Ultrasound convolutional neural network model for assisting distinguishment of cervical lymph node lymphoma and atypical reactive hyperplasia
10.13929/j.issn.1003-3289.2025.06.012
- VernacularTitle:超声卷积神经网络模型辅助鉴别颈部淋巴结淋巴瘤与不典型反应性增生
- Author:
Haitao HU
1
;
Fang JIA
1
;
Xiaorong WANG
1
Author Information
1. 新疆医科大学第一附属医院腹部超声诊断科,新疆乌鲁木齐 830054
- Publication Type:Journal Article
- Keywords:
head and neck neoplasms;
lymphoma;
hyperplasia;
ultrasonography;
diagnosis,differential;
convolutional neural network
- From:
Chinese Journal of Medical Imaging Technology
2025;41(6):903-907
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of ultrasound convolutional neural network(CNN)model for assisting distinguishment of cervical lymph node lymphoma and atypical reactive hyperplasia.Methods Totally 335 cases of cervical lymph node lymphoma and 335 cases of atypical reactive hyperplasia were retrospectively enrolled,including 520 cases in development group(260 cases of lymphoma and 260 cases of atypical reactive hyperplasia)and 150 cases in validation group(75 cases of lymphoma and 75 cases of atypical reactive hyperplasia).Patients in development group were divided into training set(182 cases of lymphoma and 182 cases of atypical reactive hyperplasia)and test set(78 cases of lymphoma and 78 cases of atypical reactive hyperplasia)at the ratio of 7∶3.One target lymph node was selected for each case,and one gray-scale ultrasound image and one CDFI were included for training,testing CNN models and verifying the auxiliary efficacy of the models.Based on ultrasound images in training set,5 CNN models,including AlexNet,VGG16,ResNet18,DenseNet161 and EfficientNet-B0,were constructed and trained for distinguishing cervical lymph node lymphoma and atypical reactive hyperplasia,and the models were tested in test set to screen out the best one with the highest classification accuracy.The efficacy of the best CNN model for assisting distinguishment of cervical lymph node lymphoma and atypical reactive hyperplasia was observed.Results Among 5 CNN models,the accuracy of ResNet18 model in test set was the highest(78.21％),and ResNet18 model was regarded as the best CNN model,its sensitivity,specificity and accuracy for assisting distinguishment of cervical lymph node lymphoma and atypical reactive hyperplasia were all higher than those of independent diagnosis made by ultrasound physicians(all P＜0.01).Conclusion The constructed ResNet18 model could be used to effectively assist differentiating cervical lymph node lymphoma and atypical reactive hyperplasia.
