Zhuang Zixuan, Zhang Yang, Yang Xuyang, Deng Xiangbing, Wang Ziqiang
Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan Province, China.
Abdom Radiol (NY). 2024 Jun;49(6):2008-2016. doi: 10.1007/s00261-024-04209-8. Epub 2024 Feb 27.
To prospectively develop and validate the T2WI texture analysis model based on a node-by-node comparison for improving the diagnostic accuracy of lymph node metastasis (LNM) in rectal cancer.
A total of 381 histopathologically confirmed lymph nodes (LNs) were collected. LNs texture features were extracted from MRI-T2WI. Spearman's rank correlation coefficient and the least absolute shrinkage and selection operator were used for feature selection to construct the LN rad-score. Then the clinical risk factors and LN texture features were combined to establish combined predictive model. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Decision curve analysis (DCA) and nomogram were used to evaluate the clinical application of the model.
A total of 107 texture features were extracted from LN-MRI images. After selection and dimensionality reduction, the radiomics prediction model consisting of 8 texture features showed well-predictive performance in the training and validation cohorts (AUC, 0.676; 95% CI 0.582-0.771) (AUC, 0.774; 95% CI 0.648-0.899). A clinical-radiomics prediction model with the best performance was created by combining clinical and radiomics features, 0.818 (95% CI 0.742-0.893) for the training and 0.922 (95% CI 0.863-0.980) for the validation cohort. The LN Rad-score in clinical-radiomics nomogram obtained the highest classification contribution and was well calibrated. DCA demonstrated the superiority of the clinical-radiomics model.
The lymph node T2WI-based texture features can help to improve the preoperative prediction of LNM.
前瞻性地开发并验证基于逐个节点比较的T2WI纹理分析模型，以提高直肠癌淋巴结转移（LNM）的诊断准确性。
共收集381个经组织病理学证实的淋巴结（LN）。从MRI-T2WI中提取LN的纹理特征。采用Spearman等级相关系数和最小绝对收缩与选择算子进行特征选择，构建LN放射学评分。然后将临床危险因素和LN纹理特征相结合，建立联合预测模型。通过受试者操作特征（ROC）曲线下面积（AUC）评估模型性能。采用决策曲线分析（DCA）和列线图评估模型的临床应用。
从LN-MRI图像中提取了共107个纹理特征。经过选择和降维后，由8个纹理特征组成的放射组学预测模型在训练和验证队列中表现出良好的预测性能（AUC，0.676；95%CI 0.582-0.771）（AUC，0.774；95%CI 0.648-0.899）。通过结合临床和放射组学特征创建了性能最佳的临床-放射组学预测模型，训练队列的AUC为0.818（95%CI 0.742-0.893），验证队列的AUC为0.922（95%CI 0.863-0.980）。临床-放射组学列线图中的LN放射学评分获得了最高的分类贡献，且校准良好。DCA证明了临床-放射组学模型的优越性。
基于淋巴结T2WI的纹理特征有助于提高LNM的术前预测能力。
