利用术前 CT 放射组学预测肝内胆管癌 (ICC) 患者的三级淋巴结构 (TLS) 状态和无复发生存期 (RFS)。总共纳入 116 名 ICC 患者（训练：86 例；外部验证：30 例）。增强 CT 图像用于放射组学模型。对临床模型应用逻辑回归分析。组合模型以临床和影像组学模型为基础，共提取107个影像组学特征，经过淘汰和筛选后，组合6个特征，建立用于TLSs预测的影像组学模型。将动脉期弥漫性高增强与AJCC第8期相结合构建临床模型。组合（放射组学列线图）模型在训练队列中优于独立放射组学模型和临床模型（AUC，分别为 0.85 vs. 0.82 和 0.75），并在外部验证队列中得到验证（AUC，0.88 vs. 0.86 和 0.71，分别）。 rad评分不低于-0.76（低风险）组的患者表现出明显优于低于-0.76（高风险）组的患者的RFS（p < 0.001，C指数 = 0.678）。列线图评分不低于-1.16（低风险）组的患者的RFS显着优于低于-1.16（高风险）组的患者（p < 0.001，C指数 = 0.723）。CT放射组学列线图可以显示作为肿瘤内 TLS 状态的术前生物标志物，优于独立的放射组学或临床模型；术前 CT 放射组学列线图对 ICC 患者的 RFS 实现了准确的分层，优于术后病理 TLS 状态。放射组学列线图在预测 TLS 方面优于独立放射组学或临床模型，且预后分层优于 ICC 患者术后病理 TLS 状态。可能有助于识别从手术和随后的免疫治疗中获益最多的患者。• 组合（放射组学列线图）模型由放射组学模型和临床模型（动脉期弥漫性过度增强和 AJCC 第 8 期）组成。 • 放射组学列线图在预测 ICC 患者的 TLS 方面表现出比独立放射组学或临床模型更好的性能。 • 术前 CT 放射组学列线图比术后病理 TLS 状态对 ICC 患者的 RFS 进行了更准确的分层。© 2023。欧洲放射学会 (ESR)。

To predict the tertiary lymphoid structures (TLSs) status and recurrence-free survival (RFS) of intrahepatic cholangiocarcinoma (ICC) patients using preoperative CT radiomics.A total of 116 ICC patients were included (training: 86; external validation: 30). The enhanced CT images were performed for the radiomics model. The logistic regression analysis was applied for the clinical model. The combined model was based on the clinical and radiomics models.A total of 107 radiomics features were extracted, and after being eliminated and selected, six features were combined to establish a radiomics model for TLSs prediction. Arterial phase diffuse hyperenhancement and AJCC 8th stage were combined to construct a clinical model. The combined (radiomics nomogram) model outperformed both the independent radiomics model and clinical model in the training cohort (AUC, 0.85 vs. 0.82 and 0.75, respectively) and was validated in the external validation cohort (AUC, 0.88 vs. 0.86 and 0.71, respectively). Patients in the rad-score no less than -0.76 (low-risk) group showed significantly better RFS than those in the less than -0.76 (high-risk) group (p < 0.001, C-index = 0.678). Patients in the nomogram score no less than -1.16 (low-risk) group showed significantly better RFS than those of the less than -1.16 (high-risk) group (p < 0.001, C-index = 0.723).CT radiomics nomogram could serve as a preoperative biomarker of intra-tumoral TLSs status, better than independent radiomics or clinical models; preoperative CT radiomics nomogram achieved accurate stratification for RFS of ICC patients, better than the postoperative pathologic TLSs status.The radiomics nomogram showed better performance in predicting TLSs than independent radiomics or clinical models and better prognosis stratification than postoperative pathologic TLSs status in ICC patients, which may facilitate identifying patients benefiting most from surgery and subsequent immunotherapy.• The combined (radiomics nomogram) model consisted of the radiomics model and clinical model (arterial phase diffuse hyperenhancement and AJCC 8th stage). • The radiomics nomogram showed better performance in predicting TLSs than independent radiomics or clinical models in ICC patients. • Preoperative CT radiomics nomogram achieved more accurate stratification for RFS of ICC patients than the postoperative pathologic TLSs status.© 2023. European Society of Radiology (ESR).