| 贾坤,李伟,裴月颖,牛帅.人工智能超声联合血清透明质酸合成酶2(HAS2)及三叶因子1(TFF1)诊断早期乳腺癌[J].中国医学影像技术,2025,41(2):254~257 |
| 人工智能超声联合血清透明质酸合成酶2(HAS2)及三叶因子1(TFF1)诊断早期乳腺癌 |
| Artificial intelligence ultrasound combined with serum hyaluronic acid synthase 2 (HAS2) and trefoil factor 1 (TFF1) for early diagnosis of breast cancer |
| 投稿时间：2024-09-20 修订日期：2024-10-12 |
| DOI：10.13929/j.issn.1003-3289.2025.02.015 |
| 中文关键词: 乳腺肿瘤 超声检查 人工智能 透明质酸合成酶 三叶因子1 |
| 英文关键词:breast neoplasms ultrasonography artificial intelligence hyaluronan synthases trefoil factor-1 |
| 基金项目:卫生部医药卫生科技项目(13010520231488)、河北省2024年度医学科学研究课题(20242378)。 |
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| 中文摘要: |
| 目的 观察人工智能超声联合血清透明质酸合成酶2(HAS2)及三叶因子1(TFF1)诊断乳腺癌的价值。方法 回顾性收集176例疑诊乳腺癌患者,根据病理结果分为恶性组(n=50)与良性组(n=126);以人工智能超声及卷积神经网络算法自动标注乳腺可疑病灶,由医师根据乳腺影像报告和数据系统(BI-RADS)对病灶进行分级,以0~3级为良性、4~5级为恶性。比较组间临床资料及病灶人工智能超声表现;绘制受试者工作特征曲线,计算曲线下面积(AUC),评估HAS2、TFF1、人工智能超声及三者联合诊断乳腺癌的效能。结果 恶性组HAS2、TFF1、腺体厚度异常、病灶低回声及血流形态异常者占比均显著高于良性组(P均＜0.001)。单一根据血清HAS2、TFF1及人工智能超声表现诊断乳腺癌的AUC分别为0.772、0.754及0.859;三者联合的AUC为0.925,其诊断效能高于各单一项(P均＜0.05)。结论 人工智能超声联合血清HAS2及TFF1诊断乳腺癌效能良好。 |
| 英文摘要: |
| Objective To observe the value of artificial intelligence ultrasound combined with serum hyaluronic acid synthase 2 (HAS2) and trefoil factor-1 (TFF1) for early diagnosis of breast cancer. Methods Totally 176 patients with suspected breast cancer were retrospective enrolled and divided into malignant group (n=50) and benign group (n=126) according to pathological results. Artificial intelligence ultrasound and convolutional neural network algorithms were used to automatically label suspicious breast lesions. The lesions were manually graded based on breast imaging reports and data systems, classifying 0—3 grades as benign lesions, 4—5 grades as malignant lesions. Clinical data and artificial intelligence ultrasound manifestations were compared between groups. Receiver operating characteristic curve was drawn, the area under the curve (AUC) was calculated to evaluate the efficacy of HAS2, TFF1, artificial intelligence ultrasound and their combination for diagnosing breast cancer. Results HAS2, TFF1, as well as the proportions of abnormal glandular thickness, low-echo lesions and abnormal blood flow morphology in malignant group were all higher than those in benign group (all P＜0.001). AUC of serum HAS2, TFF1 and artificial intelligence ultrasound for diagnosing breast cancer was 0.772, 0.754 and 0.859, respectively. The combined diagnostic efficacy of the above three (AUC=0.925) was higher than single diagnostic efficacy (all P＜0.05). Conclusion Artificial intelligence ultrasound combined with serum HAS2 and TFF1 had good efficacy for early diagnosis of breast cancer. |
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