Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China.
Wenling Big Data and Artificial Intelligence Institute in Medicine, TaiZhou 317502, China.
Br J Radiol. 2024 Nov 1;97(1163):1816-1825. doi: 10.1093/bjr/tqae147.
To evaluate the performance of ultrasound-based deep learning (DL) models in distinguishing breast phyllodes tumours (PTs) from fibroadenomas (FAs) and their clinical utility in assisting radiologists with varying diagnostic experiences.
We retrospectively collected 1180 ultrasound images from 539 patients (247 PTs and 292 FAs). Five DL network models with different structures were trained and validated using nodule regions annotated by radiologists on breast ultrasound images. DL models were trained using the methods of transfer learning and 3-fold cross-validation. The model demonstrated the best evaluation index in the 3-fold cross-validation was selected for comparison with radiologists' diagnostic decisions. Two-round reader studies were conducted to investigate the value of DL model in assisting 6 radiologists with different levels of experience.
Upon testing, Xception model demonstrated the best diagnostic performance (area under the receiver-operating characteristic curve: 0.87; 95% CI, 0.81-0.92), outperforming all radiologists (all P < .05). Additionally, the DL model enhanced the diagnostic performance of radiologists. Accuracy demonstrated improvements of 4%, 4%, and 3% for senior, intermediate, and junior radiologists, respectively.
The DL models showed superior predictive abilities compared to experienced radiologists in distinguishing breast PTs from FAs. Utilizing the model led to improved efficiency and diagnostic performance for radiologists with different levels of experience (6-25 years of work).
We developed and validated a DL model based on the largest available dataset to assist in diagnosing PTs. This model has the potential to allow radiologists to discriminate 2 types of breast tumours which are challenging to identify with precision and accuracy, and subsequently to make more informed decisions about surgical plans.
评估基于超声的深度学习（DL）模型在区分乳腺叶状肿瘤（PTs）和纤维腺瘤（FAs）方面的性能及其在辅助具有不同诊断经验的放射科医生方面的临床应用价值。
我们回顾性地收集了来自 539 名患者（247 例 PTs 和 292 例 FA）的 1180 个超声图像。使用放射科医生在乳腺超声图像上标注的结节区域，对 5 种具有不同结构的 DL 网络模型进行了训练和验证。使用迁移学习和 3 折交叉验证的方法对 DL 模型进行训练。在 3 折交叉验证中，选择表现最佳的评估指标的模型与放射科医生的诊断决策进行比较。进行了两轮读者研究，以研究 DL 模型在辅助 6 名具有不同经验水平的放射科医生方面的价值。
在测试中，Xception 模型表现出最佳的诊断性能（受试者工作特征曲线下面积：0.87；95%置信区间，0.81-0.92），优于所有放射科医生（均 P < .05）。此外，DL 模型提高了放射科医生的诊断性能。对于高级、中级和初级放射科医生，准确性分别提高了 4%、4%和 3%。
与经验丰富的放射科医生相比，DL 模型在区分乳腺 PTs 和 FAs 方面具有更高的预测能力。使用该模型可以提高具有不同经验水平（6-25 年工作经验）的放射科医生的效率和诊断性能。
我们开发并验证了一种基于最大可用数据集的 DL 模型，以协助诊断 PTs。该模型有可能使放射科医生能够区分两种具有挑战性的乳腺肿瘤，从而更准确地做出手术计划决策。
