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Value analysis of deep learning model based on DCE-MRI images in the differential diagnosis of benign and malignant breast tumors
LUO Wenbin  ZHENG Ye  LIU Xin  WANG Lei  DUAN Shaoyin 

Cite this article as: LUO W B, ZHENG Y, LIU X, et al. Value analysis of deep learning model based on DCE-MRI images in the differential diagnosis of benign and malignant breast tumors[J]. Chin J Magn Reson Imaging, 2024, 15(10): 22-29. DOI:10.12015/issn.1674-8034.2024.10.005.


[Abstract] Objective To explore the value of image deep learning model based on dynamic contrast-enhanced magnetic resonance imaging in differential diagnosis of benign and malignant breast tumors.Materials and Methods A total of 303 breast tumor patients diagnosed pathologically in the Second Affiliated Hospital of Xiamen Medical College from September 2018 to December 2022 were retrospectively collected, including 144 benign and 159 malignant. Stratified random sampling patients were divided into 212 training set and 91 test set according to the ratio of 7:3. Six DCE-MRI Deep learning models were constructed: 50-layer deep residual network (ResNet-50), Inception-V3, Googlenet, Densely connected convolutional networks (DenseNet)-121, visual geometry group (VGG)-19 and mobile neural network (MobileNet)-V3 were used to visualize the model simultaneously with gradient-weighted class activation mapping. Finally, the diagnostic results of the deep learning model, junior and senior radiologists were compared by the first and second rounds of reading. According to the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, negative predictive value and positive predictive value analyze the diagnostic efficiency of different deep learning models and two rounds of reading, calculate the area under the curve of each deep learning model, compare the ROC curves among the models with DeLong test, and compare the diagnostic efficiency of two rounds of reading with paired chi-square test.Results The AUC of the six deep learning models ResNet-50, Inception-V3, Googlenet, DenseNet-121, VGG-19 and MobileNet-V3 was 0.874 [95% confidence interval (CI): 0.828-0.920], 0.771 (95% CI: 0.707-0.834), 0.993 (95% CI: 0.986-0.999), 0.926 (95% CI: 0.888-0.958), 0.947 (95% CI: 0.918-0.975) and 0.945 (95% CI: 0.918-0.973). The test sets of ResNet-50, Inception-V3, Googlenet, DenseNet-121, VGG-19 and MobileNet-V3 had an AUC of 0.841 (95% CI: 0.755-0.927), 0.746 (95% CI: 0.641-0.851), 0.822 (95% CI: 0.736-0.909), 0.752 (95% CI: 0.650-0.855), 0.827 (95% CI: 0.737-0.918) and 0.779 (95% CI: 0.685-0.874). ResNet-50 model Grad-CAM visualization images showed that malignant breast tumors were activated in the center and benign tumors were activated in the periphery. In the first round of reading, the accuracy, specificity and sensitivity of ResNet-50 deep learning model were 80.2%, 86.7% and 73.9%, junior doctors were 73.6%, 73.3% and 73.9%, and senior doctors were 80.2%, 80.0% and 80.4%, respectively. In the second round of reading, with the assistance of ResNet-50 model, the accuracy, specificity and sensitivity of junior doctors increased by 15.4%, 17.8% and 13.1% (P<0.05), while the accuracy, specificity and sensitivity of senior doctors increased by 12.1%, 13.3% and 10.9% (P=0.001, 0.031, 0.063).Conclusions ResNet-50 model has the best performance in differential diagnosis of benign and malignant breast tumors, and visual images may become the basis of imaging diagnosis. With the help of this model, radiologists significantly improve the accuracy of differential diagnosis of benign and malignant breast tumors, which provides an objective basis for clinical decision-making.
[Keywords] breast neoplasms；auxiliary diagnosis；deep learning；dynamic contrast-enhanced magnetic resonance imaging；convolutional neural network

LUO Wenbin1   ZHENG Ye1   LIU Xin1   WANG Lei1   DUAN Shaoyin2*  

1 Department of Radiology, the second Affiliated Hospital of Xiamen Medical College, Xiamen 361021, China

2 Department of Radiology, Zhongshan Hospital Xiamen University, Xiamen 361004, China

Corresponding author: DUAN S Y, E-mail: xmdsy@xmu.edu.cn

Conflicts of interest   None.

Received  2024-05-28
Accepted  2024-09-06
DOI: 10.12015/issn.1674-8034.2024.10.005
Cite this article as: LUO W B, ZHENG Y, LIU X, et al. Value analysis of deep learning model based on DCE-MRI images in the differential diagnosis of benign and malignant breast tumors[J]. Chin J Magn Reson Imaging, 2024, 15(10): 22-29. DOI:10.12015/issn.1674-8034.2024.10.005.

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