胃癌劳伦分型的术前预测：基于CT图像和临床特征的影像组学列线图分析
Preoperative prediction for lauren type of gastric cancer: A radiomics nomogram analysis based on CT images and clinical features.
作者信息
Sun Zongqiong, Jin Linfang, Zhang Shuai, Duan Shaofeng, Xing Wei, Hu Shudong
机构信息
Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China.
Department of Pathology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China.
出版信息
J Xray Sci Technol. 2021;29(4):675-686. doi: 10.3233/XST-210888.
PURPOSE
To investigate feasibility of predicting Lauren type of gastric cancer based on CT radiomics nomogram before operation.
MATERIALS AND METHODS
The clinical data and pre-treatment CT images of 300 gastric cancer patients with Lauren intestinal or diffuse type confirmed by postoperative pathology were retrospectively analyzed, who were randomly divided into training set and testing set with a ratio of 2:1. Clinical features were compared between the two Lauren types in the training set and testing set, respectively. Gastric tumors on CT images were manually segmented using ITK-SNAP software, and radiomic features of the segmented tumors were extracted, filtered and minimized using the least absolute shrinkage and selection operator (LASSO) regression to select optimal features and develop radiomics signature. A nomogram was constructed with radiomic features and clinical characteristics to predict Lauren type of gastric cancer. Clinical model, radiomics signature model, and the nomogram model were compared using the receiver operating characteristic (ROC) curve analysis with area under the curve (AUC). The calibration curve was used to test the agreement between prediction probability and actual clinical findings, and the decision curve was performed to assess the clinical usage of the nomogram model.
RESULTS
In clinical features, Lauren type of gastric cancer relate to age and CT-N stage of patients (all p < 0.05). Radiomics signature was developed with the retained 10 radiomic features. The nomogram was constructed with the 2 clinical features and radiomics signature. Among 3 prediction models, performance of the nomogram was the best in predicting Lauren type of gastric cancer, with the respective AUC, accuracy, sensitivity and specificity of 0.864, 78.0%, 90.0%, 70.0%in the testing set. In addition, the calibration curve showed a good agreement between prediction probability and actual clinical findings (p > 0.05).
CONCLUSION
The nomogram combining radiomics signature and clinical features is a useful tool with the increased value to predict Lauren type of gastric cancer.
目的
探讨术前基于CT影像组学列线图预测胃癌Lauren分型的可行性。
材料与方法
回顾性分析300例术后病理确诊为Lauren肠型或弥漫型胃癌患者的临床资料及治疗前CT图像，按2∶1比例随机分为训练集和测试集。分别比较训练集和测试集中两种Lauren分型的临床特征。采用ITK-SNAP软件手动分割CT图像上的胃肿瘤，提取分割肿瘤的影像组学特征，使用最小绝对收缩和选择算子（LASSO）回归进行特征筛选和降维，以选择最优特征并构建影像组学标签。将影像组学特征与临床特征相结合构建列线图，用于预测胃癌的Lauren分型。采用受试者操作特征（ROC）曲线分析及曲线下面积（AUC）比较临床模型、影像组学标签模型和列线图模型。采用校准曲线检验预测概率与实际临床结果之间的一致性，并绘制决策曲线评估列线图模型的临床实用性。
结果
在临床特征方面，胃癌的Lauren分型与患者年龄及CT-N分期有关（均P<0.05）。利用保留的10个影像组学特征构建了影像组学标签。列线图由2个临床特征和影像组学标签构建而成。在3种预测模型中，列线图预测胃癌Lauren分型的性能最佳，测试集中其AUC、准确度、灵敏度和特异度分别为0.864、78.0%、90.
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