Deep Learning Based on Ultrasound Images Differentiates Parotid Gland Pleomorphic Adenomas and Warthin Tumors.

IF 2.5 4区 医学 Q1 ACOUSTICS
Yajuan Li, Mingchi Zou, Xiaogang Zhou, Xia Long, Xue Liu, Yanfeng Yao
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Abstract

Exploring the clinical significance of employing deep learning methodologies on ultrasound images for the development of an automated model to accurately identify pleomorphic adenomas and Warthin tumors in salivary glands. A retrospective study was conducted on 91 patients who underwent ultrasonography examinations between January 2016 and December 2023 and were subsequently diagnosed with pleomorphic adenoma or Warthin's tumor based on postoperative pathological findings. A total of 526 ultrasonography images were collected for analysis. Convolutional neural network (CNN) models, including ResNet18, MobileNetV3Small, and InceptionV3, were trained and validated using these images for the differentiation of pleomorphic adenoma and Warthin's tumor. Performance evaluation metrics such as receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value were utilized. Two ultrasound physicians, with varying levels of expertise, conducted independent evaluations of the ultrasound images. Subsequently, a comparative analysis was performed between the diagnostic outcomes of the ultrasound physicians and the results obtained from the best-performing model. Inter-rater agreement between routine ultrasonography interpretation by the two expert ultrasonographers and the automatic identification diagnosis of the best model in relation to pathological results was assessed using kappa tests. The deep learning models achieved favorable performance in differentiating pleomorphic adenoma from Warthin's tumor. The ResNet18, MobileNetV3Small, and InceptionV3 models exhibited diagnostic accuracies of 82.4% (AUC: 0.932), 87.0% (AUC: 0.946), and 77.8% (AUC: 0.811), respectively. Among these models, MobileNetV3Small demonstrated the highest performance. The experienced ultrasonographer achieved a diagnostic accuracy of 73.5%, with sensitivity, specificity, positive predictive value, and negative predictive value of 73.7%, 73.3%, 77.8%, and 68.8%, respectively. The less-experienced ultrasonographer achieved a diagnostic accuracy of 69.0%, with sensitivity, specificity, positive predictive value, and negative predictive value of 66.7%, 71.4%, 71.4%, and 66.7%, respectively. The kappa test revealed strong consistency between the best-performing deep learning model and postoperative pathological diagnoses (kappa value: .778, p-value < .001). In contrast, the less-experienced ultrasonographer demonstrated poor consistency in image interpretations (kappa value: .380, p-value < .05). The diagnostic accuracy of the best deep learning model was significantly higher than that of the ultrasonographers, and the experienced ultrasonographer exhibited higher diagnostic accuracy than the less-experienced one. This study demonstrates the promising performance of a deep learning-based method utilizing ultrasonography images for the differentiation of pleomorphic adenoma and Warthin's tumor. The approach reduces subjective errors, provides decision support for clinicians, and improves diagnostic consistency.

基于超声图像的深度学习鉴别腮腺多形性腺瘤和沃辛瘤。
探讨超声图像应用深度学习方法开发唾液腺多形性腺瘤和沃辛瘤自动识别模型的临床意义。回顾性研究了2016年1月至2023年12月期间接受超声检查并根据术后病理结果诊断为多形性腺瘤或Warthin肿瘤的91例患者。共收集526张超声图像进行分析。卷积神经网络(CNN)模型,包括ResNet18、MobileNetV3Small和InceptionV3,使用这些图像进行训练和验证,用于多形性腺瘤和Warthin肿瘤的分化。采用受试者工作特征(ROC)曲线、曲线下面积(AUC)、敏感性、特异性、阳性预测值、阴性预测值等性能评价指标。两位具有不同专业水平的超声医生对超声图像进行了独立评估。随后,将超声医生的诊断结果与最佳模型的结果进行比较分析。采用kappa试验评估两名超声专家的常规超声判读与与病理结果相关的最佳模型的自动识别诊断之间的评分一致性。深度学习模型在多形性腺瘤和Warthin瘤的鉴别上取得了良好的表现。ResNet18、MobileNetV3Small和InceptionV3模型的诊断准确率分别为82.4% (AUC: 0.932)、87.0% (AUC: 0.946)和77.8% (AUC: 0.811)。在这些模型中,MobileNetV3Small表现出最高的性能。经验丰富的超声医师诊断准确率为73.5%,敏感性73.7%,特异性73.3%,阳性预测值77.8%,阴性预测值68.8%。经验不足的超声医师诊断准确率为69.0%,敏感性为66.7%,特异性为71.4%,阳性预测值为71.4%,阴性预测值为66.7%。kappa检验显示,表现最好的深度学习模型与术后病理诊断之间具有很强的一致性(kappa值:.778,p值p值
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来源期刊
Ultrasonic Imaging
Ultrasonic Imaging 医学-工程:生物医学
CiteScore
5.10
自引率
8.70%
发文量
15
审稿时长
>12 weeks
期刊介绍: Ultrasonic Imaging provides rapid publication for original and exceptional papers concerned with the development and application of ultrasonic-imaging technology. Ultrasonic Imaging publishes articles in the following areas: theoretical and experimental aspects of advanced methods and instrumentation for imaging
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