The value of deep learning models based on ultrafast dynamic contrast-enhanced MRI for diagnosing malignant breast lesions
10.3760/cma.j.cn112149-20240821-00501
- VernacularTitle:基于超快速动态对比增强MRI的深度学习模型诊断乳腺恶性病变的价值
- Author:
Wenqi WANG
1
;
Wenjuan MA
1
;
Yijun GUO
1
;
Jingbo WANG
1
;
Hong LU
1
Author Information
1. 天津医科大学肿瘤医院乳腺影像诊断科 国家恶性肿瘤临床医学研究中心 天津市恶性肿瘤临床医学研究中心 乳腺癌防治教育部重点实验室 天津市肿瘤防治重点实验室，天津 300060
- Publication Type:Journal Article
- Keywords:
Breast neplasms;
Ultrafast dynamic contrast enhanced magnetic resonance imaging;
Deep learning;
Diagnosis
- From:
Chinese Journal of Radiology
2025;59(3):307-312
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of deep learning models based on ultrafast dynamic contrast-enhanced MRI (UF-DCE MRI) in predicting malignant breast lesions.Methods:The study was a cross-sectional study. Clinical and imaging data of 347 patients with breast lesions who received treatment at Tianjin Medical University Cancer Institute and Hospital from March 2023 to January 2024 were analyzed retrospectively. A total of 347 lesions were observed in the 347 patients, including 75 benign and 272 malignant lesions. The random number method was used to divide into the training set with 243 cases and the validation set with 104 cases in a ratio of 7∶3. All patients underwent breast UF-DCE MRI and conventional dynamic-enhanced MRI (DCE-MRI). A 27-channel model (27-phase enhancement images of input UF-DCE MRI), a 3-channel model (3-phase enhancement images of input DCE-MRI), and a 1-channel model (1st-phase enhancement images of DCE-MRI) were built based on the pre-trained ResNet18 deep learning model on ImageNet. The efficacy of each model in predicting breast malignant lesions was analyzed using receiver operating characteristic curves and area under the curve (AUC). The differences of AUC were compared using DeLong test.Results:In the training and validation sets, the 27-channel model had the highest AUC for diagnosing malignant breast lesions, which were 0.848 (95% CI 0.818-0.877) and 0.784 (95% CI 0.752-0.817), respectively. DeLong test showed no statistically significant difference in the AUC values of the three models in the validation set for the diagnosis of malignant lesions of the breast in a two-by-two comparison ( P>0.05). UF-DCE MRI scans were 27 phases totaling 81 s with a temporal resolution of 3 s/phase; DCE-MRI scans were 3 phases totaling 270 s with a temporal resolution of 90 s/phase. Conclusions:The model combining UF-DCE MRI with deep learning demonstrates comparable efficacy to DCE-MRI deep learning model in diagnosing breast malignant lesions. However the UF-DCE MRI has the advantages of high temporal resolution and short scanning time, which makes this model valuable for precise diagnosis and treatment of breast cancer.
