Department of Breast Surgery, Affiliated Hospital of Chengde Medical University, Chengde 067000, Hebei, China.
Department of Pathology, Affiliated Hospital of Chengde Medical university, Chengde 067000, Hebei, China.
J Healthc Eng. 2021 Sep 17;2021:6318936. doi: 10.1155/2021/6318936. eCollection 2021.
This study was to analyze the effect of the combined application of deep learning technology and ultrasound imaging on the effect of breast-conserving surgery for breast cancer. A deep label distribution learning (LDL) model was designed, and the semiautomatic segmentation algorithm based on the region growing and active contour technology (RA) and the segmentation model based on optimized nearest neighbors (ON) were introduced for comparison. The designed algorithm was applied to the breast-conserving surgery of breast cancer patients. According to the difference in intraoperative guidance methods, 102 female patients with early breast cancer were divided into three groups: 34 cases in W1 group (ultrasound guidance based on deep learning segmentation model), 34 cases in W2 group (ultrasound guidance), and 34 cases in W3 group (palpation guidance). The results revealed that the tumor area segmented by the LDL algorithm constructed in this study was closer to the real tumor area; the segmentation accuracy (AC), Jaccard, and true-positive (TP) values of the LDL algorithm were obviously greater than those of the RA and ON algorithms, while the false-positive (FP) value was significantly lower in contrast to the RA and ON algorithms, showing statistically observable differences ( < 0.05); the actual resection volume of the patients in the W1 group was the closest to the ideal resection volume, which was much smaller in contrast to that of the patients in the W2 and W3 groups, showing statistical differences ( < 0.05); the positive margins of the patients in the W1 group were statistically lower than those in the W2 and W3 groups ( < 0.05). In addition, 1 patient in the W1 group was not satisfied with the cosmetic effect, 3 patients in the W2 group were not satisfied with the cosmetic effect, and 9 patients in the W3 group were not satisfied with the cosmetic effect. Finally, it was found that the ultrasound image based on the deep LDL model effectively improved the AC of tumor resection and negative margins, reduced the probability of normal tissue being removed, and improved the postoperative cosmetic effect of breast.
本研究旨在分析深度学习技术与超声成像相结合对乳腺癌保乳手术效果的影响。设计了一个深度标签分布学习（LDL）模型，并引入了基于区域生长和主动轮廓技术的半自动分割算法（RA）和基于优化最近邻的分割模型（ON）进行比较。将设计的算法应用于乳腺癌患者的保乳手术中。根据术中指导方法的不同，将 102 例早期乳腺癌女性患者分为三组：W1 组（基于深度学习分割模型的超声引导）34 例，W2 组（超声引导）34 例，W3 组（触诊引导）34 例。结果显示，本研究构建的 LDL 算法分割的肿瘤区域更接近真实肿瘤区域；LDL 算法的分割准确率（AC）、Jaccard 和真阳性（TP）值明显大于 RA 和 ON 算法，而假阳性（FP）值明显低于 RA 和 ON 算法，差异有统计学意义（ < 0.05）；W1 组患者的实际切除体积最接近理想切除体积，明显小于 W2 组和 W3 组患者，差异有统计学意义（ < 0.05）；W1 组患者的阳性切缘明显低于 W2 组和 W3 组患者，差异有统计学意义（ < 0.05）。此外，W1 组中有 1 例患者对美容效果不满意，W2 组中有 3 例患者对美容效果不满意，W3 组中有 9 例患者对美容效果不满意。最后发现，基于深度 LDL 模型的超声图像有效提高了肿瘤切除的 AC 和阴性切缘，降低了正常组织被切除的概率，提高了乳房的术后美容效果。
