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.

中文翻译：

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lauren 型胃癌术前预测：基于 CT 图像和临床特征的放射组学列线图分析

目的：探讨术前基于CT影像组列线图预测Lauren型胃癌的可行性。材料与方法：回顾性分析300例术后病理证实为Lauren肠型或弥漫型胃癌患者的临床资料和治疗前CT图像，按2：1的比例随机分为训练集和测试集。分别在训练集和测试集中比较了两种 Lauren 类型的临床特征。使用 ITK-SNAP 软件手动分割 CT 图像上的胃肿瘤，并使用最小绝对收缩和选择算子 (LASSO) 回归提取、过滤和最小化分割肿瘤的放射组学特征，以选择最佳特征并开发放射组学特征。构建具有放射学特征和临床特征的列线图来预测胃癌的劳伦类型。临床模型、放射组学特征模型和列线图模型使用接受者操作特征 (ROC) 曲线分析与曲线下面积 (AUC) 进行比较。校准曲线用于测试预测概率与实际临床结果之间的一致性，并执行决策曲线以评估列线图模型的临床使用情况。结果：临床特征上，Lauren胃癌分型与患者年龄、CT-N分期有关（均p < 0.05）。放射组学特征是使用保留的 10 个放射组学特征开发的。列线图由 2 个临床特征和放射组学特征构成。在 3 个预测模型中，列线图在预测Lauren型胃癌方面的表现最好，在测试集中其AUC、准确性、敏感性和特异性分别为0.864、78.0%、90.0%、70.0%。此外，校准曲线显示出预测概率与实际临床结果之间的良好一致性（p > 0.05）。结论：结合放射组学特征和临床特征的列线图是一种有用的工具，具有增加的预测胃癌劳伦类型的价值。