Colorectal cancer (CRC) is a global public health concern, ranking among the most commonly diagnosed malignancies worldwide. Despite advancements in treatment modalities, the specter of CRC recurrence remains a significant challenge, demanding innovative solutions for early detection and intervention. The integration of machine learning into oncology offers a promising avenue to address this issue, providing data-driven insights and personalized care. This retrospective study analyzed data from 396 patients who underwent surgical procedures for colon cancer (CC) between 2010 and 2021. Machine learning algorithms were employed to predict CC recurrence, with a focus on demographic, clinicopathological, and laboratory characteristics. A range of evaluation metrics, including AUC (Area Under the Receiver Operating Characteristic), accuracy, recall, precision, and F1 scores, assessed the performance of machine learning algorithms. Significant risk factors for CC recurrence were identified, including sex, carcinoembryonic antigen (CEA) levels, tumor location, depth, lymphatic and venous invasion, and lymph node involvement. The CatBoost Classifier demonstrated exceptional performance, achieving an AUC of 0.92 and an accuracy of 88 % on the test dataset. Feature importance analysis highlighted the significance of CEA levels, albumin levels, N stage, weight, platelet count, height, neutrophil count, lymphocyte count, and gender in determining recurrence risk. The integration of machine learning into healthcare, exemplified by this study's findings, offers a pathway to personalized patient risk stratification and enhanced clinical decision-making. Early identification of individuals at risk of CC recurrence holds the potential for more effective therapeutic interventions and improved patient outcomes. Machine learning has the potential to revolutionize our approach to CC recurrence prediction, emphasizing the synergy between medical expertise and cutting-edge technology in the fight against cancer. This study represents a vital step toward precision medicine in CC management, showcasing the transformative power of data-driven insights in oncology.

中文翻译：

---

机器学习预测结肠癌复发


结直肠癌（CRC）是一个全球性的公共卫生问题，是全球最常诊断的恶性肿瘤之一。尽管治疗方式取得了进步，但结直肠癌复发的幽灵仍然是一个重大挑战，需要创新的解决方案来进行早期检测和干预。将机器学习集成到肿瘤学中，为解决这一问题提供了一条有前途的途径，提供数据驱动的见解和个性化护理。这项回顾性研究分析了 2010 年至 2021 年间接受结肠癌 (CC) 手术的 396 名患者的数据。采用机器学习算法来预测 CC 复发，重点关注人口统计学、临床病理学和实验室特征。一系列评估指标，包括 AUC（接收器操作特征下的面积）、准确性、召回率、精确度和 F1 分数，评估了机器学习算法的性能。确定了 CC 复发的重要危险因素，包括性别、癌胚抗原 (CEA) 水平、肿瘤位置、深度、淋巴和静脉侵犯以及淋巴结受累。 CatBoost 分类器表现出了卓越的性能，在测试数据集上实现了 0.92 的 AUC 和 88% 的准确率。特征重要性分析强调了 CEA 水平、白蛋白水平、N 分期、体重、血小板计数、身高、中性粒细胞计数、淋巴细胞计数和性别在确定复发风险中的重要性。本研究的结果证明了机器学习与医疗保健的整合，为个性化患者风险分层和增强临床决策提供了一条途径。 早期识别有 CC 复发风险的个体有可能实现更有效的治疗干预并改善患者的预后。机器学习有潜力彻底改变我们的 CC 复发预测方法，强调医学专业知识和尖端技术在抗击癌症方面的协同作用。这项研究代表了 CC 管理中精准医学的重要一步，展示了肿瘤学中数据驱动见解的变革力量。