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.
腹腔镜右半结肠切除术，特别是结合完整结肠系膜切除（CME）和中央血管结扎（CVL）技术的发展，代表了结肠癌手术领域的重大进步。CoDIG 1和CoDIG 2研究凸显了意大利在该领域的积极探索，为优化患者预后和手术效率提供了有价值的发现。在此背景下，准确预测术后住院时间对于改善资源分配和患者护理至关重要，然而利用机器学习技术进行预测的研究仍显不足。本研究旨在利用CoDIG 1（1224例患者）和CoDIG 2（788例患者）研究的数据，通过机器学习技术预测结肠癌右半结肠切除术患者的住院时间。研究采用包括随机森林和支持向量机在内的多种机器学习算法进行训练，其中CoDIG 1数据用于内部验证，CoDIG 2数据用于外部验证。随机森林算法在内部验证中表现出色，在预测长期住院（超过5天）时获得最佳性能，ROC曲线下面积达0.92。使用支持向量机模型进行的外部验证显示ROC值为75%。加速康复方案、吻合方式和引流等因素被确定为住院时间的关键预测因子。将机器学习技术整合到结肠癌手术术后住院时间预测中，为个性化患者护理和改善手术管理提供了新途径。在算法中纳入术中特征，能够根据计划的手术干预对患者住院时间进行预测分析。这对于制定个体化术后护理方案，以及医院有效规划和管理重大手术的长期住院安排具有重要意义。
肿瘤(癌症)患者之家