[1]万翠霞,陈湘光,杨志企,等.基于增强CT影像组学模型和临床特征模型评估进展期胃癌浆膜侵犯[J].中国医学物理学杂志,2023,40(12):1518-1522.[doi:DOI:10.3969/j.issn.1005-202X.2023.12.010]
　WAN Cuixia,CHEN Xiangguang,et al.Development and validation of models to predict serosal invasion in advanced gastric cancer using the enhanced CT imaging-based radiomics features and clinical features[J].Chinese Journal of Medical Physics,2023,40(12):1518-1522.[doi:DOI:10.3969/j.issn.1005-202X.2023.12.010]
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基于增强CT影像组学模型和临床特征模型评估进展期胃癌浆膜侵犯()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第12期
页码:
1518-1522
栏目:
医学影像物理
出版日期:
2023-12-27

文章信息/Info

Title:
Development and validation of models to predict serosal invasion in advanced gastric cancer using the enhanced CT imaging-based radiomics features and clinical features
文章编号:
1005-202X（2023）12-1518-05
作者:
万翠霞1; 3; 陈湘光3; 杨志企3; 董婷4; 张胜3; 江桂华2; 4
1.广东医科大学梅州临床医学院， 广东 梅州 514031； 2.广东医科大学，广东 湛江 524023； 3.梅州市人民医院（梅州市医学科学院）放射科， 广东 梅州 514031； 4.广东省第二人民医院影像科， 广东 广州 510317
Author(s):
WAN Cuixia1;  3;  CHEN Xiangguang3;  YANG Zhiqi3;  DONG Ting4;  ZHANG Sheng3;  JIANG Guihua2;  4
1. Meizhou Clinical Medical College of Guangdong Medical University, Meizhou 514031, China 2.Guangdong Medical University, Zhanjiang 524023, China 3. Department of Radiology, Meizhou Peoples Hospital (Meizhou Academy of Medical Sciences), Meizhou 514031, China 4. Department of Radiology, Guangdong Second Provincial General Hospital, Guangzhou 510317, China
关键词:
胃癌; 浆膜侵犯; 临床病理特征：影像组学特征; CT
Keywords:
Keywords: gastric cancer serosal invasion clinicopathological feature radiomics feature CT
分类号:
R735.2；R816.5
DOI:
DOI:10.3969/j.issn.1005-202X.2023.12.010
文献标志码:
A
摘要:
目的：探讨基于增强CT影像组学模型和临床特征模型评估进展期胃癌浆膜侵犯的价值。方法：收集351例术前2周内行腹部增强CT检查进展期胃癌患者资料并以7:3比例随机分为训练组247例和验证组104例。基于动静脉期CT图像在A.K软件中共提取3 190个影像组学特征，通过降维筛选后建立影像组学模型，比较进展期胃癌浆膜侵犯阳性和阴性组之间的临床特征差异，并构建临床模型。模型效能评估采用受试者工作特征曲线分析。结果：在训练组和验证组中，N、M分期在浆膜侵犯阳性组和阴性组间的差异有统计意义（P<0.05）。基于动静脉期图像最终筛选出14个影像组学特征。在验证组中，影像组学模型预测进展期胃癌浆膜侵犯的诊断效能高于基于联合N分期和M分期构建临床模型的诊断效能（AUC：0.854 vs 0.793）。结论：基于增强CT影像组模型和基于N、M分期的临床模型均能够成功预测进展期胃癌浆膜侵犯，前者诊断效能较优。
Abstract:
Abstract: Objective To explore the predictive value of the enhanced CT imaging-based radiomics model and the clinical model for the serosal invasion in advanced gastric cancer. Methods The data were collected from 351 patients with advanced gastric cancer who underwent abdominal enhanced CT examination within 2 weeks before surgery, and the patients were randomly divided into a training group (n=247) and a validation group (n=104) in a ratio of 7:3. The 3 190 radiomics features which were extracted from the arterial and venous phase CT images using A.K software were dimensionally reduced for constructing a radiomics model. The pathological features between serosal invasion positive and negative groups were compared, and the significant features were used to establish a clinical model. The models performance was evaluated using receiver operating characteristic curve. Results In the training and validation groups, N staging and M staging were different in serosal invasion positive and negative groups (P<0.05). A total of 14 radiomic features were ultimately selected from the arterial and venous phase images. In the validation group, the diagnostic efficacy of the radiomic model for predicting serosal invasion in advanced gastric cancer was higher than that of the clinical model based on the combination of N staging and M staging (AUC: 0.854 vs 0.793). Conclusion Both the radiomics model based on the enhanced CT imaging and the clinical model based on the combination of N staging and M staging can successfully predict serosal invasion in advanced gastric cancer, but the former performs better.

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备注/Memo

备注/Memo:
【收稿日期】2023-08-14 【基金项目】广州市重大脑疾病分子功能影像与人工智能重点实验室项目（202201020373）；梅州市社会发展科技计划项目（2022B17）；梅州市人民医院培育项目（PY-C2022051） 【作者简介】万翠霞，主治医师，研究方向：影像医学与核医学，E-mail: 641874861@qq.com 【通信作者】江桂华，主任医师，博士生导师，研究方向：分子影像学、脑功能磁共振成像，E-mail: jianggh@gd2h.org.cn
更新日期/Last Update: 2023-12-27