Introduction: Nutritional care is a cornerstone of cancer treatment, with the potential to significantly improve patient outcomes by addressing malnutrition and enhancing recovery. Despite growing awareness, the integration of evidence-based nutritional strategies into oncology remains inconsistent. Following a 2020 survey that highlighted critical gaps, the NutriOnc Research Group launched targeted initiatives to raise awareness and improve practices. This study reassesses progress in integrating nutritional care and identifies persistent challenges. Methods: A 29-question survey was conducted in 2024 among 73 early-career oncologists, surgeons, radiation oncologists, and nutrition specialists across Italy. Respondents provided insights into clinical nutrition program availability, multidisciplinary team composition, screening practices, and educational needs. Results: Encouraging advancements were noted, with 80.8% of respondents reporting the presence of clinical nutrition programs, compared to fewer structured efforts in 2020. However, only 26.0% included nutrition specialists in multidisciplinary teams, and barriers such as financial constraints, limited product availability, and the absence of trained professionals persisted. While 82.2% performed nutritional screening, variability in tools and practices highlighted the need for standardization. Strikingly, 98.6% expressed a strong demand for advanced education on clinical nutrition, emphasizing the need for innovative and accessible training programs. Conclusions: This study reveals a landscape of progress tempered by persistent inequities. To close the gap, oncology must embrace standardized protocols, expand access to trained nutrition specialists, and invest in educational initiatives. The findings offer actionable insights for transforming cancer care through nutrition, underscoring its potential to improve survival, quality of life, and overall treatment success.

Background: Artificial intelligence is emerging as a promising tool in surgical oncology, with growing evidence suggesting potential applications in diagnostic support, intraoperative guidance, and perioperative risk assessment. In gastric cancer surgery, emerging applications range from AI-assisted endoscopic detection to data-driven perioperative risk prediction, while some technological developments, particularly in robotic autonomy, derive from broader surgical or experimental models that may inform future gastric procedures. Methods: A narrative review was conducted following established methodological standards, including the Scale for the Assessment of Narrative Review Articles (SANRA) and the Search–Appraisal–Synthesis–Analysis (SALSA) framework. English-language studies indexed in PubMed, Scopus, Embase, and Web of Science up to October 2025 were included. Evidence was synthesized thematically across five domains: AI-assisted anatomical recognition and lymphadenectomy support, autonomous robotic systems, early cancer detection, perioperative predictive and frailty models, and ethical and regulatory considerations. Results: AI-based computer vision and deep learning algorithms have demonstrated promising capabilities for real-time anatomical recognition, surgical phase classification, and intraoperative guidance, although evidence of direct patient-level benefit remains limited. In diagnostic settings, AI-assisted endoscopy and Raman spectroscopy have been shown to improve early lesion detection and reduce dependence on operator experience. Predictive models, including MySurgeryRisk and AI-driven frailty assessments, may support individualized prehabilitation planning and perioperative risk stratification. Persistent limitations include small and heterogeneous datasets, insufficient external validation, and unresolved concerns related to data privacy, algorithmic interpretability, and medico-legal responsibility. Conclusions: Artificial intelligence is progressively emerging as a promising tool in gastric cancer surgery, integrating automation, advanced analytics, and human clinical reasoning. Its safe and ethical adoption requires robust validation, transparent governance, and continuous surgeon oversight. When developed within human-centered and ethically grounded frameworks, AI can augment, rather than replace, surgical expertise, potentially advancing precision, safety, and equity in oncologic care.

Background: Esophageal cancer, the seventh most common malignancy globally, requires esophagectomy for curative treatment. However, esophagectomy is associated with high postoperative morbidity and mortality, highlighting the need for minimally invasive approaches. Robotic-assisted surgery has emerged as a promising alternative to traditional open and minimally invasive esophagectomy (MIE), offering potential benefits in improving clinical and oncological outcomes. This review aims to assess the postoperative morbidity and outcomes of robotic surgery. Methods: A comprehensive review of the current literature was conducted, focusing on studies evaluating the role of robotic-assisted surgery in esophagectomy. Data were synthesized on the clinical outcomes, including postoperative complications, survival rates, and recovery time, as well as technological advancements in robotic surgery platforms. Studies comparing robotic-assisted esophagectomy with traditional approaches were analyzed to determine the potential advantages of robotic systems in improving surgical precision and patient outcomes. Results: Robotic-assisted esophagectomy (RAMIE) has shown significant improvements in clinical outcomes compared to open surgery and MIE, including reduced postoperative pain, less blood loss, and faster recovery. RAMIE offers enhanced thoracic access, with fewer complications than thoracotomy. The RACE technique has improved patient recovery and reduced morbidity. Fluorescence-guided technologies, including near-infrared fluorescence (NIRF), have proven valuable for sentinel node biopsy, lymphatic mapping, and angiography, helping identify critical structures and minimizing complications like anastomotic leakage and chylothorax. Despite these benefits, challenges such as the high cost of robotic systems and limited long-term data hinder broader adoption. Hybrid approaches, combining robotic and open techniques, remain common in clinical practice. Conclusions: Robotic-assisted esophagectomy offers promising advantages, including enhanced precision, reduced complications, and faster recovery, but challenges related to cost, accessibility, and evidence gaps must be addressed. The hybrid approach remains a valuable option in select clinical scenarios. Continued research, including large-scale randomized controlled trials, is necessary to further establish the role of robotic surgery as the standard treatment for resectable esophageal cancer.

