Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan.
Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
J Gastroenterol. 2023 Jun;58(6):586-597. doi: 10.1007/s00535-023-01987-8. Epub 2023 Apr 26.
Pancreatic neuroendocrine neoplasms (PanNENs) are a heterogeneous group of tumors. Although the prognosis of resected PanNENs is generally considered to be good, a relatively high recurrence rate has been reported. Given the scarcity of large-scale reports about PanNEN recurrence due to their rarity, we aimed to identify the predictors for recurrence in patients with resected PanNENs to improve prognosis.
We established a multicenter database of 573 patients with PanNENs, who underwent resection between January 1987 and July 2020 at 22 Japanese centers, mainly in the Kyushu region. We evaluated the clinical characteristics of 371 patients with localized non-functioning pancreatic neuroendocrine tumors (G1/G2). We also constructed a machine learning-based prediction model to analyze the important features to determine recurrence.
Fifty-two patients experienced recurrence (14.0%) during the follow-up period, with the median time of recurrence being 33.7 months. The random survival forest (RSF) model showed better predictive performance than the Cox proportional hazards regression model in terms of the Harrell's C-index (0.841 vs. 0.820). The Ki-67 index, residual tumor, WHO grade, tumor size, and lymph node metastasis were the top five predictors in the RSF model; tumor size above 20 mm was the watershed with increased recurrence probability, whereas the 5-year disease-free survival rate decreased linearly as the Ki-67 index increased.
Our study revealed the characteristics of resected PanNENs in real-world clinical practice. Machine learning techniques can be powerful analytical tools that provide new insights into the relationship between the Ki-67 index or tumor size and recurrence.
胰腺神经内分泌肿瘤（PanNENs）是一组异质性肿瘤。尽管切除后的 PanNENs 预后通常被认为良好，但据报道其复发率相对较高。由于 PanNENs 罕见，因此关于其复发的大规模报告较少，我们旨在确定切除后 PanNENs 患者复发的预测因素，以改善预后。
我们建立了一个由 573 例 PanNENs 患者组成的多中心数据库，这些患者于 1987 年 1 月至 2020 年 7 月在日本 22 个中心（主要在九州地区）接受了手术。我们评估了 371 例局限性无功能胰腺神经内分泌肿瘤（G1/G2）患者的临床特征。我们还构建了一个基于机器学习的预测模型，以分析确定复发的重要特征。
在随访期间，52 例患者（14.0%）出现复发，复发的中位时间为 33.7 个月。随机生存森林（RSF）模型在 Harrell's C 指数（0.841 比 0.820）方面表现出比 Cox 比例风险回归模型更好的预测性能。RSF 模型中的前五个预测因子为 Ki-67 指数、残余肿瘤、WHO 分级、肿瘤大小和淋巴结转移；肿瘤大小超过 20mm 是复发概率增加的分水岭，而随着 Ki-67 指数的增加，5 年无病生存率呈线性下降。
我们的研究揭示了真实世界临床实践中切除的 PanNENs 的特征。机器学习技术可以成为强大的分析工具，为 Ki-67 指数或肿瘤大小与复发之间的关系提供新的见解。
