| 俞宇羽,朱翰林,魏培英,张海峰,冯波.平扫CT影像组学极限梯度提升(XGBoost)模型预测急性胰腺炎周围坏死物积聚[J].中国医学影像技术,2025,41(2):281~285 |
| 平扫CT影像组学极限梯度提升(XGBoost)模型预测急性胰腺炎周围坏死物积聚 |
| Non-contrast CT radiomics extreme gradient boosting (XGBoost) model for predicting acute necrotic collection around acute pancreatitis |
| 投稿时间：2024-09-02 修订日期：2024-10-29 |
| DOI：10.13929/j.issn.1003-3289.2025.02.021 |
| 中文关键词: 胰腺炎 坏死物积聚 影像组学 |
| 英文关键词:pancreatitis necrotic collection radiomics |
| 基金项目:浙江省杭州市医药卫生科技项目(A20220121)。 |
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| 中文摘要: |
| 目的 基于SHAP法观察平扫CT影像组学极限梯度提升(XGBoost)模型预测急性胰腺炎(AP)周围急性坏死物积聚(ANC)的价值。方法 回顾性收集307例首诊AP患者。基于首诊平扫CT自动分割胰腺周围组织感兴趣体积(VOI),提取并筛选最优影像组学特征;记录基于首诊增强CT的AP严重程度改良版CT严重指数(MCTSI)评分。根据随访结果将AP患者分为胰腺周围ANC组(ANC组)与液体积聚(APFC)组。以XGBoost法分别基于最佳影像组学特征、MCTSI及二者联合构建预测AP ANC的影像组学、MCTSI及联合模型,采用5折交叉验证法评估各模型诊断效能,利用SHAP法分析各变量对于联合模型的贡献。结果 307例中,ANC组134例、APFC组173例。基于首诊平扫CT共筛选出6个最优影像组学特征;据此构建的影像组学、MCTSI及联合模型的受试者工作特征曲线下面积(AUC)分别为0.936、0.693及0.917;其中MCTSI模型的AUC低于影像组学模型及联合模型的AUC(Z=－3.485、－2.824,P均＜0.01),后二者AUC差异无统计学意义(Z=－0.817,P=0.415)。联合模型中,最优影像组学特征的贡献度均优于MCTSI评分。结论 平扫CT影像组学XGBoost模型能有效预测AP ANC。 |
| 英文摘要: |
| Objective To observe the value of non-contrast CT radiomics extreme gradient boosting (XGBoost) model based on SHAP method for predicting acute necrotic collection (ANC) around acute pancreatitis (AP). Methods A total of 307 patients with initially clinically diagnosed AP were retrospectively enrolled. The optimal radiomics features of peripheral pancreatic tissue volume of interest (VOI) were extracted and screened based on automatic segmentation on the first non-contrast CT, and the evaluation results of modified CT severity index (MCTSI) score of AP severity based on first enhanced CT were recorded. The patients were divided into peripancreatic ANC group (ANC group) and acute peripancreatic fluid collection (APFC) group according to follow-up abdominal CT. XGBoost method was used to construct radiomics model, MCTSI model and combined model for predicting AP ANC based on the optimal radiomics features, MCTSI and their combination, respectively. The diagnostic efficacy of each model was evaluated using 5-fold cross-validation method, and the contribution of each variable to combined model was analyzed with SHAP method. Results Among 307 cases, there were 134 cases in ANC group and 173 in APFC group. Totally 6 optimal radiomics features were screened based on the first non-contrast CT. The area under the receiver operating characteristic curve (AUC) of radiomics model, MCTSI model and combined model was 0.936, 0.693 and 0.917, respectively. The AUC of MCTSI model was lower than that of radiomics model and combined model (Z=－3.485, －2.824, both P＜0.01), while no significant difference of AUC was found between radiomics model and combined model (Z=－0.817, P=0.415). The contribution of optimal radiomics features to combined model were all higher than that of MCTSI score. Conclusion Non-contrast CT radiomics XGBoost model could effectively predict AP ANC. |
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