Colorectal cancer, the third most common cancer worldwide, affects 40–45% of patients on the right side. Surgery, especially minimally invasive methods such as laparoscopic and robotic procedures, is the preferred treatment. However, these techniques present technical complications. The anatomical complexity and variations in vessel branching patterns pose challenges, particularly for less experienced surgeons. The CoDIG 2 is a nationwide observational study involving 76 specialized Italian general surgery departments focused on colorectal surgery. The centres were directed to maintain their standard surgical and clinical practices. The aim of this study was to analyse the intraoperative vascular anatomy of Italian patients who underwent laparoscopic right colectomy and explore the ligature techniques used by Italian surgeons. Surgeons reported information about vascularization of the right colon for 616 patients and about surgical anatomy of RCA for 368 patients. Fifty-three patients (10.8%) showed no RCA intraoperatively. The right colic artery (RCA) was categorized according to the Yada classification (types 1–4) during evaluation, and intraoperative assessments revealed that Yada type 1 was the most common type (55.2%), while radiologic evaluations revealed a higher prevalence of type 2. Furthermore, compared with the superior mesenteric vein (SMV), the RCA is more often located anteriorly according to intraoperative and contrast-enhanced CT examination; 59.9% were found in the anterior position during intraoperative examination, while 40.1% were found in the same position on preoperative contrast-enhanced CT. Vascularization of the right colon, including missing branches, additional branches, shared trunks, and retro-superior courses of the mesenteric vein, exhibited notable variations. To understand vascular variations, a preoperative radiological study is necessary; although there was no concordance between the intraoperative and radiological evaluations, this is a limitation of preinterventional radiological evaluation (PII) because it is always needed for oncological staging. This approach is especially critical for inexperienced surgeons to avoid potential complications, such as problematic bleeding.

The evolution of laparoscopic right hemicolectomy, particularly with complete mesocolic excision (CME) and central vascular ligation (CVL), represents a significant advancement in colon cancer surgery. The CoDIG 1 and CoDIG 2 studies highlighted Italy’s progressive approach, providing useful findings for optimizing patient outcomes and procedural efficiency. Within this context, accurately predicting postoperative length of stay (LoS) is crucial for improving resource allocation and patient care, yet its determination through machine learning techniques (MLTs) remains underexplored. This study aimed to harness MLTs to forecast the LoS for patients undergoing right hemicolectomy for colon cancer, using data from the CoDIG 1 (1224 patients) and CoDIG 2 (788 patients) studies. Multiple MLT algorithms, including random forest (RF) and support vector machine (SVM), were trained to predict LoS, with CoDIG 1 data used for internal validation and CoDIG 2 data for external validation. The RF algorithm showed a strong internal validation performance, achieving the best performances and a 0.92 ROC in predicting long-term stays (more than 5 days). External validation using the SVM model demonstrated 75% ROC values. Factors such as fast-track protocols, anastomosis, and drainage emerged as key predictors of LoS. Integrating MLTs into predicting postoperative LOS in colon cancer surgery offers a promising avenue for personalized patient care and improved surgical management. Using intraoperative features in the algorithm enables the profiling of a patient’s stay based on the planned intervention. This issue is important for tailoring postoperative care to individual patients and for hospitals to effectively plan and manage long-term stays for more critical procedures.