| 袁梦晨,刘译阳,李红亮,陈林,段博,赵帅,尤亚茹,陈星枝,高剑波.双能量CT影像组学联合临床及CT特征预测胃腺癌分化程度[J].中国医学影像技术,2024,40(10):1542~1547 |
| 双能量CT影像组学联合临床及CT特征预测胃腺癌分化程度 |
| Dual-energy CT radiomics combined with clinical and CT features for predicting differentiation degree of gastric adenocarcinoma |
| 投稿时间：2024-03-09 修订日期：2024-04-21 |
| DOI：10.13929/j.issn.1003-3289.2024.10.018 |
| 中文关键词: 胃肿瘤 影像组学 细胞分化 前瞻性研究 |
| 英文关键词:stomach neoplasms radiomics cell differentiation prospective studies |
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| 中文摘要: |
| 目的 观察双能量CT(DECT)联合临床及CT特征预测胃腺癌(GAC)分化程度的价值。方法 前瞻性分析254例GAC患者,根据病理结果将其分为高级别组(低分化GAC,n=88)与低级别组(中-高分化GAC,n=166),同时按8 ∶ 2比例分为训练集(n=203,含70例高级别、133例低级别GAC)及验证集(n=51,含18例高级别、33例低级别GAC)。基于静脉期单能级(40、70、100及140 keV)DECT提取及筛选影像组学特征并构建多能级影像组学模型预测GAC分级;以单因素及多因素logistic回归对训练集临床、CT特征及DECT参数进行分析并构建临床-CT模型,并与多能级影像组学模型构成联合模型。评估各模型预测效能,评价联合模型的校准度。结果 临床-CT、多能级影像组学及联合模型在训练集的曲线下面积(AUC)分别为0.74、0.75及0.78,在验证集分别为0.73、0.77及0.78。除联合模型在训练集的AUC高于临床-CT模型外(P＜0.05),各模型在各集的AUC差异均无统计学意义(P均＞0.05)。联合模型在训练集及验证集的校准度均良好(P均＞0.05)。结论 DECT影像组学联合临床及CT特征能有效预测GAC分化程度。 |
| 英文摘要: |
| Objective To observe the value of dual-energy CT (DECT) radiomics combined with clinical and CT features for predicting differentiation degree of gastric adenocarcinoma (GAC). Methods Totally 254 patients with GAC were prospectively analyzed and divided into high-grade group (low differentiation GAC, n=88) and low-grade group (middle-high differentiation GAC, n=166) according to pathological results. The patients were also divided into training set (n=203, including 70 high-grade and 133 low-grade GAC) and verification set (n=51, including 18 high-grade and 33 low-grade GAC) at the ratio of 8∶2. Radiomics features were extracted and screened based on venous phase single-level (40, 70, 100 and 140 keV) DECT, and a multi-energy radiomics model was constructed to predict GAC classification. Univariate analysis and multivariate logistic regression were used to analyze clinical and CT features as well as DECT parameters in training set to construct a clinic-CT model. Then a combined model was constructed through combining clinic-CT model with radiomics model. The predictive efficacy of the models were evaluated, and the calibration degree of the combined model was assessed. Results The area under the curve (AUC) of clinic-CT model, multi-energy radiomics model and combined model was 0.74, 0.75 and 0.78 in training set, and 0.73, 0.77 and 0.78 in verification set, respectively. Except for AUC of combined model was higher than that of clinic-CT model in training set (P＜0.05), no significant difference of AUC was found among models in training set nor verification set (all P＞0.05). The calibration degree of combined model was good in both training set and verification set (both P＞0.05). Conclusion DECT radiomics combined with clinical and CT features could effectively predict differentiation degree of GAC. |
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