Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer.
Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer.
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用于术前预测膀胱癌淋巴结转移的基于 MRI 的放射组学特征的开发和验证
DOI：
10.1016/j.ebiom.2018.07.029
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发表时间：
2018-08
期刊：
影响因子：
11.1
通讯作者：
Lin T
中科院分区：
文献类型：
--
作者：
Wu S;Zheng J;Li Y;Wu Z;Shi S;Huang M;Yu H;Dong W;Huang J;Lin T
术前淋巴结状态对膀胱癌(BCA)的治疗具有重要意义。然而，根据目前的方法，有一定比例的患者存在临床结节分期不准确的风险。在这里，我们报告了一个准确的基于磁共振成像(MRI)的放射组学特征，用于BCA术前预测LN转移的个体化。总共有10 3名符合条件的患者被分成训练集(n = 69)和验证集(n = 34)。从T2加权MRI图像上感兴趣的癌灶(VOIS)中提取718个放射组学特征。在训练集中使用最小绝对收缩和选择算子(LASSO)算法构造放射组学签名，其性能被评估，然后在验证集中进行验证。还进行了分层分析。在多变量Logistic回归分析的基础上，开发了包含放射组学特征和选定的临床预测因素的放射组学诺模图。对诺模图的辨别、校准和临床应用进行了评估。由9个选定的特征组成，放射组学特征在训练集中表现出良好的区分能力，AUC值为0.9005，在验证集中得到确认，AUC值为0.8447。令人鼓舞的是，放射组学特征在核磁共振报告的LN阴性(CN0)亚组中也显示出良好的区分性(AuC，0.8406)。由放射组学签名和核磁共振报告的LN状态组成的诺模图在训练和验证集上显示出良好的校准和区分性(AUC，分别为0.9118和0.8902)。决策曲线分析表明，该图具有较好的临床应用价值。基于MRI的放射组学诺模图有可能作为一种无创性工具用于BCA术前LN转移的个体化预测。在临床实施之前，还需要进一步的外部验证。
Preoperative lymph node (LN) status is important for the treatment of bladder cancer (BCa). However, a proportion of patients are at high risk for inaccurate clinical nodal staging by current methods. Here, we report an accurate magnetic resonance imaging (MRI)-based radiomics signature for the individual preoperative prediction of LN metastasis in BCa. In total, 103 eligible BCa patients were divided into a training set (n = 69) and a validation set (n = 34). And 718 radiomics features were extracted from the cancerous volumes of interest (VOIs) on T2-weighted MRI images. A radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm in the training set, whose performance was assessed and then validated in the validation set. Stratified analyses were also performed. Based on the multivariable logistic regression analysis, a radiomics nomogram was developed incorporating the radiomics signature and selected clinical predictors. Discrimination, calibration and clinical usefulness of the nomogram were assessed. Consisting of 9 selected features, the radiomics signature showed a favorable discriminatory ability in the training set with an AUC of 0.9005, which was confirmed in the validation set with an AUC of 0.8447. Encouragingly, the radiomics signature also showed good discrimination in the MRI-reported LN negative (cN0) subgroup (AUC, 0.8406). The nomogram, consisting of the radiomics signature and the MRI-reported LN status, showed good calibration and discrimination in the training and validation sets (AUC, 0.9118 and 0.8902, respectively). The decision curve analysis indicated that the nomogram was clinically useful. The MRI-based radiomics nomogram has the potential to be used as a non-invasive tool for individualized preoperative prediction of LN metastasis in BCa. External validation is further required prior to clinical implementation.
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