Liu Tianyu, Hu Yurui, Liu Zehua, Jiang Zeshuo, Ling Xiao, Zhu Xueling, Li Wenfei
School of Graduate, Hebei North University, Zhangjiakou, 075000, Hebei, China.
Department of Radiology, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, Hebei, China.
J Imaging Inform Med. 2024 Nov 25. doi: 10.1007/s10278-024-01340-2.
To investigate whether automatic segmentation based on DCE-MRI with a deep learning (DL) algorithm enabled advantages over manual segmentation in differentiating BI-RADS 4 breast lesions. A total of 197 patients with suspicious breast lesions from two medical centers were enrolled in this study. Patients treated at the First Hospital of Qinhuangdao between January 2018 and April 2024 were included as the training set (n = 138). Patients treated at Lanzhou University Second Hospital were assigned to an external validation set (n = 59). Areas of suspicious lesions were delineated based on DL automatic segmentation and manual segmentation, and evaluated consistency through the Dice correlation coefficient. Radiomics models were constructed based on DL and manual segmentations to predict the nature of BI-RADS 4 lesions. Meanwhile, the nature of the lesions was evaluated by both a professional radiologist and a non-professional radiologist. Finally, the area under the curve value (AUC) and accuracy (ACC) were used to determine which prediction model was more effective. Sixty-four malignant cases (32.5%) and 133 benign cases (67.5%) were included in this study. The DL-based automatic segmentation model showed high consistency with manual segmentation, achieving a Dice coefficient of 0.84 ± 0.11. The DL-based radiomics model demonstrated superior predictive performance compared to professional radiologists, with an AUC of 0.85 (95% CI 0.79-0.92). The DL model significantly reduced working time and improved efficiency by 83.2% compared to manual segmentation, further demonstrating its feasibility for clinical applications. The DL-based radiomics model for automatic segmentation outperformed professional radiologists in distinguishing between benign and malignant lesions in BI-RADS category 4, thereby helping to avoid unnecessary biopsies. This groundbreaking progress suggests that the DL model is expected to be widely applied in clinical practice in the near future, providing an effective auxiliary tool for the diagnosis and treatment of breast cancer.
为研究基于深度学习（DL）算法的DCE-MRI自动分割在鉴别BI-RADS 4类乳腺病变方面是否比手动分割更具优势。本研究纳入了来自两个医疗中心的197例乳腺可疑病变患者。2018年1月至2024年4月在秦皇岛市第一医院接受治疗的患者作为训练集（n = 138）。在兰州大学第二医院接受治疗的患者被分配到外部验证集（n = 59）。基于DL自动分割和手动分割勾勒出可疑病变区域，并通过Dice相关系数评估一致性。基于DL和手动分割构建放射组学模型以预测BI-RADS 4类病变的性质。同时，由专业放射科医生和非专业放射科医生评估病变的性质。最后，使用曲线下面积值（AUC）和准确率（ACC）来确定哪种预测模型更有效。本研究纳入了64例恶性病例（32.5%）和133例良性病例（67.5%）。基于DL的自动分割模型与手动分割显示出高度一致性，Dice系数为0.84±0.11。基于DL的放射组学模型显示出比专业放射科医生更好的预测性能，AUC为0.85（95%CI 0.79 - 0.92）。与手动分割相比，DL模型显著减少了工作时间，效率提高了83.2%，进一步证明了其临床应用的可行性。基于DL的自动分割放射组学模型在区分BI-RADS 4类良性和恶性病变方面优于专业放射科医生，从而有助于避免不必要的活检。这一开创性进展表明，DL模型有望在不久的将来在临床实践中得到广泛应用，为乳腺癌的诊断和治疗提供有效的辅助工具。
