未经批准:在临床工作中,准确测量乳腺癌的体积和大小对制定治疗方案具有重要意义。然而,这是耗时的,放射科医师之间存在观察者之间和观察者之间的差异。这项研究的目的是评估基于自动分割的Res-UNet卷积神经网络的性能,用于磁共振成像(MRI)上肿块增强乳腺癌的大小和体积测量。
UNASSIGNED:从2015年1月至2021年10月,共选择1,000名在治疗前接受了1.5T动态对比增强MRI检查的女性乳腺癌患者,并随机分为训练队列(n=800)和测试队列(n=200)。与放射科医生手动描绘的名为地面真相的面具相比,使用骰子相似系数(DSC)和组内相关系数(ICC)评估模型在分割方面的性能.对肿瘤(T)分期分类的性能进行了准确评估,灵敏度,和特异性。
未经评估:在测试队列中,自动分割的DSC达到0.89。在模型和放射科医师之间显示了最大和最小直径的优异一致性(ICC>0.95)和体积测量的良好一致性(ICC>0.80)。经过训练的模型大约需要10-15s才能提供自动分割,并对T阶段进行分类,总精度分别为0.93,灵敏度为0.94、0.94和0.75,特异性分别为0.95、0.92和0.99,在T1、T2和T3中。
UNASSIGNED:我们的模型证明了用于乳腺癌大小和体积测量的自动分割的良好性能和可靠性,这可以节省时间和有效的临床决策。
UNASSIGNED: In clinical work, accurately measuring the volume and the size of breast cancer is significant to develop a treatment plan. However, it is time-consuming, and inter- and intra-observer variations among radiologists exist. The purpose of this study was to assess the performance of a Res-UNet convolutional neural network based on automatic segmentation for size and volumetric measurement of mass enhancement breast cancer on magnetic resonance imaging (MRI).
UNASSIGNED: A total of 1,000 female breast cancer patients who underwent preoperative 1.5-T dynamic contrast-enhanced MRI prior to treatment were selected from January 2015 to October 2021 and randomly divided into a training cohort (n = 800) and a testing cohort (n = 200). Compared with the masks named ground truth delineated manually by radiologists, the model performance on segmentation was evaluated with dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC). The performance of tumor (T) stage classification was evaluated with accuracy, sensitivity, and specificity.
UNASSIGNED: In the test cohort, the DSC of automatic segmentation reached 0.89. Excellent concordance (ICC > 0.95) of the maximal and minimal diameter and good concordance (ICC > 0.80) of volumetric measurement were shown between the model and the radiologists. The trained model took approximately 10-15 s to provide automatic segmentation and classified the T stage with an overall accuracy of 0.93, sensitivity of 0.94, 0.94, and 0.75, and specificity of 0.95, 0.92, and 0.99, respectively, in T1, T2, and T3.
UNASSIGNED: Our model demonstrated good performance and reliability for automatic segmentation for size and volumetric measurement of breast cancer, which can be time-saving and effective in clinical decision-making.