Surgically resected grade 1–2 (G1-2) pancreatic neuroendocrine tumors (PanNETs) exhibit diverse clinical outcomes, highlighting the need for reliable prognostic biomarkers. Our study aimed to develop and validate CT-based radiomics model for predicting postsurgical outcome in patients with G1-2 PanNETs, and to compare its performance with the current clinical staging system. This multicenter retrospective study included patients who underwent dynamic CT and subsequent curative resection for G1–2 PanNETs. A radiomics-based model (R-score) for predicting recurrence-free survival (RFS) was developed from a development set (441 patients from one institution) using least absolute shrinkage and selection operator-Cox regression analysis. A clinical model (C-model) consisting of age and tumor stage according to the 8th American Joint Committee on Cancer staging system was built, and an integrative model combining the C-model and the R-score (CR-model) was developed using multivariable Cox regression analysis. Using an external test set (159 patients from another institution), the models’ performance for predicting RFS and overall survival (OS) was evaluated using Harrell’s C-index. The incremental value of adding the R-score to the C-model was evaluated using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The median follow-up periods were 68.3 and 59.7 months in the development and test sets, respectively. In the development set, 58 patients (13.2%) experienced recurrence and 35 (7.9%) died. In the test set, tumors recurred in 14 patients (8.8%) and 12 (7.5%) died. In the test set, the R-score had a C-index of 0.716 for RFS and 0.674 for OS. Compared with the C-model, the CR-model showed higher C-index (RFS, 0.734 vs. 0.662, p = 0.012; OS, 0.781 vs. 0.675, p = 0.043). CR-model also showed improved classification (NRI, 0.330, p < 0.001) and discrimination (IDI, 0.071, p < 0.001) for prediction of 3-year RFS. Our CR-model outperformed the current clinical staging system in prediction of the prognosis for G1–2 PanNETs and added incremental value for predicting postoperative recurrence. The CR-model enables precise identification of high-risk patients, guiding personalized treatment planning to improve outcomes in surgically resected grade 1–2 PanNETs.

中文翻译：

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基于 CT 的放射组学对 1-2 级胰腺神经内分泌肿瘤的预后价值

手术切除的 1-2 级 (G1-2) 胰腺神经内分泌肿瘤 (PanNET) 表现出不同的临床结果，凸显了对可靠的预后生物标志物的需求。我们的研究旨在开发和验证基于 CT 的放射组学模型，用于预测 G1-2 PanNET 患者的术后结果，并将其性能与当前的临床分期系统进行比较。这项多中心回顾性研究包括接受动态 CT 和随后 G1-2 PanNET 根治性切除的患者。用于预测无复发生存期 (RFS) 的基于放射组学的模型 (R 评分) 是使用最小绝对收缩和选择算子 Cox 回归分析从开发集（来自一个机构的 441 名患者）开发出来的。根据第八届美国癌症联合委员会分期系统建立了由年龄和肿瘤分期组成的临床模型（C模型），并使用C模型和R评分相结合的综合模型（CR模型）开发了多变量Cox回归分析。使用外部测试集（来自另一个机构的 159 名患者），使用 Harrell 的 C 指数评估模型预测 RFS 和总生存期 (OS) 的性能。使用净重分类改进 (NRI) 和综合辨别改进 (IDI) 评估将 R 分数添加到 C 模型的增量值。开发组和测试组的中位随访时间分别为 68.3 个月和 59.7 个月。在开发组中，58 名患者 (13.2%) 出现复发，35 名患者 (7.9%) 死亡。在测试集中，14 名患者（8.8%）肿瘤复发，12 名患者（7.5%）死亡。在测试集中，RFS 的 R 得分 C 指数为 0.716，OS 的 C 指数为 0.674。与 C 模型相比，CR 模型显示出更高的 C 指数（RFS，0.734 vs. 0.662，p = 0.012；OS，0.781 vs. 0.675，p = 0.043）。CR 模型还显示出改进的 3 年 RFS 预测分类（NRI，0.330，p < 0.001）和区分（IDI，0.071，p < 0.001）。我们的 CR 模型在预测 G1-2 PanNET 的预后方面优于当前的临床分期系统，并为预测术后复发增加了增量价值。CR 模型能够精确识别高危患者，指导个性化治疗计划，以改善手术切除的 1-2 级 PanNET 的预后。