Abstract：
Liver tumors constitute a major part of the global disease burden, often making regular imaging follow-up necessary. Recently, deep learning (DL) has increasingly been applied in this research area. How these methods could facilitate report writing is still a question, which our study aims to address by assessing multiple DL methods using the Medical Open Network for Artificial Intelligence (MONAI) framework, which may provide clinicians with preliminary information about a given liver lesion. For this purpose, we collected 2274 three-dimensional images of lesions, which we cropped from gadoxetate disodium enhanced T1w, native T1w, and T2w magnetic resonance imaging (MRI) scans. After we performed training and validation using 202 and 65 lesions, we selected the best performing model to predict features of lesions from our in-house test dataset containing 112 lesions. The model (EfficientNetB0) predicted 10 features in the test set with an average area under the receiver operating characteristic curve (standard deviation), sensitivity, specificity, negative predictive value, positive predictive value of 0.84 (0.1), 0.78 (0.14), 0.86 (0.08), 0.89 (0.08) and 0.71 (0.17), respectively. These results suggest that AI methods may assist less experienced residents or radiologists in liver MRI reporting of focal liver lesions.
摘要：
肝肿瘤构成了全球疾病负担的主要部分，经常需要定期成像随访。最近,深度学习(DL)已越来越多地应用于这一研究领域。这些方法如何促进报告的编写仍然是一个问题，我们的研究旨在通过使用医疗开放人工智能网络（MONAI）框架评估多种DL方法来解决这一问题，这可以为临床医生提供有关给定肝脏病变的初步信息。为此,我们收集了2274张病变的三维图像，我们用gadoxetate二钠增强的T1w裁剪而成，原生T1w,和T2w磁共振成像（MRI）扫描。在我们使用202和65个病变进行训练和验证后，我们从包含112个病变的内部测试数据集中选择了性能最佳的模型来预测病变特征.模型（EfficientNetB0）预测了测试集中的10个特征，其中接收器工作特性曲线下的平均面积（标准偏差），灵敏度,特异性，负预测值，阳性预测值为0.84（0.1），0.78（0.14），0.86(0.08),0.89（0.08）和0.71（0.17），分别。这些结果表明，AI方法可以帮助经验不足的居民或放射科医生进行肝脏MRI报告局灶性肝脏病变。