Introduction: Robot-assisted minimally invasive gastrectomy (RAMIG) represents a significant advancement in the surgical management of gastric cancer, offering superior dexterity, enhanced visualization, and improved ergonomics compared to laparoscopic gastrectomy (LG). This review systematically evaluates the current evidence on perioperative outcomes, oncological efficacy, learning curves, and economic considerations, providing insights into RAMIG’s potential role in modern gastric cancer surgery. Methods: A thorough analysis of retrospective, prospective, and meta-analytic studies was conducted to compare RAMIG with LG. Key outcomes, including operative time, intraoperative blood loss, lymph node retrieval, postoperative complications, learning curve duration, and cost-effectiveness, were assessed. Emphasis was placed on both short-term and long-term oncological outcomes to determine the clinical value of RAMIG. Results: Evidence indicates that RAMIG is associated with reduced intraoperative blood loss, lower morbidity rates, and a shorter learning curve, with proficiency achieved after 11–25 cases compared to 40–60 cases for LG. The robotic platform’s articulated instruments and enhanced three-dimensional visualization enable more precise lymphadenectomy, particularly in complex anatomical regions. Despite these advantages, operative time remains longer, and costs remain higher due to system acquisition, maintenance, and consumable expenses. However, emerging data suggest a gradual narrowing of cost disparities. While short-term outcomes are favorable, further high-quality, multicenter studies are needed to validate long-term oncological efficacy and survival outcomes. Conclusion: RAMIG offers significant technical and clinical advantages over conventional LG, particularly in terms of precision and learning efficiency. However, the long-term oncological benefits and economic feasibility require further validation. Future research should focus on cost optimization, advanced technological integration such as near-infrared fluorescence and artificial intelligence, and multicenter trials to solidify RAMIG’s role as a standard approach for gastric cancer surgery.

Background: Chronological age remains deeply embedded in perioperative risk assessment because it is readily available and intuitively associated with adverse outcomes. In clinical practice, however, patients of similar age frequently experience markedly different postoperative trajectories, suggesting that physiological reserve may more accurately reflect vulnerability to surgical stress than years lived alone. We therefore investigated whether age-based stratification inadequately captures perioperative vulnerability when compared with functional phenotyping based on frailty status and baseline handgrip strength (HGS). Methods: We conducted a prospective multicenter observational cohort study including 223 adults undergoing elective abdominal surgery between January 2023 and June 2025. Chronological age was evaluated both continuously and using a conventional threshold ( 10 days, was used as a pragmatic clinical benchmark. Cross-classification analyses, logistic regression, and receiver operating characteristic (ROC) curve analyses were performed to compare the discriminatory performance of chronological age, frailty phenotype, and HGS. Results: Substantial discordance was observed between chronological age and frailty phenotype. Among patients younger than 70 years, 7.7% met criteria for frailty, whereas 58.0% of patients aged ≥70 years were classified as fit or pre-frail. Prolonged hospitalization occurred in 48 patients (21.5%) and varied markedly according to frailty status within each age group. Frail patients younger than 70 years demonstrated higher rates of prolonged LOS than fit older patients (40.0% vs. 10.5%). Chronological age demonstrated limited discrimination for prolonged hospitalization (AUC 0.579), while the ≥70-year threshold showed poor discriminatory performance (AUC 0.541). Frailty phenotype demonstrated improved discrimination (AUC 0.679), whereas the combined multivariable model integrating age, frailty, HGS, sex, and oncologic indication achieved good discriminatory performance (AUC 0.810). In multivariable analyses, frailty remained independently associated with prolonged LOS, whereas chronological age did not. Conclusions: Chronological age alone demonstrated limited discriminatory performance for perioperative risk stratification. Functional phenotyping based primarily on frailty status, complemented by objective functional measures such as HGS, may better capture physiological reserve and support more individualized, function-centered perioperative assessment in abdominal surgery.