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作 者:张大伟 王海宝[1] 方杰 李之豪 徐丽艳[1] 余长亮[1] 王万勤[1] 伏树奇[1] 李晓舒[1] Zhang Dawei;Wang Haibao;Fang Jie(Dept of Radiology,The First Affiliated Hospital of Anhui Medical University,Hefei 230022)
机构地区:[1]安徽医科大学第一附属医院放射科,合肥230022
出 处:《安徽医科大学学报》2021年第4期652-655,共4页Acta Universitatis Medicinalis Anhui
基 金:国家自然科学基金(编号:81901726)。
摘 要:回顾性分析经手术病理学证实的203例甲状腺微小结节患者,其中甲状腺微小乳头状癌148例和微小结节性甲状腺肿55例,使用影像组学智能分析软件(AK)对甲状腺微小结节的CT平扫及增强图像进行组学分析,运用6种模型进行评估:Forest模型、SVM模型、KNN模型、Tree模型、Bayes模型及Logistic模型。获取不同模型对于甲状腺良恶性微小结节鉴别诊断的准确度、特异性、敏感度及AUC。平扫和增强扫描Forest模型诊断效能均最佳。平扫训练组诊断准确度99.3%、特异性97.9%、敏感度100%、AUC 0.999,测试组诊断准确度87.9%、特异性81.0%、敏感度91.1%、AUC 0.884。增强训练组诊断准确度100%、特异性100%、敏感度100%、AUC 1.000,测试组诊断准确度82.8%、特异性93.3%、敏感度71.4%、AUC 0.905。CT影像组学Forest模型对甲状腺微小结节的良恶性鉴别诊断具有一定的价值,平扫和增强CT影像组学分别具有较高的敏感度和特异性。203 thyroid micro-nodules pathologically confirmed were retrospectively analysed,including 148 cases of thyroid microcarcinoma and 55 cases of micronodular goiter.The software(AK)was used to perform radiomics analysis on CT plain and enhancement scan images of thyroid micro-nodules.Six models were used for evaluation:Forest model,SVM model,KNN model,Tree model,Bayes model and Logistic model.70%of the cases were randomly selected as the training group,and the testing group was the remaining 30%.The accuracy,specificity,sensitivity and AUC of different models for the differential diagnosis of two types of micro-thyroid nodules were obtained.The diagnostic efficiency of plain scan and enhanced scan were analyzed and Forest model was the best.The diagnostic accuracy,specificity,sensitivity and AUC of the plain scan were 99.3%,97.9%,100%and 0.999,respectivelyfor training group while 87.9%,81%,91.1%and 0.884 for the testing group.For the enhancement scan,those of the training group were all 100%and the test group were 82.8%,93.3%,71.4%and 0.905.For CT radiomics,Forest model is of certain value in the differential diagnosis of benign and malignant thyroid micronodule.Plain scan and enhanced CT have high sensitivity and specificity,respectively.
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