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磁共振成像列线图用于术前评估肝细胞癌微血管侵犯及预后的研究。

A Nomogram of Magnetic Resonance Imaging for Preoperative Assessment of Microvascular Invasion and Prognosis of Hepatocellular Carcinoma.

机构信息

First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China.

Department of Radiology, First Hospital of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China.

出版信息

Dig Dis Sci. 2023 Dec;68(12):4521-4535. doi: 10.1007/s10620-023-08022-z. Epub 2023 Oct 4.

Abstract

BACKGROUND

Microvascular invasion (MVI) is a predictor of recurrence and overall survival in hepatocellular carcinoma (HCC), the preoperative diagnosis of MVI through noninvasive methods play an important role in clinical treatment.

AIMS

To investigate the effectiveness of radiomics features in evaluating MVI in HCC before surgery.

METHODS

We included 190 patients who had undergone contrast-enhanced MRI and curative resection for HCC between September 2015 and November 2021 from two independent institutions. In the training cohort of 117 patients, MVI-related radiomics models based on multiple sequences and multiple regions from MRI were constructed. An independent cohort of 73 patients was used to validate the proposed models. A final Clinical-Imaging-Radiomics nomogram for preoperatively predicting MVI in HCC patients was generated. Recurrence-free survival was analyzed using the log-rank test.

RESULTS

For tumor-extracted features, the performance of signatures in fat-suppressed T1-weighted images and hepatobiliary phase was superior to that of other sequences in a single-sequence model. The radiomics signatures demonstrated better discriminatory ability than that of the Clinical-Imaging model for MVI. The nomogram incorporating clinical, imaging and radiomics signature showed excellent predictive ability and achieved well-fitted calibration curves, outperforming both the Radiomics and Clinical-Radiomics models in the training and validation cohorts.

CONCLUSIONS

The Clinical-Imaging-Radiomics nomogram model of multiple regions and multiple sequences based on serum alpha-fetoprotein, three MRI characteristics, and 12 radiomics signatures achieved good performance for predicting MVI in HCC patients, which may help clinicians select optimal treatment strategies to improve subsequent clinical outcomes.

摘要

背景

微血管侵犯(MVI)是肝细胞癌(HCC)复发和总生存的预测因子,通过非侵入性方法术前诊断 MVI 在临床治疗中具有重要作用。

目的

探讨影像组学特征在术前评估 HCC 中 MVI 的有效性。

方法

我们纳入了 2015 年 9 月至 2021 年 11 月期间来自两个独立机构的 190 名接受过增强 MRI 和根治性切除术治疗 HCC 的患者。在 117 名患者的训练队列中,构建了基于 MRI 多序列和多区域的与 MVI 相关的影像组学模型。使用 73 名患者的独立队列验证所提出的模型。生成用于预测 HCC 患者术前 MVI 的临床-影像-影像组学列线图。使用对数秩检验分析无复发生存率。

结果

对于肿瘤提取特征,在单序列模型中,脂肪抑制 T1 加权图像和肝胆期特征的特征性能优于其他序列。影像组学特征在 MVI 方面的鉴别能力优于临床-影像模型。纳入临床、影像和影像组学特征的列线图模型在训练和验证队列中均表现出优异的预测能力,实现了拟合良好的校准曲线,优于影像组学和临床-影像组学模型。

结论

基于血清甲胎蛋白、三种 MRI 特征和 12 个影像组学特征的多区域和多序列的临床-影像-影像组学列线图模型在预测 HCC 患者的 MVI 方面表现出良好的性能,这可能有助于临床医生选择最佳治疗策略,以改善后续临床结果。

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