Background/Objectives: Pancreatic cancer is a very aggressive disease with a poor prognosis, even when diagnosed at an early stage. This study aimed to validate and refine a radiomic-based [18F]FDG-PET model to predict distant relapse-free survival (DRFS) in patients with unresectable locally advanced pancreatic cancer (LAPC).Methods: A Cox regression model incorporating two radiomic features (RFs) and cancer stage (III vs. IV) was temporally validated using a larger cohort (215 patients treated between 2005–2022). Patients received concurrent chemoradiotherapy with capecitabine and hypo-fractionated Intensity Modulated Radiotherapy (IMRT). Data were split into training (145 patients, 2005–2017) and validation (70 patients, 2017–2022) groups. Seventy-eight RFs were extracted, harmonized, and analyzed using machine learning to develop refined models.Results: The model incorporating Statistical-Percentile10, Morphological-ComShift, and stage demonstrated moderate predictive accuracy (training: C-index = 0.632; validation: C-index = 0.590). When simplified to include only Statistical-Percentile10, performance improved slightly in the validation group (C-index = 0.601). Adding GLSZM3D-grayLevelVariance to Statistical-Percentile10, while excluding Morphological-ComShift, further enhanced accuracy (training: C-index = 0.654; validation: C-index = 0.623). Despite these refinements, all versions showed similar moderate ability to stratify patients into risk classes.Conclusions: [18F]FDG-PET radiomic features are robust predictors of DRFS after chemoradiotherapy in LAPC. Despite moderate performance, these models hold promise for patient risk stratification. Further validation with external cohorts is ongoing.
**背景/目的：** 胰腺癌是一种侵袭性极强、预后不良的疾病，即使在早期诊断时也是如此。本研究旨在验证并优化一种基于影像组学的[18F]FDG-PET模型，以预测不可切除局部晚期胰腺癌（LAPC）患者的远处无复发生存期（DRFS）。 **方法：** 采用一个更大的队列（2005–2022年间治疗的215例患者）对包含两个影像组学特征（RFs）和癌症分期（III期 vs. IV期）的Cox回归模型进行了时间验证。患者接受了卡培他滨同步放化疗联合大分割调强放射治疗（IMRT）。数据被分为训练组（145例患者，2005–2017年）和验证组（70例患者，2017–2022年）。提取了78个RFs，进行数据协调后，利用机器学习进行分析以开发优化模型。 **结果：** 包含统计-百分位10、形态学-质心偏移和分期的模型显示出中等的预测准确性（训练组：C指数 = 0.632；验证组：C指数 = 0.590）。当简化为仅包含统计-百分位10时，验证组的性能略有提升（C指数 = 0.601）。在统计-百分位10的基础上加入GLSZM3D-灰度方差，同时排除形态学-质心偏移，进一步提高了准确性（训练组：C指数 = 0.654；验证组：C指数 = 0.623）。尽管进行了这些优化，所有版本的模型在将患者分层为不同风险类别方面均显示出相似的中等能力。 **结论：** [18F]FDG-PET影像组学特征是LAPC患者放化疗后DRFS的稳健预测因子。尽管性能中等，但这些模型在患者风险分层方面具有应用前景。目前正在使用外部队列进行进一步的验证。
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