Preoperative planning and intraoperative real-time navigation with indocyanine green fluorescence in robotic liver surgery.

PURPOSE: We aimed at exploring indocyanine green (ICG) fluorescence wide spectrum of applications in hepatobiliary surgery as can result particularly useful in robotic liver resections (RLR) in order to overcome some technical limitations, increasing safety, and efficacy. METHODS: We describe our experience of 76 RLR performed between March 2020 and December 2022 exploring all the possible applications of pre- and intraoperative ICG administration. RESULTS: Hepatocellular carcinoma and colorectal liver metastases were the most common indications for RLR (34.2% and 26.7% of patients, respectively), and 51.3% of cases were complex resections with high IWATE difficulty scores. ICG was administered preoperatively in 61 patients (80.3%), intraoperatively in 42 patients (55.3%) and in both contexts in 25 patients (32.9%), with no observed adverse events. The most frequent ICG goal was to achieve tumor enhancement (59 patients, 77.6%), with a success rate of 94.9% and the detection of 3 additional malignant lesions. ICG facilitated evaluation of the resection margin for residual tumor and perfusion adequacy in 33.9% and 32.9% of cases, respectively, mandating a resection enlargement in 7.9% of patients. ICG fluorescence allowed the identification of the transection plane through negative staining in the 25% of cases. Vascular and biliary structures were visualized in 21.1% and 9.2% of patients, with a success rate of 81.3% and 85.7%, respectively. CONCLUSION: RLR can benefit from the routine integration of ICG fluoresce evaluation according to each individual patient and condition-specific goals and issues, allowing liver functional assessment, anatomical and vascular evaluation, tumor detection, and resection margins assessment.

Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases.

Colorectal cancer (CRC) is the third most common malignancy worldwide, with approximately 50% of patients developing colorectal cancer liver metastasis (CRLM) during the follow-up period. Management of CRLM is best achieved via a multidisciplinary approach and the diagnostic and therapeutic decision-making process is complex. In order to optimize patients' survival and quality of life, there are several unsolved challenges which must be overcome. These primarily include a timely diagnosis and the identification of reliable prognostic factors. Furthermore, to allow optimal treatment options, a precision-medicine, personalized approach is required. The widespread digitalization of healthcare generates a vast amount of data and together with accessible high-performance computing, artificial intelligence (AI) technologies can be applied. By increasing diagnostic accuracy, reducing timings and costs, the application of AI could help mitigate the current shortcomings in CRLM management. In this review we explore the available evidence of the possible role of AI in all phases of the CRLM natural history. Radiomics analysis and convolutional neural networks (CNN) which combine computed tomography (CT) images with clinical data have been developed to predict CRLM development in CRC patients. AI models have also proven themselves to perform similarly or better than expert radiologists in detecting CRLM on CT and magnetic resonance scans or identifying them from the noninvasive analysis of patients' exhaled air. The application of AI and machine learning (ML) in diagnosing CRLM has also been extended to histopathological examination in order to rapidly and accurately identify CRLM tissue and its different histopathological growth patterns. ML and CNN have shown good accuracy in predicting response to chemotherapy, early local tumor progression after ablation treatment, and patient survival after surgical treatment or chemotherapy. Despite the initial enthusiasm and the accumulating evidence, AI technologies' role in healthcare and CRLM management is not yet fully established. Its limitations mainly concern safety and the lack of regulation and ethical considerations. AI is unlikely to fully replace any human role but could be actively integrated to facilitate physicians in their everyday practice. Moving towards a personalized and evidence-based patient approach and management, further larger, prospective and rigorous studies evaluating AI technologies in patients at risk or affected by CRLM are needed.

Robotic central pancreatectomy: a systematic review and meta-analysis.

BACKGROUND: Central pancreatectomy is usually performed to excise lesions of the neck or proximal body of the pancreas. In the last decade, thanks to the advent of novel technologies, surgeons have started to perform this procedure robotically. This review aims to appraise the results and outcomes of robotic central pancreatectomies (RCP) through a systematic review and meta-analysis. METHODS: A systematic search of MEDLINE, Embase, and Web Of Science identified studies reporting outcomes of RCP. Pooled prevalence rates of postoperative complications and mortality were computed using random-effect modelling. RESULTS: Thirteen series involving 265 patients were included. In all cases but one, RCP was performed to excise benign or low-grade tumours. Clinically relevant post-operative pancreatic fistula (POPF) occurred in 42.3% of patients. While overall complications were reported in 57.5% of patients, only 9.4% had a Clavien-Dindo score ≥ III. Re-operation was necessary in 0.7% of the patients. New-onset diabetes occurred postoperatively in 0.3% of patients and negligible mortality and open conversion rates were observed. CONCLUSION: RCP is safe and associated with low perioperative mortality and well preserved postoperative pancreatic function, although burdened by high overall morbidity and POPF rates.