Peng Yu-Ting, Pang Jin-Shu, Lin Peng, Chen Jia-Min, Wen Rong, Liu Chang-Wen, Wen Zhi-Yuan, Wu Yu-Quan, Peng Jin-Bo, Zhang Lu, Yang Hong, Wen Dong-Yue, He Yun
Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong, Road, Nanning, Guangxi Zhuang Autonomous Region, China.
Department of Medical Ultrasound, Fujian Medical University Union Hospital, No.29 Xinquan road, Fuzhou, Fujian Province, China.
BMC Med Imaging. 2025 Jan 2;25(1):4. doi: 10.1186/s12880-024-01542-8.
To develop ultrasound-based radiomics models and a clinical model associated with inflammatory markers for predicting intrahepatic cholangiocarcinoma (ICC) lymph node (LN) metastasis. Both are integrated for enhanced preoperative prediction.
This study retrospectively enrolled 156 surgically diagnosed ICC patients. A region of interest (ROI) was manually identified on the ultrasound image of the tumor to extract radiomics features. In the training cohort, we performed a Wilcoxon test to screen for differentially expressed features, and then we used 12 machine learning algorithms to develop 107 models within the cross-validation framework and determine the optimal radiomics model through receiver operating characteristic (ROC) curve analysis. Multivariable logistic regression analysis was used to identify independent risk factors to construct a clinical model. The combined model was established by combining ultrasound-based radiomics and clinical parameters. The Delong test and decision curve analysis (DCA) were used to compare the diagnostic efficacy and clinical utility of different models.
A total of 1239 radiomics features were extracted from the ROIs of tumors. Among the 107 prediction models, the model (Stepglm + LASSO) utilizing 10 radiomics features ultimately yielded the highest average area under the receiver operating characteristic curve (AUC) of 0.872, with an AUC of 0.916 in the training cohort and 0.827 in the validation cohort. The combined model, which incorporates the optimal radiomics score, clinical N stage, and platelet-to-lymphocyte ratio (PLR), achieved an AUC of 0.882 in the validation cohort, significantly outperforming the clinical model with an AUC of 0.687 (P = 0.009). According to the DCA analysis, the combined model also showed better clinical benefits.
The combined model incorporating ultrasound-based radiomics features and the PLR marker offers an effective, noninvasive intelligence-assisted tool for preoperative LN metastasis prediction in ICC patients.
Not applicable.
建立基于超声的影像组学模型和与炎症标志物相关的临床模型，用于预测肝内胆管癌（ICC）淋巴结（LN）转移。将两者整合以增强术前预测能力。
本研究回顾性纳入156例经手术确诊的ICC患者。在肿瘤的超声图像上手动确定感兴趣区域（ROI）以提取影像组学特征。在训练队列中，我们进行Wilcoxon检验以筛选差异表达特征，然后使用12种机器学习算法在交叉验证框架内开发107个模型，并通过受试者操作特征（ROC）曲线分析确定最佳影像组学模型。采用多变量逻辑回归分析确定独立危险因素以构建临床模型。通过结合基于超声的影像组学和临床参数建立联合模型。使用Delong检验和决策曲线分析（DCA）比较不同模型的诊断效能和临床实用性。
从肿瘤的ROI中总共提取了1239个影像组学特征。在107个预测模型中，利用10个影像组学特征的模型（Stepglm + LASSO）最终获得最高的受试者操作特征曲线下平均面积（AUC）为0.872，在训练队列中的AUC为0.916，在验证队列中的AUC为0.827。结合最佳影像组学评分、临床N分期和血小板与淋巴细胞比值（PLR）的联合模型在验证队列中的AUC为0.882，显著优于AUC为0.687的临床模型（P = 0.009）。根据DCA分析，联合模型也显示出更好的临床效益。
结合基于超声的影像组学特征和PLR标志物的联合模型为ICC患者术前LN转移预测提供了一种有效的、非侵入性的智能辅助工具。
不适用。
