肝内胆管癌手术中行充分淋巴结切除术患者淋巴结转移的可能性:一项回顾性多中心研究。

IF 9.1 1区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY
Liver Cancer Pub Date : 2024-10-16 eCollection Date: 2025-06-01 DOI:10.1159/000541646
Carlo Sposito, Alessandro Cucchetti, Francesca Ratti, Laura Alaimo, Francesco Ardito, Stefano Di Sandro, Matteo Serenari, Giammauro Berardi, Marianna Maspero, Giuseppe Maria Ettorre, Matteo Cescon, Fabrizio Di Benedetto, Felice Giuliante, Andrea Ruzzenente, Giorgio Ercolani, Luca Aldrighetti, Vincenzo Mazzaferro
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引用次数: 0

摘要

淋巴结转移(淋巴结转移[LNM])是肝内胆管癌(ICC)手术后预后的主要决定因素之一。先前的研究调查了临床放射学特征与LNM概率之间的关系,包括淋巴结取样不足的患者。本研究的目的是建立一种模型,利用术前临床和放射学特征来预测接受充分淋巴结切除术的患者发生淋巴结转移的风险。方法:收集2000年至2023年间在意大利7个中心接受根治性手术并进行充分淋巴结切除术的ICC患者,并将其分为衍生组和验证组。在衍生队列中应用逻辑回归和优势分析来确定与LNM病理相关的变量。最终的系数是从衍生队列中具有最高c统计量的模型中导出的,其中包含的变量数量最少(简约)。然后在外部验证队列中对模型进行测试,并将线性预测因子分为四分位数,以产生四种风险类别。结果:共发现693例患者。术前CA 19-9、影像学临床可疑淋巴结、患者年龄、肿瘤负荷评分与LNM显著相关。这些因素被纳入模型(https://aicep.website/calculators/),在推导和验证队列中,c统计量分别为0.723 (95% CI: 0.680, 0.766)和0.771 (95% CI: 0.699, 0.842)。病理淋巴结阳性在各危险组中呈进行性增加(低危29.9%,中低危45.1%,中高危51.5%,高危87.3%);P = 0.001)。结论:建立了一种新的模型,结合术前CA 19-9、影像学临床可疑淋巴结、患者年龄和肿瘤负荷评分来预测术前LNM的风险。该模型显示出较高的准确性,并有潜力协助临床医生在谁是候选人的手术患者的管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Probability of Lymph Node Metastases in Patients Undergoing Adequate Lymphadenectomy during Surgery for Intrahepatic Cholangiocarcinoma: A Retrospective Multicenter Study.

Introduction: Nodal metastases (lymph node metastasis [LNM]) are one of the major determinants of prognosis following surgery for intrahepatic cholangiocarcinoma (ICC). Previous studies investigating the correlation between clinical-radiological features and the probability of LNM include patients undergoing inadequate nodal sampling. Aim of this study was to develop a model to predict the risk of LNM in patients undergoing adequate lymphadenectomy using preoperative clinical and radiological features.

Methods: Patients undergoing radical surgery for ICC with adequate lymphadenectomy at seven Italian Centers between 2000 and 2023 were collected and divided into a derivation and a validation cohort. Logistic regression and dominance analysis were applied in the derivation cohort to identify variables associated with LNM at pathology. The final coefficients were derived from the model having the highest c-statistic in the derivation cohort with the lowest number of variables included (parsimony). The model was then tested in the external validation cohort, and the linear predictor was divided into quartiles to generate four risk categories.

Results: A total of 693 patients were identified. Preoperative CA 19-9, clinically suspicious lymph nodes at radiology, patients' age, and tumor burden score were significantly associated with LNM. These factors were included in a model (https://aicep.website/calculators/) showing a c-statistic of 0.723 (95% CI: 0.680, 0.766) and 0.771 (95% CI: 0.699, 0.842) in the derivation and validation cohort, respectively. A progressive increase of pathological lymph node positivity across risk groups was observed (29.9% in low-risk, 45.1% in intermediate-low risk, 51.5% in intermediate-high risk, and 87.3% in high-risk patients; p = 0.001).

Conclusions: A novel model that combines preoperative CA 19-9, clinically suspicious lymph nodes at radiology, patients' age, and tumor burden score was developed to predict the risk of LNM before surgery. The model exhibited high accuracy and has the potential to assist clinicians in the management of patients who are candidate to surgery.

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来源期刊
Liver Cancer
Liver Cancer Medicine-Oncology
CiteScore
20.80
自引率
7.20%
发文量
53
审稿时长
16 weeks
期刊介绍: Liver Cancer is a journal that serves the international community of researchers and clinicians by providing a platform for research results related to the causes, mechanisms, and therapy of liver cancer. It focuses on molecular carcinogenesis, prevention, surveillance, diagnosis, and treatment, including molecular targeted therapy. The journal publishes clinical and translational research in the field of liver cancer in both humans and experimental models. It publishes original and review articles and has an Impact Factor of 13.8. The journal is indexed and abstracted in various platforms including PubMed, PubMed Central, Web of Science, Science Citation Index, Science Citation Index Expanded, Google Scholar, DOAJ, Chemical Abstracts Service, Scopus, Embase, Pathway Studio, and WorldCat.
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