Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
Department of Surgery, University of Verona, Verona, Italy.
J Gastrointest Surg. 2021 May;25(5):1156-1163. doi: 10.1007/s11605-020-04720-5. Epub 2020 Jul 14.
The objective of the current study was to develop a model to predict the likelihood of occult lymph node metastasis (LNM) prior to resection of intrahepatic cholangiocarcinoma (ICC).
Patients who underwent hepatectomy for ICC between 2000 and 2017 were identified using a multi-institutional database. A novel model incorporating clinical and preoperative imaging data was developed to predict LNM.
Among 980 patients who underwent resection of ICC, 190 (19.4%) individuals had at least one LNM identified on final pathology. An enhanced imaging model incorporating clinical and imaging data was developed to predict LNM ( https://k-sahara.shinyapps.io/ICC_imaging/ ). The performance of the enhanced imaging model was very good in the training data set (c-index 0.702), as well as the validation data set with bootstrapping resamples (c-index 0.701) and outperformed the preoperative imaging alone (c-index 0.660). The novel model predicted both 5-year overall survival (OS) (low risk 48.4% vs. high risk 18.4%) and 5-year disease-specific survival (DSS) (low risk 51.9% vs. high risk 25.2%, both p < 0.001). When applied among Nx patients, 5-year OS and DSS of low-risk Nx patients was comparable with that of N0 patients, while high-risk Nx patients had similar outcomes to N1 patients (p > 0.05).
This tool may represent an opportunity to stratify prognosis of Nx patients and can help inform clinical decision-making prior to resection of ICC.
本研究旨在建立一个模型,以预测肝内胆管癌(ICC)切除术前隐匿性淋巴结转移(LNM)的可能性。
使用多机构数据库确定了 2000 年至 2017 年间接受 ICC 肝切除术的患者。建立了一个新的模型,该模型结合了临床和术前影像学数据,以预测 LNM。
在 980 例接受 ICC 切除术的患者中,190 例(19.4%)患者的最终病理检查至少有一个 LNM。建立了一个增强的影像学模型,该模型结合了临床和影像学数据,以预测 LNM(https://k-sahara.shinyapps.io/ICC_imaging/)。该增强的影像学模型在训练数据集中的性能非常好(c 指数为 0.702),在包含 bootstrap 重采样的验证数据集中的性能也很好(c 指数为 0.701),并且优于单独的术前影像学(c 指数为 0.660)。该新模型预测了 5 年总生存率(OS)(低危组 48.4% vs. 高危组 18.4%)和 5 年疾病特异性生存率(DSS)(低危组 51.9% vs. 高危组 25.2%,均 p<0.001)。在 Nx 患者中应用时,低危组 Nx 患者的 5 年 OS 和 DSS 与 N0 患者相当,而高危组 Nx 患者的结局与 N1 患者相似(p>0.05)。
该工具可能为 Nx 患者的预后分层提供机会,并有助于在 ICC 切除术前做出临床决策。