Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model
Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model
复制标题
基于人工神经网络的美国放射组学模型对直肠癌肿瘤沉积的术前预测
DOI：
10.1007/s00330-019-06558-1
复制
发表时间：
2020-04-01
影响因子：
5.9
通讯作者：
Wang, Wei
中科院分区：
文献类型：
--
作者：
Chen, Li-Da;Li, Wei;Wang, Wei
目的建立一种基于机器学习的超声放射组学模型，用于术前预测肿瘤沉积物。方法2015年12月至2017年12月，前瞻性纳入127例直肠癌患者，分为训练集和验证集。对每位患者进行直肠内超声（ERUS）和剪切波弹性成像（SWE）检查。每个患者总共提取了4176个US放射组学特征。在减少和选择US放射组学特征之后，在训练集中使用人工神经网络（ANN）构建预测模型。此外，还开发了两种模型（一种包含临床信息，另一种基于MRI放射组学）。通过评估其诊断性能并比较验证集中的曲线下面积（AUC）来验证这些模型。结果训练集和验证集分别包括29例（33.3%）和11例（27.5%）TD患者。建立了美国放射组学神经网络模型。预测TD的模型在验证队列中显示出75.0%的准确性。其敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和AUC分别为72.7%、75.9%、53.3%、88.0%和0.743。对于包含临床信息的模型，AUC改善至0.795。尽管在90名同时具有超声和MRI数据的患者（包括训练集和验证集）中，US放射组学模型的AUC与MRI放射组学模型的AUC相比有所改善（0.916 vs. 0.872），但差异不显著（p = 0.384）。结论超声放射组学可作为一种潜在的预测治疗前TD的模型。
Objective To develop a machine learning-based ultrasound (US) radiomics model for predicting tumour deposits (TDs) preoperatively. Methods From December 2015 to December 2017, 127 patients with rectal cancer were prospectively enrolled and divided into training and validation sets. Endorectal ultrasound (ERUS) and shear-wave elastography (SWE) examinations were conducted for each patient. A total of 4176 US radiomics features were extracted for each patient. After the reduction and selection of US radiomics features , a predictive model using an artificial neural network (ANN) was constructed in the training set. Furthermore, two models (one incorporating clinical information and one based on MRI radiomics) were developed. These models were validated by assessing their diagnostic performance and comparing the areas under the curve (AUCs) in the validation set. Results The training and validation sets included 29 (33.3%) and 11 (27.5%) patients with TDs, respectively. A US radiomics ANN model was constructed. The model for predicting TDs showed an accuracy of 75.0% in the validation cohort. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and AUC were 72.7%, 75.9%, 53.3%, 88.0% and 0.743, respectively. For the model incorporating clinical information, the AUC improved to 0.795. Although the AUC of the US radiomics model was improved compared with that of the MRI radiomics model (0.916 vs. 0.872) in the 90 patients with both ultrasound and MRI data (which included both the training and validation sets), the difference was nonsignificant (p = 0.384). Conclusions US radiomics may be a potential model to accurately predict TDs before therapy.