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Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning
Scientific Reports ( IF 4.6 ) Pub Date : 2020-11-23 , DOI: 10.1038/s41598-020-77389-0
Joonsang Lee 1 , Nicholas Wang 1 , Sevcan Turk 2 , Shariq Mohammed 1 , Remy Lobo 2 , John Kim 2 , Eric Liao 2 , Sandra Camelo-Piragua 3 , Michelle Kim 4 , Larry Junck 5 , Jayapalli Bapuraj 2 , Ashok Srinivasan 2 , Arvind Rao 1
Affiliation  

Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurrence. In this study, we proposed to use a multiparametric MRI data as a sequence input for the convolutional neural network with the recurrent neural network based deep learning structure to discriminate between pseudoprogression and true tumor progression. In this study, 43 biopsy-proven patient data identified as diffuse infiltrating glioma patients whose disease progressed/recurred were used. The dataset consists of five original MRI sequences; pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, FLAIR, and ADC images as well as two engineered sequences; T1post–T1pre and T2–FLAIR. Next, we used three CNN-LSTM models with a different set of sequences as input sequences to pass through CNN-LSTM layers. We performed threefold cross-validation in the training dataset and generated the boxplot, accuracy, and ROC curve, AUC from each trained model with the test dataset to evaluate models. The mean accuracy for VGG16 models ranged from 0.44 to 0.60 and the mean AUC ranged from 0.47 to 0.59. For CNN-LSTM model, the mean accuracy ranged from 0.62 to 0.75 and the mean AUC ranged from 0.64 to 0.81. The performance of the proposed CNN-LSTM with multiparametric sequence data was found to outperform the popular convolutional CNN with a single MRI sequence. In conclusion, incorporating all available MRI sequences into a sequence input for a CNN-LSTM model improved diagnostic performance for discriminating between pseudoprogression and true tumor progression.



中文翻译:

通过深度学习使用多参数 MRI 数据区分弥漫浸润性胶质瘤的假进展和真实进展

区分假性进展与真正的肿瘤进展已成为弥漫浸润性胶质瘤随访的重大挑战,特别是高级别,这导致早期胶质瘤复发患者的潜在治疗延迟。在这项研究中,我们建议使用多参数 MRI 数据作为卷积神经网络的序列输入,该网络具有基于循环神经网络的深度学习结构,以区分假性进展和真实的肿瘤进展。在这项研究中,使用了 43 名经活检证实的患者数据,这些患者数据被确定为疾病进展/复发的弥漫性浸润性胶质瘤患者。数据集由五个原始 MRI 序列组成;对比前 T1 加权、对比后 T1 加权、T2 加权、FLAIR 和 ADC 图像以及两个工程序列;T1post–T1pre 和 T2–FLAIR。下一个,我们使用了三个具有不同序列集的 CNN-LSTM 模型作为输入序列以通过 CNN-LSTM 层。我们在训练数据集中进行了三重交叉验证,并从每个训练模型和测试数据集生成箱线图、准确率和 ROC 曲线、AUC 以评估模型。VGG16 模型的平均准确度范围为 0.44 到 0.60,平均 AUC 范围为 0.47 到 0.59。对于 CNN-LSTM 模型,平均准确率范围为 0.62 至 0.75,平均 AUC 范围为 0.64 至 0.81。发现所提出的具有多参数序列数据的 CNN-LSTM 的性能优于具有单个 MRI 序列的流行卷积 CNN。综上所述,

更新日期:2020-11-23
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