Background: Staging systems are essential for guiding treatment and predicting outcomes in cancer patients. For pancreatic neuroendocrine tumors, the American Joint Committee on Cancer (AJCC) Tumor, Lymph Node, and Metastasis (TNM) system is the current standard. However, its predictive accuracy is limited, as survival curves often overlap, particularly between Stage I and Stage II. Improved methods of patient stratification are therefore needed.Methods: We applied the Ensemble Algorithm for Clustering Cancer Data (EACCD) that involves calculating dissimilarities, ensemble learning, and hierarchical clustering. Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute. Models were developed with AJCC TNM variables (T, N, M) and expanded by including patient age.Results: The AJCC TNM system achieved a C-index of 0.6656 (95% CI: 0.6473–0.6839), with survival curves showing poor separation. In contrast, the EACCD model using TNM variables produced four prognostic groups with refined and clear separation, yielding a comparable C-index of 0.6685 (95% CI: 0.6518–0.6852). When age was added, EACCD identified five groups with even stronger stratification and a higher C-index of 0.7015 (95% CI: 0.6852–0.7178).Conclusions: EACCD provides a refined prognostic framework for pancreatic neuroendocrine tumors, outperforming the AJCC TNM system by offering clearer survival stratification, comparable or higher C-index values, and integration of additional clinical factors.
背景：分期系统对于指导癌症患者治疗和预测预后至关重要。对于胰腺神经内分泌肿瘤，美国癌症联合委员会（AJCC）的肿瘤-淋巴结-转移（TNM）分期系统是当前标准。然而，其预测准确性有限，生存曲线常出现重叠，尤其在I期与II期之间。因此需要改进患者分层方法。 方法：我们应用了癌症数据聚类集成算法（EACCD），该算法包含相异性计算、集成学习和层次聚类步骤。数据来源于美国国家癌症研究所的监测、流行病学和最终结果（SEER）数据库。模型首先基于AJCC TNM变量（T、N、M）构建，随后通过纳入患者年龄进行扩展。 结果：AJCC TNM系统的C指数为0.6656（95% CI：0.6473–0.6839），其生存曲线区分度欠佳。相比之下，使用TNM变量的EACCD模型产生了四个预后组别，呈现出精细且清晰的区分，获得可比的C指数0.6685（95% CI：0.6518–0.6852）。当加入年龄变量后，EACCD识别出五个具有更强分层能力的组别，C指数提升至0.7015（95% CI：0.6852–0.7178）。 结论：EACCD为胰腺神经内分泌肿瘤提供了更精细的预后评估框架，相较于AJCC TNM系统具有更清晰的生存分层、相当或更高的C指数值，并能整合其他临床因素，展现出更优的预后预测性能。
Using Machine Learning to Revise the AJCC Staging System for Neuroendocrine Tumors of the Pancreas
肿瘤(癌症)患者之家