肝细胞癌
分割
深度学习
人工智能
预处理器
接收机工作特性
计算机科学
卷积神经网络
医学
计算机断层摄影术
无线电技术
模式识别(心理学)
机器学习
放射科
内科学
作者
Liyang Wang,Wu Meilong,Rui Li,Xiaolei Xu,Chengzhan Zhu,Xiaobin Feng
出处
期刊:Cancers
[MDPI AG]
日期:2022-06-15
卷期号:14 (12): 2956-2956
被引量:10
标识
DOI:10.3390/cancers14122956
摘要
Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) directly affects a patient's prognosis. The development of preoperative noninvasive diagnostic methods is significant for guiding optimal treatment plans. In this study, we investigated 138 patients with HCC and presented a novel end-to-end deep learning strategy based on computed tomography (CT) radiomics (MVI-Mind), which integrates data preprocessing, automatic segmentation of lesions and other regions, automatic feature extraction, and MVI prediction. A lightweight transformer and a convolutional neural network (CNN) were proposed for the segmentation and prediction modules, respectively. To demonstrate the superiority of MVI-Mind, we compared the framework's performance with that of current, mainstream segmentation, and classification models. The test results showed that MVI-Mind returned the best performance in both segmentation and prediction. The mean intersection over union (mIoU) of the segmentation module was 0.9006, and the area under the receiver operating characteristic curve (AUC) of the prediction module reached 0.9223. Additionally, it only took approximately 1 min to output a prediction for each patient, end-to-end using our computing device, which indicated that MVI-Mind could noninvasively, efficiently, and accurately predict the presence of MVI in HCC patients before surgery. This result will be helpful for doctors to make rational clinical decisions.
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