[1]国亚新,满高才,龚秀茹,等.能谱CT和影像组学无创评估大鼠肝纤维化[J].中国医学物理学杂志,2024,41(12):1509-1516.[doi:DOI:10.3969/j.issn.1005-202X.2024.12.008]
　GUO Yaxin,MAN Gaocai,GONG Xiuru,et al.Non-invasive assessment of rat liver fibrosis using spectral CT and radiomics[J].Chinese Journal of Medical Physics,2024,41(12):1509-1516.[doi:DOI:10.3969/j.issn.1005-202X.2024.12.008]
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能谱CT和影像组学无创评估大鼠肝纤维化()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第12期
页码:
1509-1516
栏目:
医学影像物理
出版日期:
2024-12-17

文章信息/Info

Title:
Non-invasive assessment of rat liver fibrosis using spectral CT and radiomics
文章编号:
1005-202X（2024）12-1509-08
作者:
国亚新1; 满高才2; 龚秀茹3; 石齐4; 张闽光1
1.上海中医药大学附属市中医医院放射科， 上海 200071； 2.滨州医学院附属医院放射科， 山东 滨州 256603； 3.同济大学附属东方医院放射科， 上海 200120； 4.上海中医药大学附属市中医医院肿瘤科， 上海 200071
Author(s):
GUO Yaxin1;  MAN Gaocai2;  GONG Xiuru3;  SHI Qi4;  ZHANG Minguang1
1. Department of Radiology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200071, China 2. Department of Radiology, Binzhou Medical University Hospital, Binzhou 256603, China 3. Department of Radiology, Shanghai East Hospital, Tongji University, Shanghai 200120, China 4. Department of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200071, China
关键词:
肝纤维化; 大鼠模型; 能谱CT; 影像组学
Keywords:
Keywords: liver fibrosis rat model spectral CT radiomics
分类号:
R318；R811
DOI:
DOI:10.3969/j.issn.1005-202X.2024.12.008
文献标志码:
A
摘要:
目的：基于大鼠模型，探讨能谱CT定量参数联合影像组学对早期肝纤维化的预测价值。方法：前瞻性动物实验，构建肝纤维化大鼠干预模型，采集来自56只大鼠的112个能谱CT平扫样本，在能谱CT 图像中测量定量参数（40、60和100 keV CT值、能谱曲线斜率），评估各参数在肝纤维化分期中的价值；使用3D Slicer软件在60 keV单能图像中勾画感兴趣区，并提取影像组学特征，采用t检验、相关性分析和最小绝对收缩与选择算子算法进行影像组学特征筛选，得到影像组学评分（Rad-score）。根据病理学结果分为非显著肝纤维化与显著肝纤维化两个类别，使用筛选的能谱CT参数和影像组学特征构建3个Logistic回归模型，包括能谱CT模型、影像组学模型和联合模型。采用受试者工作特征曲线评价模型预测价值，绘制校准曲线评价模型拟合情况。结果：除100 keV CT值外，能谱CT参数和Rad-score在非显著肝纤维化与显著肝纤维化间差异均有统计学意义（P<0.05）。能谱CT模型、影像组学模型和联合模型在训练集中的AUC值分别为0.850、0.895和0.939，在测试集中分别为0.818、0.803和0.883；校准曲线显示3个模型拟合情况良好，无明显偏离。结论：基于能谱CT和影像组学构建的模型表现良好，具有进一步优化的价值。
Abstract:
Abstract: Objective To explore the predictive value of spectral CT quantitative parameters combined with radiomics for early liver fibrosis using rat models. Methods Prospective animal experiments were conducted, and the intervention models were constructed for liver fibrosis in rats. A total of 112 spectral CT plain samples were collected from 56 rats, and quantitative parameters (40, 60 and 100 keV CT values, slope of the spectral curve) were measured in the spectral CT images to assess the value of each parameter in the staging of liver fibrosis. The regions of interest were sketched out in the 60 keV monoenergetic images using 3D Slicer software, and from which the radiomics features were extracted. The t-test, correlation analysis and the least absolute contraction and selection operator algorithms were used for radiomics feature screening, and the Rad-score was calculated. According to the pathological results, they were classified into non-significant liver fibrosis and significant liver fibrosis, and 3 Logistic regression models (spectral CT model, radiomics model and combined model) were established using the selected spectral CT parameters and radiomics features. The predictive value of these models was evaluated using receiver operating characteristic curves, and calibration curves were plotted to evaluate model fit. Results Except for 100 keV CT value, spectral CT parameters and Rad-score differed statistically between non-significant and significant liver fibroses (P<0.05). The spectral CT model, radiomics model and combined model had AUC of 0.850, 0.895, 0.939 in the training set, and 0.818, 0.803, 0.883 in the test set. The calibration curves showed that the 3 models were well fitted, without significant deviation. Conclusion The model constructed with spectral CT and radiomics performes well, worthy of further optimization.

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[1]刘显旺,刘宏,邓靓娜,等.能谱CT基物质分离技术定量评估乙肝肝纤维化分期的价值[J].中国医学物理学杂志,2022,39(6):701.[doi:DOI:10.3969/j.issn.1005-202X.2022.06.008]
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备注/Memo

备注/Memo:
【收稿日期】2024-06-16 【基金项目】国家自然科学基金（81673743）；上海市自然科学基金（19ZR1452400）；上海市卫生健康委员会青年人才项目（K227567N） 【作者简介】国亚新，硕士研究生，研究方向：医学图像处理，E-mail: guoyaxin1025@163.com 【通信作者】张闽光，主任医师，博士生导师，研究方向：腹部影像学，E-mail: mgzhang09@163.com
更新日期/Last Update: 2024-12-20