[1]陈昭,张宇,周乐,等.基于钆赛酸二钠增强MRI的影像组学与深度迁移学习预测肝细胞癌术前微血管侵犯[J].中国医学物理学杂志,2025,42(10):1353-1360.[doi:DOI:10.3969/j.issn.1005-202X.2025.10.013]
　CHEN Zhao,ZHANG Yu,ZHOU Le,et al.Radiomics and deep transfer learning based on gadoxetic acid disodium-enhanced MRI for predicting preoperative microvascular invasion in hepatocellular carcinoma[J].Chinese Journal of Medical Physics,2025,42(10):1353-1360.[doi:DOI:10.3969/j.issn.1005-202X.2025.10.013]
点击复制

基于钆赛酸二钠增强MRI的影像组学与深度迁移学习预测肝细胞癌术前微血管侵犯()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第10期
页码:
1353-1360
栏目:
医学影像物理
出版日期:
2025-10-29

文章信息/Info

Title:
Radiomics and deep transfer learning based on gadoxetic acid disodium-enhanced MRI for predicting preoperative microvascular invasion in hepatocellular carcinoma
文章编号:
1005-202X（2025）10-1353-08
作者:
陈昭; 张宇; 周乐; 陈强; 苏华伟
青岛大学附属医院放射科， 山东 青岛 266555
Author(s):
CHEN Zhao;  ZHANG Yu;  ZHOU Le;  CHEN Qiang;  SU Huawei
Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266555, China
关键词:
肝细胞癌; 微血管侵犯; 影像组学; 深度迁移学习; 钆赛酸二钠
Keywords:
Keywords: hepatocellular carcinoma microvascular invasion radiomics deep transfer learning gadoxetic acid disodium
分类号:
R318；R816.5
DOI:
DOI:10.3969/j.issn.1005-202X.2025.10.013
文献标志码:
A
摘要:
目的：探讨基于钆赛酸二钠增强磁共振（MRI）的影像组学与深度迁移学习（DTL）在术前预测肝细胞癌（HCC）微血管侵犯（MVI）的价值。方法：回顾性分析青岛大学附属医院2019年1月至2024年9月间369例术后经病理证实MVI情况的HCC患者的MRI和临床病理资料。依据MVI阴性与阳性表现，将其划分为MVI-组219例和MVI+组150例。按照7:3比例随机分为训练组（n=258）和测试组（n=111）。基于肝胆期图像提取并筛选出影像组学、DTL以及两者融合特征中的最优特征。分别基于组学特征、DTL特征及二者融合特征利用随机森林、多层感知机、支持向量机3种算法构建9个机器学习模型。此外，利用受试者工作特征曲线对各模型的诊断效能进行全面评估，并确定最优模型作为输出模型。结果：构建的所有模型中融合特征模型效能总体高于单独特征模型，训练集随机森林分类器模型效能最高，AUC为0.998（95% CI: 0.996~1.000），作为本研究输出模型。结论：基于钆赛酸二钠增强MRI的影像组学与DTL模型可有效预测HCC的MVI，其中训练集融合特征的随机森林分类器模型效能最佳。
Abstract:
Abstract: Objective To explore the value of radiomics and deep transfer learning (DTL) based on gadoxetic acid disodium-enhanced magnetic resonance imaging (MRI) for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods A retrospective analysis was conducted using the MRI and clinicopathological data of 369 HCC patients who underwent surgery and had pathologically confirmed MVI at the Affiliated Hospital of Qingdao University from January 2019 to September 2024. According to the negative and positive manifestations of MVI, these patients were divided into MVI- group (n=219) and MVI+ group (n=150) and they were then randomly assigned into the training set (n=258) and the test set (n=111) in a ratio of 7:3. Based on the hepatobiliary phase images, the optimal features were extracted and screened from radiomics features, DTL features, and the fusion features of the two. Nine machine learning models were constructed using 3 algorithms (random forest, multi-layer perceptron, and support vector machine, separately) and trained on radiomics features, DTL features, and the fusion features of the two. The diagnostic efficacy of each model was evaluated using receiver operating characteristic curve, and the optimal model was identified as the output model. Results Among all the constructed models, those based on fused features outperformed models using individual features. The random forest classifier model in the training set had the best performance, with an AUC of 0.998 (95% CI: 0.996-1.000), and was therefore selected as the output model in this study. Conclusion Radiomics and DTL models based on gadoxetic acid disodium-enhanced MRI can effectively predict the MVI in HCC. Among these, the random forest classifier model utilizing fused features in the training set exhibits the best performance.

相似文献/References:

