Qu Chen, Xia Fei, Chen Ling, Li Hong-Jian, Li Wei-Min
Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People's Republic of China.
Department of Ultrasonography, Huai'an Cancer Hospital, Huai'an, Jiangsu, People's Republic of China.
Int J Gen Med. 2024 Sep 14;17:4061-4069. doi: 10.2147/IJGM.S479969. eCollection 2024.
: To explore the diagnostic value of artificial intelligence (AI)-based on real-time dynamic ultrasound imaging system for minimal breast lesions.
Minimal breast lesions with a maximum diameter of ≤10mm were selected in this prospective study. The ultrasound equipment and AI system were activated Simultaneously. The ultrasound imaging video is connected to the server of AI system to achieve simultaneous output of AI and ultrasound scanning. Dynamic observation of breast lesions was conducted via ultrasound. And these lesions were evaluated and graded according to the Breast Imaging Reporting and Data System (BI-RADS) classification system through deep learning (DL) algorithms in AI. Surgical pathology was taken as the gold standard, and ROC curves were drawn to determine the area under the curve (AUC) and the optimal threshold values of BI-RADS. The diagnostic efficacy was compared with the use of a BI-RADS category >3 as the threshold for clinically intervening in diagnosing minimal breast cancers.
291 minimal breast lesions were enrolled in the study, of which 228 were benign (78.35%) and 63 were malignant (21.65%). The AUC of the ROC curve was 0.833, with the best threshold value >4A. When using >BI-RADS 3 and >BI-RADS 4A as threshold values, the sensitivity and negative predictive value for minimal breast cancers were higher for >BI-RADS 3 than >BI-RADS 4A (100% vs 65.08%, 100% vs 89.91%, P values <0.001). However, the corresponding specificity, positive predictive value, and accuracy were lower than those for >BI-RADS 4A (42.11% vs 85.96%, 32.31% vs 56.16%, and 54.64% vs 81.44%, P values <0.001).
The AI-based real-time dynamic ultrasound imaging system shows good capacity in diagnosing minimal breast lesions, which is helpful for early diagnosis and treatment of breast cancer, and improves the prognosis of patients. However, it still results in some missed diagnoses and misdiagnoses of minimal breast cancers.
探讨基于人工智能（AI）的实时动态超声成像系统对乳腺微小病变的诊断价值。
本前瞻性研究选取最大直径≤10mm的乳腺微小病变。同时启动超声设备和AI系统。将超声成像视频连接至AI系统服务器，以实现AI与超声扫描同步输出。通过超声对乳腺病变进行动态观察。并通过AI中的深度学习（DL）算法，根据乳腺影像报告和数据系统（BI-RADS）分类系统对这些病变进行评估和分级。以手术病理作为金标准，绘制ROC曲线以确定曲线下面积（AUC）及BI-RADS的最佳阈值。将诊断效能与以BI-RADS类别>3作为临床干预诊断乳腺微小癌阈值的情况进行比较。
本研究纳入291例乳腺微小病变，其中228例为良性（78.35%），63例为恶性（21.65%）。ROC曲线的AUC为0.833，最佳阈值>4A。当以>BI-RADS 3和>BI-RADS 4A作为阈值时，对于乳腺微小癌，>BI-RADS 3的灵敏度和阴性预测值高于>BI-RADS 4A（100%对65.08%，100%对89.91%，P值<0.001）。然而，相应的特异度、阳性预测值和准确度低于>BI-RADS 4A（42.11%对85.96%，32.31%对56.16%，54.64%对81.44%，P值<0.001）。
基于AI的实时动态超声成像系统在诊断乳腺微小病变方面显示出良好的能力，有助于乳腺癌的早期诊断和治疗，并改善患者预后。然而，对于乳腺微小癌仍会导致一些漏诊和误诊。
