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临床列线图模型预测肝癌切除术前行微血管侵犯的术前预测。

Clinical Nomogram Model for Pre-Operative Prediction of Microvascular Invasion of Hepatocellular Carcinoma before Hepatectomy.

机构信息

Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan.

Department of General Surgery, E-Da Cancer Hospital, I-Shou University, Kaohsiung 824, Taiwan.

出版信息

Medicina (Kaunas). 2024 Aug 28;60(9):1410. doi: 10.3390/medicina60091410.

Abstract

: Microvascular invasion (MVI) significantly impacts recurrence and survival rates after liver resection in hepatocellular carcinoma (HCC). Pre-operative prediction of MVI is crucial in determining the treatment strategy. This study aims to develop a nomogram model to predict the probability of MVI based on clinical features in HCC patients. : A total of 489 patients with a pathological diagnosis of HCC were enrolled from our hospital. Those registered from 2012-2015 formed the derivation cohort, and those from 2016-2019 formed the validation cohort for pre-operative prediction of MVI. A nomogram model for prediction was created using a regression model, with risk factors derived from clinical and tumor-related features before surgery. : Using the nomogram model to predict the odds ratio of MVI before hepatectomy, the AFP, platelet count, GOT/GPT ratio, albumin-alkaline phosphatase ratio, ALBI score, and GNRI were identified as significant variables for predicting MVI. The Youden index scores for each risk variable were 0.287, 0.276, 0.196, 0.185, 0.115, and 0.112, respectively, for the AFP, platelet count, GOT/GPT ratio, AAR, ALBI, and GNRI. The maximum value of the total nomogram scores was 220. An increase in the number of nomogram points indicated a higher probability of MVI occurrence. The accuracy rates ranged from 55.9% to 64.4%, and precision rates ranged from 54.3% to 68.2%. Overall survival rates were 97.6%, 83.4%, and 73.9% for MVI(-) and 80.0%, 71.8%, and 41.2% for MVI(+) ( < 0.001). The prognostic effects of MVI(+) on tumor-free survival and overall survival were poor in both the derivation and validation cohorts. : Our nomogram model, which integrates clinical factors, showed reliable calibration for predicting MVI and provides a useful tool enabling surgeons to estimate the probability of MVI before resection. Consequently, surgical strategies and post-operative care programs can be adapted to improve the prognosis of HCC patients where possible.

摘要

微血管侵犯(MVI)显著影响肝癌(HCC)患者肝切除术后的复发率和生存率。术前预测 MVI 对于确定治疗策略至关重要。本研究旨在建立一种基于 HCC 患者临床特征预测 MVI 概率的列线图模型。

本研究共纳入 489 例经病理诊断为 HCC 的患者。其中,2012-2015 年登记的患者为推导队列,2016-2019 年登记的患者为验证队列,用于预测 MVI 的术前预测。使用回归模型创建预测 MVI 的列线图模型,使用术前临床和肿瘤相关特征的危险因素。

使用列线图模型预测肝切除术前 MVI 的优势比,AFP、血小板计数、GOT/GPT 比值、白蛋白-碱性磷酸酶比值、ALBI 评分和 GNRI 被确定为预测 MVI 的显著变量。每个风险变量的约登指数评分分别为 AFP 的 0.287、血小板计数的 0.276、GOT/GPT 比值的 0.196、AAR 的 0.185、ALBI 的 0.115 和 GNRI 的 0.112。总列线图评分的最大值为 220。列线图点数的增加表明 MVI 发生的概率更高。准确性从 55.9%到 64.4%不等,精密度从 54.3%到 68.2%不等。MVI(-)和 MVI(+)的总生存率分别为 97.6%、83.4%和 73.9%(<0.001)和 80.0%、71.8%和 41.2%。在推导和验证队列中,MVI(+)对无肿瘤生存和总生存的预后影响均较差。

我们的列线图模型,整合了临床因素,对预测 MVI 具有可靠的校准能力,并为外科医生提供了一种有用的工具,使他们能够在切除前估计 MVI 的概率。因此,可能的情况下,手术策略和术后护理方案可以进行调整,以改善 HCC 患者的预后。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6aa/11433876/1ecc7298c470/medicina-60-01410-g001.jpg

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