Graduate school, Tianjin Medical University, Tianjin 300070, China.
Department of Ultrasound, The Affiliated of Inner Mongolia Medical University, Hohhot, 010050 Inner Mongolia, China.
Comput Math Methods Med. 2022 Mar 3;2022:6557494. doi: 10.1155/2022/6557494. eCollection 2022.
The changes of hormone expression and efficacy of breast cancer (BC) were investigated under the VGG19FCN algorithm and ultrasound omics. 120 patients with BC were selected, of which 90 were positive for hormone receptor and 30 were negative. The VGG19FCN model algorithm and classifier were selected to classify the features of ultrasound breast map, and reliable ultrasound feature data were obtained. The evaluation and analysis of BC hormone receptor expression and clinical efficacy in patients with BC were realized by using ultrasonic omics. The evaluation of the results of the VGG19FCN algorithm was DSC (Dice similarity coefficient) = 0.9626, MPA (mean pixel accuracy) = 0.9676, and IOU (intersection over union) = 0.9155. When the classifier was used to classify the lesion features of BC image, the sensitivity of classification was improved to a certain extent. Compared with the classification of radiologists, when classifying whether patients had BC lesions, the sensitivity increased by 22.7%, the accuracy increased from 71.9% to 79.7%, and the specific evaluation index increased by 0.8%. No substantial difference was indicated between RT (arrive time), WIS (wash in slope), and TTP (time to peak) before and after chemotherapy, > 0.05. After chemotherapy, the AUC (area under curve) and PI (peak intensity) of ultrasonographic examination were substantially lower than those before chemotherapy, and there were substantial differences in statistics ( < 0.05). In summary, the VGG19FCN algorithm effectively reduces the subjectivity of traditional ultrasound images and can effectively improve the value of ultrasound image features in the accurate diagnosis of BC. It provides a theoretical basis for the subsequent treatment of BC and the prediction of biological behavior. The VGG19FCN algorithm had a good performance in ultrasound image processing of BC patients, and hormone receptor expression changed substantially after chemotherapy treatment.
研究了 VGG19FCN 算法和超声组学下乳腺癌（BC）激素表达和疗效的变化。选择了 120 例 BC 患者，其中 90 例激素受体阳性，30 例激素受体阴性。选择 VGG19FCN 模型算法和分类器对超声乳腺图的特征进行分类，得到可靠的超声特征数据。利用超声组学实现 BC 激素受体表达及临床疗效评价分析。VGG19FCN 算法评价结果为 DSC（Dice 相似系数）=0.9626、MPA（平均像素准确率）=0.9676、IOU（交并比）=0.9155。当分类器用于分类 BC 图像的病变特征时，分类的敏感性得到了一定程度的提高。与放射科医生的分类相比，在分类患者是否存在 BC 病变时，敏感性提高了 22.7%，准确率从 71.9%提高到 79.7%，特异性评估指标提高了 0.8%。化疗前后 RT（到达时间）、WIS（洗脱斜率）和 TTP（达峰时间）无明显差异， >0.05。化疗后，超声检查的 AUC（曲线下面积）和 PI（峰强度）显著低于化疗前，统计差异显著（ <0.05）。综上所述，VGG19FCN 算法有效降低了传统超声图像的主观性，能有效提高 BC 超声图像特征的诊断价值。为 BC 后续治疗及生物行为预测提供了理论依据。VGG19FCN 算法在 BC 患者的超声图像处理中性能良好，化疗后激素受体表达发生显著变化。