[1]韩柱君,巩贯忠,路玉昆,等. 原发性肝细胞癌的CT影像组学分析[J].中国医学物理学杂志,2018,35(12):1426.[doi:DOI:10.3969/j.issn.1005-202X.2018.12.011]
　HAN Zhujun,GONG Guanzhong,LU Yukun,et al. CT radiomic features of primary hepatocellular carcinoma[J].Chinese Journal of Medical Physics,2018,35(10):1426.[doi:DOI:10.3969/j.issn.1005-202X.2018.12.011]
[2]闫冰,吴爱东,张洪波,等. 肝细胞癌共面和非共面容积旋转调强放疗与螺旋断层放疗的剂量学研究[J].中国医学物理学杂志,2019,36(8):877.[doi:DOI:10.3969/j.issn.1005-202X.2019.08.003]
　YAN Bing,WU Aidong,ZHANG Hongbo,et al. Dosimetric study on coplanar and noncoplanar volumetric modulated arc therapy and helical tomotherapy for hepatocellular carcinoma[J].Chinese Journal of Medical Physics,2019,36(10):877.[doi:DOI:10.3969/j.issn.1005-202X.2019.08.003]
[3]刘海峰,许永生,刘钊,等.钆塞酸二钠增强磁共振和弥散加权成像诊断肝细胞癌TACE术后存活或复发病灶的价值[J].中国医学物理学杂志,2020,37(5):561.[doi:10.3969/j.issn.1005-202X.2020.05.006]
　LIU Haifeng,XU Yongsheng,LIU Zhao,et al.Diagnostic value of Gd-EOB-DTPA enhanced MRI and DWI in residual or recurrent lesionsafter TACE for hepatocellular carcinoma[J].Chinese Journal of Medical Physics,2020,37(10):561.[doi:10.3969/j.issn.1005-202X.2020.05.006]
[4]吴林耿,陈猛,苟庆,等.中性粒细胞/淋巴细胞比值对晚期肝细胞癌FOLFOX肝动脉灌注化疗近期疗效的影响[J].中国医学物理学杂志,2021,38(5):602.[doi:DOI:10.3969/j.issn.1005-202X.2021.05.015]
　WU Lingeng,CHEN Meng,et al.Effects of neutrophil to lymphocyte ratio on short-term efficacy of FOLFOX hepatic arterial infusion chemotherapy for advanced hepatocellular carcinoma[J].Chinese Journal of Medical Physics,2021,38(10):602.[doi:DOI:10.3969/j.issn.1005-202X.2021.05.015]
[5]魏佳赟,姚佳,李汛.细胞外基质刚度在肝细胞癌中的研究进展[J].中国医学物理学杂志,2021,38(6):770.[doi:DOI:10.3969/j.issn.1005-202X.2021.06.020]
　WEI Jiayun,YAO Jia,et al.Advances of research on extracellular matrix stiffness in hepatocellular carcinoma[J].Chinese Journal of Medical Physics,2021,38(10):770.[doi:DOI:10.3969/j.issn.1005-202X.2021.06.020]
[6]范义湘,易紫薇,胡煜麟,等.NIS基因转染对肝细胞癌NIS蛋白表达及摄碘功能的影响[J].中国医学物理学杂志,2021,38(10):1219.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.007]
　FAN Yixiang,YI Ziwei,HU Yulin,et al.Effects of NIS gene transfection on NIS protein expression and iodine uptake in hepatocellular carcinoma[J].Chinese Journal of Medical Physics,2021,38(10):1219.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.007]
[7]谢孟臻,吴斌,许立强,等.双层探测器光谱CT虚拟平扫中重建层厚对肝细胞癌显示的影响[J].中国医学物理学杂志,2022,39(12):1495.[doi:DOI:10.3969/j.issn.1005-202X.2022.12.007]
　XIE Mengzhen,WU Bin,XU Liqiang,et al.Effect of reconstructed slice thickness on the display of hepatocellular carcinoma in virtual non-contrast scan of dual-layer detector spectral CT[J].Chinese Journal of Medical Physics,2022,39(10):1495.[doi:DOI:10.3969/j.issn.1005-202X.2022.12.007]
[8]李申超,沈树成,赵汝彬,等.肝细胞癌常规与缩小射束夹角放疗计划的肝段剂量学比较[J].中国医学物理学杂志,2025,42(12):1556.[doi:DOI:10.3969/j.issn.1005-202X.2025.12.003]
　LI Shenchao,SHEN Shucheng,et al.Dosimetric comparison of hepatic segments between conventional and beam angle-reduced radiotherapy plans for hepatocellular carcinoma[J].Chinese Journal of Medical Physics,2025,42(10):1556.[doi:DOI:10.3969/j.issn.1005-202X.2025.12.003]
[9]刘杨军,刘淑珍,李鹏,等.基于MSCT特征构建肝细胞癌根治性切除术后复发的预测模型[J].中国医学物理学杂志,2026,43(2):261.[doi:DOI:10.3969/j.issn.1005-202X.2026.02.017]
　LIU Yangjun,LIU Shuzhen,LI Peng,et al.Construction of a predictive model for hepatocellular carcinoma recurrence after radical resection based on MSCT features[J].Chinese Journal of Medical Physics,2026,43(10):261.[doi:DOI:10.3969/j.issn.1005-202X.2026.02.017]
[10]黄倩,庄银平,徐鹏,等.基于多序列MRI和Swin Transformer深度学习模型的肝细胞癌微血管侵犯预测[J].中国医学物理学杂志,2026,43(2):245.[doi:DOI:10.3969/j.issn.1005-202X.2026.02.015]
　HUANG Qian,ZHUANG Yinping,XU Peng,et al.Predicting microvascular invasion in hepatocellular carcinoma with multi-sequence MRI and a Swin Transformer-based deep learning model[J].Chinese Journal of Medical Physics,2026,43(10):245.[doi:DOI:10.3969/j.issn.1005-202X.2026.02.015]

备注/Memo

备注/Memo:
【收稿日期】2025-04-02 【基金项目】山东省自然科学基金（ZR2023MH240） 【作者简介】陈昭，主管技师，研究方向：腹部磁共振，E-mail: chenzhao365@126.com 【通信作者】苏华伟，副主任技师，E-mail: su-huawei@126.com
更新日期/Last Update: 2025-10-29