully run on a 145 HCC patient cohort. The predicted LRC outperforms the surgeons’ individual and combined anticipated complexities (accuracy and AUC scores: 79.4±3.4% and 85.1±3.2% respectively). Conclusion: This automatic digital tool accurately predicts intraoperative LRC and paves the way for an innovative oncology surgery planning. This tool could help orient patients towards appropriate medical centers depending on the predicted LRC level.