| 黄裕存,曹治,陆少范,黄胜福,邝宇良.基于CT增强图像纹理特征预测可切除胃癌患者预后[J].中国医学影像技术,2020,36(7):1046~1050 |
| 基于CT增强图像纹理特征预测可切除胃癌患者预后 |
| Prediction of prognosis of resectable gastric cancer patients based on texture features of enhanced CT images |
| 投稿时间：2020-01-20 修订日期：2020-03-16 |
| DOI：10.13929/j.issn.1003-3289.2020.07.025 |
| 中文关键词: 胃肿瘤 体层摄影术,X线计算机 纹理分析 预后 |
| 英文关键词:stomach neoplasms tomography, X-ray computed texture features prognosis |
| 基金项目:珠海市医疗卫生科技计划（20191210E030069）。 |
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| 中文摘要: |
| 目的 探讨基于术前CT增强图像纹理特征预测可切除胃癌患者预后的价值。方法 回顾性分析197例经手术病理确诊胃癌患者，随机分为训练组（n=147）和验证组（n=50）。于术前CT增强门静脉期图像中提取90个病灶三维定量特征，采用组间相关系数（ICC）从中选择可重复性好者，以LASSO COX回归模型进行降维并筛选出与患者总生存时间（OS）相关特征，建立影像标签，对2组患者进行分类，根据标签分数的中位数值分为高危组和低危组，观察组间OS差异，分析临床、病理特征及影像纹理特征标签与患者预后的关系。构建融合纹理特征标签和临床病理特征的诺莫图，评价其预测胃癌患者预后的效能；绘制决策曲线，评价其临床价值。结果 经筛选获得2个与患者OS相关的CT纹理特征并以之建立影像标签。训练组（χ2=9.25）和验证组（χ2=8.49）中，高危组和低危组患者OS差异均有统计学意义（P均<0.01）。影像标签及TNM分期为胃癌的独立危险因素。影像标签预测训练组和验证组患者3年OS的AUC分别为0.72（P=0.02）和0.67（P=0.07），融合影像标签和TNM分期的诺莫图模型预测3年OS的AUC分别为0.78和0.81（P均<0.01）。阈值为0.13~0.59时，诺莫图模型的净获益高于单独影像标签。结论 基于CT增强图像纹理特征建立的影像标签可用于胃癌患者术后危险分层；联合病理特征构建的纹理诺模图模型有助于预测患者预后。 |
| 英文摘要: |
| Objective To investigate the prediction value of prognosis of resectable gastric cancer patients based on texture features of preoperative enhanced CT images. Methods Data of 197 patients with gastric cancer confirmed by surgical pathology were retrospectively analyzed. The patients were randomly divided into training group (n=147) and validation group (n=50). A total of 90 3-dimensional quantitative features on portal venous phase images of preoperative enhanced CT were extracted of all patients, and intraclass correlation coefficient was used to select better repetitive features. LASSO COX regression analysis was used to reduce dimensionality and screen features related to patients' overall survival (OS). A image tag was built to classify patients in 2 groups. The patients were stratified into high-risk and low-risk groups according to the median of signature score, and the difference of OS was analyzed. A nomogram integrating image tag and pathological features was constructed after analyzing the relationship of clinical, pathological features or image texture labels and prognosis of gastric cancer patients, and the efficacy in predicting prognosis of gastric cancer patients was evaluated. Clinical decision curve was plotted to evaluate relative clinical value. Results The image tag was established with 2 OS-related CT features. Statistical differences of OS were found between high-risk and low-risk patients in both training group (χ2=9.25) and validation group (χ2=8.49, both P<0.01). The image tag and TNM staging were independent risk factors of gastric cancer. For patients in training group and validation group, AUC of image tag predicting 3-year OS was 0.72 (P=0.02) and 0.67 (P=0.07), of nomogram integrated image tag and TNM staging was 0.78 and 0.81, respectively (both P<0.01). The decision curve analysis showed that the nomogram model had higher net benefit than image tag alone with the threshold probabilities of 0.13-0.59. Conclusion Image labels based on texture features of enhanced CT image can be used for postoperative risk stratification of gastric cancer patients. Nomogram constructed with image tag combining pathological features can help to predict the prognosis of patient with resectable gastric cancer. |
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