基于超声融合成像的术中消融特异性特征能否用于预测微波消融后肝细胞癌早期复发:一项概念验证研究

IF 4.2 3区 医学 Q2 ONCOLOGY
Journal of Hepatocellular Carcinoma Pub Date : 2025-05-12 eCollection Date: 2025-01-01 DOI:10.2147/JHC.S512926
Haiyu Kang, Zhong Liu, Bin Huang, Shuang Liang, Kai Yang, Huahui Liu, Minhua Lu, Ronghua Yan, Xin Chen, Erjiao Xu
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引用次数: 0

摘要

目的:术中因素是影响肝癌微波消融(MWA)术后早期复发的关键因素,但目前基于术中数据预测MWA术后肝癌复发的模型较少。量化术中与MWA相关的因素,建立基于超声造影(CEUS)融合成像预测HCC消融后早期复发的人工智能(AI)模型。患者和方法:回顾性分析79例hCC患者,均行MWA,随访1年,术中超声造影融合成像评估。我们开发了三种分类器(支持向量机(SVM)、随机森林(RF)和多层感知器(MLP))来预测CEUS融合图像中的早期HCC复发。使用最小冗余最大相关性(mRMR)定义和筛选13个消融特异性特征,并采用留一交叉验证(LOOCV)进行性能评估。对比分析分类器之间、高级介入医生与最佳分类器之间的受者工作特征曲线下面积(AUC)。结果:纳入的79例符合条件的患者中,早期复发22例(年龄60.18±10.97;男性20例),非早期复发57例(年龄58.81±10.89;50岁男性)。mRMR筛选出6个特征用于早期复发预测,3个模型的AUC分别为0.84 (95% CI: 0.74, 0.94) 0.79 (95% CI: 0.69, 0.89)和0.77 (95% CI: 0.67, 0.88) (SVM和RF分别为p = 0.20和0.23),显著优于高级医生评估(AUC, 0.56;95% ci: 0.44, 0.68;MLP = 0.002)。结论:基于消融特异性特征的术中超声融合影像数据预测模型预测MWA术后早期HCC复发是可行的,在指导术中实时调整消融策略以实现精准消融方面具有很大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Can Intra-Operative Ablation-Specific Features Based on Ultrasound Fusion Imaging be Used to Predict Early Recurrence of Hepatocellular Carcinoma After Microwave Ablation: A Proof-of-Concept Study.

Purpose: Intra-operative factors are crucial to early recurrence of hepatocellular carcinoma (HCC) after microwave ablation (MWA), but few models have been developed based on intra-operative data to predict HCC recurrence after MWA. To quantify the intra-operative factors associated with MWA and establish an artificial intelligence (AI) model for predicting early recurrence of HCC after ablation based on contrast-enhanced ultrasound (CEUS) fusion imaging.

Patients and methods: 79 hCC patients, who underwent MWA with one-year follow-up and intraoperative CEUS fusion imaging assessment were retrospectively included. Three classifiers (support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP)) were developed to predict early HCC recurrence from CEUS fusion images. Thirteen ablation-specific features were defined and screened using minimum redundancy maximum relevance (mRMR), and leave-one-out cross-validation (LOOCV) was adopted for performance evaluation. Comparative analyses were conducted among classifiers and between a senior interventional doctor and the best classifier in terms of the area under the receiver operating characteristic curve (AUC).

Results: Of 79 eligible patients who were included, 22 were in the early-recurrence (age 60.18 ± 10.97; 20 males) and 57 were in the non-early recurrence (age 58.81 ± 10.89; 50 males). Six features were selected out by mRMR for early recurrence prediction and AUCs of three models were 0.84 (95% CI: 0.74, 0.94) 0.79 (95% CI: 0.69, 0.89) and 0.77 (95% CI: 0.67, 0.88) (p = 0.20 and 0.23 for SVM and RF, respectively), which was significantly better than that achieved by senior doctor's assessment (AUC, 0.56; 95% CI: 0.44, 0.68; p = 0.002 for MLP).

Conclusion: The prediction model based on ablation-specific features using intra-operative ultrasound fusion imaging data was feasible to predict early recurrence of HCC after MWA and showed great potential in guiding the real-time adjustment of the intra-operative ablation strategy so as to achieve precise ablation.

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来源期刊
CiteScore
0.50
自引率
2.40%
发文量
108
审稿时长
16 weeks
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