| 詹鹏超,李莉明,吕东博,罗成龙,胡志伟,梁盼,高剑波.临床及CT影像组学特征预测胃癌微卫星高度不稳定状态[J].中国医学影像技术,2024,40(1):77~82 |
| 临床及CT影像组学特征预测胃癌微卫星高度不稳定状态 |
| Clinical and CT radiomics features for predicting microsatellite instability-high status of gastric cancer |
| 投稿时间：2023-09-06 修订日期：2023-10-24 |
| DOI：10.13929/j.issn.1003-3289.2024.01.015 |
| 中文关键词: 胃肿瘤 微卫星不稳定性 体层摄影术,X线计算机 影像组学 |
| 英文关键词:stomach neoplasms microsatellite instability tomography, X-ray computed radiomics |
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| 摘要点击次数: 288 |
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| 中文摘要: |
| 目的 观察临床和CT影像组学特征用于预测胃癌微卫星高度不稳定（MSI-H）状态的价值。 方法 纳入150例胃癌患者，MSI-H阳性30例、阴性120例；按7 ∶ 3比例将其分为训练集（n=105）和验证集（n=45）。基于腹部静脉期增强CT图提取病灶影像组学特征并加以筛选，计算影像组学评分（Radscore）；比较训练集和验证集MSI-H阳性与阴性患者临床资料及Radscore差异；分别基于其间差异有统计学意义的临床因素和Radscore构建临床模型、CT影像组学模型及临床-CT影像组学联合模型，评估其预测胃癌MSI-H状态的价值。结果 训练集和验证集中，MSI-H阳性与阴性肿瘤位置、Radscore差异均有统计学意义（P均<0.05）。临床模型、CT影像组学模型及联合模型评估训练集胃癌MSI-H状态的曲线下面积（AUC）分别0.760、0.799及0.864，在验证集分别为0.735、0.812及0.849；联合模型的AUC大于2种单一模型（P均<0.05）。结论 基于肿瘤位置和Radscore的临床-CT影像组学联合特征可有效预测胃癌MSI-H状态。 |
| 英文摘要: |
| Objective To observe the value of clinical and CT radiomics features for predicting microsatellite instability-high (MSI-H) status of gastric cancer. Methods Totally 150 gastric cancer patients including 30 cases of MSI-H positive and 120 cases of MSI-H negative were enrolled and divided into training set (n=105) or validation set (n=45) at the ratio of 7:3. Based on abdominal vein phase enhanced CT images, lesions radiomics features were extracted and screened, and radiomics scores (Radscore) was calculated. Clinical data and Radscores were compared between MSI-H positive and negative patients in training set and validation set. Based on clinical factors and Radscores being significant different between MSI-H positive and negative ones, clinical model, CT radiomics model and clinical-CT radiomics combination model were constructed, and their predictive value for MSI-H status of gastric cancer were observed. Results Significant differences of tumor location and Radscore were found between MSI-H positive and negative patients in both training and validation sets (all P<0.05). The area under the curve (AUC) of clinical model, CT radiomics model and combination model for evaluating MSI-H status of gastric cancer in training set was 0.760, 0.799 and 0.864, respectively, of that in validation set was 0.735, 0.812 and 0.849, respectively. AUC of clinical-CT radiomics combination model was greater than that of the other 2 single models (all P<0.05). Conclusion Clinical-CT radiomics combination model based on tumor location and Radscore could effectively predict MSI-H status of gastric cancer. |
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