超声造影
卷积神经网络
深度学习
化疗
人工智能
医学
放射科
二元分类
计算机科学
超声波
内科学
支持向量机
作者
Yuming Shao,Yingnan Dang,Yuejuan Cheng,Yang Gui,Xueqi Chen,Tianjiao Chen,Yan Zeng,Li Tan,Jing Zhang,Mengsu Xiao,Xiaoyi Yan,Ke Lv,Zhuhuang Zhou
出处
期刊:Diagnostics
[Multidisciplinary Digital Publishing Institute]
日期:2023-06-27
卷期号:13 (13): 2183-2183
被引量:10
标识
DOI:10.3390/diagnostics13132183
摘要
Contrast-enhanced ultrasound (CEUS) is a promising imaging modality in predicting the efficacy of neoadjuvant chemotherapy for pancreatic cancer, a tumor with high mortality. In this study, we proposed a deep-learning-based strategy for analyzing CEUS videos to predict the prognosis of pancreatic cancer neoadjuvant chemotherapy. Pre-trained convolutional neural network (CNN) models were used for binary classification of the chemotherapy as effective or ineffective, with CEUS videos collected before chemotherapy as the model input, and with the efficacy after chemotherapy as the reference standard. We proposed two deep learning models. The first CNN model used videos of ultrasound (US) and CEUS (US+CEUS), while the second CNN model only used videos of selected regions of interest (ROIs) within CEUS (CEUS-ROI). A total of 38 patients with strict restriction of clinical factors were enrolled, with 76 original CEUS videos collected. After data augmentation, 760 and 720 videos were included for the two CNN models, respectively. Seventy-six-fold and 72-fold cross-validations were performed to validate the classification performance of the two CNN models. The areas under the curve were 0.892 and 0.908 for the two models. The accuracy, recall, precision and F1 score were 0.829, 0.759, 0.786, and 0.772 for the first model. Those were 0.864, 0.930, 0.866, and 0.897 for the second model. A total of 38.2% and 40.3% of the original videos could be clearly distinguished by the deep learning models when the naked eye made an inaccurate classification. This study is the first to demonstrate the feasibility and potential of deep learning models based on pre-chemotherapy CEUS videos in predicting the efficacy of neoadjuvant chemotherapy for pancreas cancer.
科研通智能强力驱动
Strongly Powered by AbleSci AI