基于三维超声视频的深度学习模型辅助不同年资医师鉴别乳腺肿块良恶性的应用价值

doi:10.16150/j.1671-2870.2025.03.010

摘要/Abstract

摘要：

目的：建立基于乳腺三维超声视频的深度学习模型（deep learning model based on three-dimensional ultrasound videos，DL-3DUV），并探讨该模型辅助不同年资医师鉴别乳腺肿块良恶性的应用价值。方法：采用ResNet50模型作为骨干网，建立乳腺肿块良恶性的分类模型（DL-3DUV），回顾性纳入400例乳腺肿块患者（共525个病灶）的自动乳腺全容积成像（automated breast volume scanner，ABVS）动态视频，按8∶2的比例随机分为训练集和测试集，比较DL-3DUV与高年资医师、低年资医师独立或联合诊断的效能。结果：独立诊断时，DL-3DUV与高年资医师比较，灵敏度（83.33%比81.77%）、准确率（82.50% vs. 84.60%）和受试者操作特征曲线的曲线下面积（area under the curve，AUC）（0.83比0.85）相当（P均>0.05），仅特异度较低（81.58%比87.73%）（P<0.05）；但DL-3DUV与低年资医师比较，灵敏度（83.33%比78.60%）、特异度（81.58%比57.00%）、准确率（82.50%比68.37%）和AUC（0.83比0.68）的差异均有统计学意义（P<0.05）。高年资医师联合DL-3DUV诊断的灵敏度和AUC高于高年资医师独立诊断（89.70%比81.77%；0.91比0.85，P均<0.05），而特异度和准确率的差异无统计学意义（91.23%比87.73%；89.17%比84.60%，P均>0.05）；低年资医师联合DL-3DUV诊断的诊断效能高于低年资医师独立诊断，灵敏度（88.10%比78.60%）、特异度（82.47%比57.00%）、准确率（85.47%比68.37%）和AUC（0.85比0.68）的差异均有统计学意义（P均<0.05）。结论：DL-3DUV在鉴别乳腺肿块良恶性中具有重要价值，有望成为临床辅助超声医师，尤其是低年资医师的有用工具。

关键词: 自动乳腺全容积成像, 乳腺癌, 深度学习

Abstract:

Objective To establish a deep learning model based on 3D ultrasound videos (DL-3DUV) and investigate its application value in assisting radiologists with different levels of experience to differentiate benign and malignant breast masses. Methods The ResNet50 model was employed to develop DL-3DUV for the classification of benign and malignant breast masses. A retrospective study was conducted using automated breast volume scanner (ABVS) dynamic videos from 400 patients with breast masses (a total of 525 lesions), which were randomly divided into training and testing sets at an 8∶2 ratio. The diagnostic performance of DL-3DUV was compared with that of senior and junior radiologists, both independently and in combination. Results When diagnosing independently, DL-3DUV demonstrated comparable sensitivi-ty (83.33% vs. 81.77%), accuracy (82.50% vs. 84.60%), and area under the curve (AUC) (0.83 vs. 0.85) compared to senior radiologists (all P>0.05), though its specificity was significantly lower (81.58% vs. 87.73%, P<0.05). Compared with junior radiologists, DL-3DUV showed significantly higher sensitivity (83.33% vs. 78.60%), specificity (81.58% vs. 57.00%), accuracy (82.50% vs. 68.37%), and AUC (0.83 vs. 0.68) (P<0.05). The combination of senior radiologists and DL-3DUV achieved higher sensitivity (89.70% vs. 81.77%) and AUC (0.91 vs. 0.85) than senior radiologists alone (all P<0.05), with no significant differences in specificity (91.23% vs. 87.73%) or accuracy (89.17% vs. 84.60%) (all P>0.05). Similarly, the combination of junior radiologists and DL-3DUV significantly improved diagnostic performance compared to junior radiologists alone, with statistically significant differences in sensitivity (88.10% vs. 78.60%), specificity (82.47% vs. 57.00%), accuracy (85.47% vs. 68.37%), and AUC (0.85 vs. 0.68) (all P<0.05). Conclusions DL-3DUV exhibits significant value in differentiating benign and malignant breast masses and is expected to become a useful tool to assist ultrasonographers, particularly for junior radiologists.

Key words: Automated breast volume scanner, Breast cancer, Deep learning

中图分类号:

郭语清, 王长燕, 刘迎春, 庞芸, 朱霞, 葛睿, 李蔚萍, 张麒, 陈林. 基于三维超声视频的深度学习模型辅助不同年资医师鉴别乳腺肿块良恶性的应用价值[J]. 诊断学理论与实践, 2025, 24(03): 312-319.

GUO Yuqing, WANG Changyan, LIU Yinchun, PANG Yun, ZHU Xia, GE Rui, LI Weiping, ZHANG Qi, CHEN Lin. Application value of a deep learning model based on 3D ultrasound videos in assisting radiologists of different experience levels to differentiate benign and malignant breast masses[J]. Journal of Diagnostics Concepts & Practice, 2025, 24(03): 312-319.

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Diagnostic method	Sensitivity (%)	Specificity (%)	Accurcy (%)	AUC
DL-3DUV	83.33(76.36, 95.30)	81.58(73.09, 90.07)	82.50(74.17, 90.83)	0.83(0.73, 0.92)
Senior radiologists	81.77(73.31, 90.23)	87.73(80.54, 94.92)	84.60(75.95, 93.25)	0.85(0.80, 0.90)
Junior radiologists	78.60(69.61, 87.59)	57.00(46.15, 67.85)	68.37(58.18, 78.56)	0.68(0.61, 0.75)
Senior radiologists+ DL-3DUV	89.70(83.04, 96.31)	91.23(85.03, 97.43)	89.17(82.36, 95.98)	0.91(0.87, 0.95)
Junior radiologists+ DL-3DUV	88.10(81.00, 95.20)	82.47(74.14, 90.80)	85.47(77.75, 93.19)	0.85(0.80, 0.91)
Comparison between radiologists and DL-3DUV
Senior radiologists vs. DL-3DUV（P）	0.500	0.016	0.400	0.071
Junior radiologists vs. DL-3DUV（P）	0.031	<0.001	<0.001	<0.001
Senior radiologists vs. junior radiologists（P）	0.125	<0.001	<0.001	<0.001
Comparison between radiologists in combination with DL-3DUV and radiologists
Senior radiologists+ DL-3DUV vs. senior radiologists（P）	0.001	0.125	0.118	0.001
Junior radiologists+ DL-3DUV vs. junior radiologists（P）	<0.001	<0.001	0.012	<0.001
Comparison between radiologists in combination with DL-3DUV and DL-3DUV
Senior radiologists+ DL-3DUV vs. DL-3DUV（P）	0.004	0.001	0.824	<0.001
Junior radiologists+ DL-3DUV vs. DL-3DUV（P）	0.031	>0.99	0.125	0.007
Comparison of different radiologists in combination with DL-3DUV
Senior radiologists+ DL-3DUV vs. junior radiologists+ DL- 3DUV（P）	0.250	0.002	0.092	0.001