机构地区:[1]中国人民解放军总医院第六医学中心耳鼻咽喉头颈外科医学部,解放军医学院,国家耳鼻咽喉疾病临床医学研究中心,北京100853 [2]浙江清华长三角研究院
出 处:《临床耳鼻咽喉头颈外科杂志》2024年第6期547-552,共6页Journal of Clinical Otorhinolaryngology Head And Neck Surgery
基 金:国家重点研发计划项目(No:2022YFC2703602);国家自然科学基金资助项目(No:61827805)。
摘 要:目的评估传统放射组学、深度学习以及深度学习放射组学特征融合模型在颞骨计算机断层扫描(CT)上诊断内耳畸形的效能。方法回顾性收集572耳颞骨CT数据,其中包含201耳畸形内耳和371耳正常内耳,按照4∶1的比例将其随机分为训练集(n=458)和测试集(n=114)。从上述颞骨CT图像中提取深度迁移学习特征和放射组学特征,并进行特征融合。以来自国家耳鼻咽喉疾病临床研究中心的2名耳科主任医师的CT判读结果作为诊断标准。使用受试者工作特征曲线(ROC)评估模型性能,计算模型的准确率、灵敏度、特异度等指标,并使用德龙检验比较模型的预测能力。结果从传统放射组学中获得1179个放射组学特征,从深度学习中获得2048个深度学习特征,在对两者进行特征筛选和融合后获得137个融合特征。深度学习放射组学特征融合模型在测试集上的AUC为0.9640(95%CI 0.9314~0.9968),准确率为0.922,灵敏度为0.881、特异度为0.945。单纯放射组学特征在测试集上的AUC为0.9290(95%CI0.8822~0.9749),准确率为0.878,灵敏度为0.881,特异度为0.877。深度学习特征在测试集上的AUC为0.9470(95%CI 0.8982~0.9948),准确率为0.913,灵敏度为0.810,特异度为0.973。即深度学习放射组学特征融合模型的预测准确率和AUC均最高。德龙检验表明,任何2种模型之间的差异均无统计学意义。结论特征融合模型可用于正常和内耳畸形的鉴别诊断,与单独使用放射组学或深度学习模型相比,其诊断效能有所提高。Objective To evaluate the diagnostic efficacy of traditional radiomics,deep learning,and deep learning radiomics in differentiating normal and inner ear malformations on temporal bone computed tomography(CT).Methods A total of 572 temporal bone CT data were retrospectively collected,including 201 cases of inner ear malformation and 371 cases of normal inner ear,and randomly divided into a training cohort(n=458)and a test cohort(n=114)in a ratio of 4∶1.Deep transfer learning features and radiomics features were extracted from the CT images and feature fusion was performed to establish the least absolute shrinkage and selection operator.The CT results interpretated by two chief otologists from the National Clinical Research Center for Otorhinolaryngological Diseases served as the gold standard for diagnosis.The model performance was evaluated using receiver operating characteristic(ROC),and the accuracy,sensitivity,specificity,and other indicators of the models were calculated.The predictive power of each model was compared using the Delong test.Results 1179 radiomics features were obtained from traditional radiomics,2048 deep learning features were obtained from deep learning,and 137 features fusion were obtained after feature screening and fusion of the two.The area under the curve(AUC)of the deep learning radiomics model on the test cohort was 0.9640(95%CI 0.9314-0.9968),with an accuracy of 0.922,sensitivity of 0.881,and specificity of 0.945.The AUC of the radiomics features alone on the test cohort was 0.9290(95%CI 0.8822-0.9749),with an accuracy of 0.878,sensitivity of 0.881,and specificity of 0.877.The AUC of the deep learning features alone on the test cohort was 0.9470(95%CI 0.8982-0.9948),with an accuracy of 0.913,sensitivity of 0.810,and specificity of 0.973.The results indicated that the prediction accuracy and AUC of the deep learning radiomics model are the highest.The Delong test showed that the differences between any two models did not reach statistical significance.Conclusion The feature fusion m
关 键 词:内耳畸形 深度学习 放射组学 计算机断层扫描 鉴别诊断
分 类 号:R764[医药卫生—耳鼻咽喉科]
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