Whole-lesion-aware network based on freehand ultrasound video for breast cancer assessment: a prospective multicenter study.

IF 3.5 2区医学 Q2 ONCOLOGY

Cancer Imaging Pub Date : 2025-06-16 DOI:10.1186/s40644-025-00892-y

Jie Han, Yuanjing Gao, Ling Huo, Dong Wang, Xiaozheng Xie, Rui Zhang, Mengsu Xiao, Nan Zhang, Meng Lei, Quanlin Wu, Lu Ma, Chao Sun, Xinyi Wang, Lei Liu, Shuzhen Cheng, Binghui Tang, Liwei Wang, Qingli Zhu, Yong Wang

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引用次数: 0

Abstract

Background: The clinical application of artificial intelligence (AI) models based on breast ultrasound static images has been hindered in real-world workflows due to operator-dependence of standardized image acquisition and incomplete view of breast lesions on static images. To better exploit the real-time advantages of ultrasound and more conducive to clinical application, we proposed a whole-lesion-aware network based on freehand ultrasound video (WAUVE) scanning in an arbitrary direction for predicting overall breast cancer risk score.

Methods: The WAUVE was developed using 2912 videos (2912 lesions) of 2771 patients retrospectively collected from May 2020 to August 2022 in two hospitals. We compared the diagnostic performance of WAUVE with static 2D-ResNet50 and dynamic TimeSformer models in the internal validation set. Subsequently, a dataset comprising 190 videos (190 lesions) from 175 patients prospectively collected from December 2022 to April 2023 in two other hospitals, was used as an independent external validation set. A reader study was conducted by four experienced radiologists on the external validation set. We compared the diagnostic performance of WAUVE with the four experienced radiologists and evaluated the auxiliary value of model for radiologists.

Results: The WAUVE demonstrated superior performance compared to the 2D-ResNet50 model, while similar to the TimeSformer model. In the external validation set, WAUVE achieved an area under the receiver operating characteristic curve (AUC) of 0.8998 (95% CI = 0.8529-0.9439), and showed a comparable diagnostic performance to that of four experienced radiologists in terms of sensitivity (97.39% vs. 98.48%, p = 0.36), specificity (49.33% vs. 50.00%, p = 0.92), and accuracy (78.42% vs.79.34%, p = 0.60). With the WAUVE model assistance, the average specificity of four experienced radiologists was improved by 6.67%, and higher consistency was achieved (from 0.807 to 0.838).

Conclusion: The WAUVE based on non-standardized ultrasound scanning demonstrated excellent performance in breast cancer assessment which yielded outcomes similar to those of experienced radiologists, indicating the clinical application of the WAUVE model promising.

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基于手绘超声视频的全病变感知网络用于乳腺癌评估：一项前瞻性多中心研究。

背景：基于乳腺超声静态图像的人工智能（AI）模型的临床应用在现实工作流程中一直受到阻碍，原因是标准化图像采集对操作者的依赖以及静态图像对乳腺病变的不完整视图。为了更好地发挥超声的实时性优势，更有利于临床应用，我们提出了一种基于任意方向徒手超声视频（WAUVE）扫描的全病变感知网络，用于预测乳腺癌总体风险评分。方法：回顾性收集两家医院2020年5月至2022年8月2771例患者的2912个影像（2912个病灶），建立WAUVE。我们在内部验证集中比较了WAUVE与静态2D-ResNet50和动态TimeSformer模型的诊断性能。随后，从2022年12月至2023年4月在另外两家医院前瞻性收集的175名患者的190个视频（190个病变）数据集被用作独立的外部验证集。由四位经验丰富的放射科医生对外部验证集进行了读者研究。我们比较了WAUVE与四位经验丰富的放射科医生的诊断表现，并评估了模型对放射科医生的辅助价值。结果：与2D-ResNet50模型相比，WAUVE表现出优越的性能，而与TimeSformer模型相似。在外部验证集中，WAUVE的受试者工作特征曲线下面积（AUC）为0.8998 (95% CI = 0.8529-0.9439)，在敏感性（97.39% vs. 98.48%, p = 0.36）、特异性（49.33% vs. 50.00%, p = 0.92）和准确性（78.42% vs.79.34%, p = 0.60）方面与四位经验丰富的放射科医生的诊断表现相当。在WAUVE模型的辅助下，4名经验丰富的放射科医生的平均特异性提高了6.67%，一致性更高（从0.807提高到0.838）。结论：基于非标准化超声扫描的WAUVE在乳腺癌评估中表现优异，其结果与经验丰富的放射科医生相似，表明WAUVE模型的临床应用前景广阔。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

CiteScore

7.00

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.