磁共振成像列线图用于术前评估肝细胞癌微血管侵犯和预后,Digestive Diseases and Sciences

微血管侵犯（MVI）是肝细胞癌（HCC）复发和总生存的预测因子，术前通过无创方法诊断MVI在临床治疗中发挥着重要作用。

探讨放射组学特征在 HCC 术前评估 MVI 中的有效性。

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 名患者，他们接受了对比增强 MRI 和 HCC 根治性切除术。在 117 名患者的训练队列中，构建了基于 MRI 的多个序列和多个区域的 MVI 相关放射组学模型。使用由 73 名患者组成的独立队列来验证所提出的模型。生成了最终的临床-影像-放射组学列线图，用于术前预测 HCC 患者的 MVI。使用对数秩检验分析无复发生存率。

对于肿瘤提取特征，脂肪抑制 T1 加权图像和肝胆相中的特征表现优于单序列模型中的其他序列。放射组学特征表现出比 MVI 临床成像模型更好的辨别能力。结合临床、成像和放射组学特征的列线图显示出出色的预测能力，并实现了拟合良好的校准曲线，在训练和验证队列中优于放射组学和临床放射组学模型。

基于血清甲胎蛋白、3个MRI特征和12个放射组学特征的多区域、多序列的临床-影像-放射组学列线图模型在预测HCC患者的MVI方面取得了良好的性能，这可能有助于临床医生选择最佳的治疗策略以改善后续治疗临床结果。

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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.

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

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.

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.

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.