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基于超声影像组学鉴别早期与中晚期子宫内膜癌

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目的 观察超声影像组学鉴别早期与中晚期子宫内膜癌(EC)的价值.方法 回顾性分析294例女性EC患者,包括早期196例、中晚期98例;按7∶3随机将其分为训练集(n=206)与验证集(n=88).比较早期与中晚期患者临床资料并构建临床模型;基于超声资料提取并筛选影像组学特征,分别采用逻辑回归(LR)、随机森林(RF)、支持向量机(SVM)、高斯朴素贝叶斯(GNB)及极限梯度提升(XGBoost)构建影像组学模型;最后构建临床-影像组学联合模型.观察各模型鉴别早期与中晚期EC的效能.结果 早期与中晚期EC患者就诊年龄、月经紊乱、腹痛及绝经占比在训练集和验证集差异均有统计学意义(P均<0.05).5个影像组学模型中,RF模型鉴别早期与中晚期EC的曲线下面积(AUC)最大.临床模型、RF影像组学模型及临床-RF影像组学模型两两比较AUC差异均有统计学意义(P均<0.05),尤以临床-RF影像组学模型的AUC最高.结论 基于RF的超声影像组学有助于鉴别早期与中晚期EC,进一步联合临床资料可提高诊断效能.
Ultrasound radiomics for distinguishing early and middle-late stage endometrial cancer
Objective To observe the value of ultrasound radiomics for distinguishing early and middle-late stage endometrial cancer(EC).Methods A total of 294 women with EC were retrospectively enrolled,including 196 in early stage and 98 in middle-late stage.The patients were randomly divided into training set(n=206)and validation set(n=88)at the ratio of 7∶3.Clinical data were compared between different stages,and a clinical model was constructed.Radiomics features were extracted and screened based on ultrasound data,and radiomics models were constructed with logistic regression(LR),random forest(RF),support vector machine(SVM),Gaussian naive Bayes(GNB)and extreme gradient boosting(XGBoost),respectively.Finally,a clinical-radiomics model was constructed.The value of each model for distinguishing early and middle-late stages EC was observed.Results Significant differences of age of consultation,menstrual disorders,abdominal pain and proportion of menopause were found between patients with early and middle-late stage EC(all P<0.05).Among these 5 radiomics models,RF model had the highest area under the curve(AUC)for distinguishing early and middle-late stage EC.Pairwise comparison of clinical model,RF radiomics model and clinical-RF radiomics model showed that significant differences of AUC were found between each 2 models(all P<0.05),and clinical-RF radiomics model had the highest AUC.Conclusion Ultrasound radiomics based on RF were helpful for distinguishing early and middle-late stage EC,and better diagnostic efficacy could be obtained through combining with clinical data.

endometrial neoplasmsneoplasm stagingultrasonographymachine learningradiomics

彭小莉、王雪莹、赵露、王诗淳、罗梦琳、任琳、张茂春

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川北医学院附属医院超声科,四川 南充 637000

川北医学院附属医院健康管理中心,四川 南充 637000

子宫内膜肿瘤 肿瘤分期 超声检查 机器学习 影像组学

2024

中国医学影像技术
中国科学院声学研究所

中国医学影像技术

CSTPCD北大核心
影响因子:0.763
ISSN:1003-3289
年,卷(期):2024.40(11)