| 彭小莉,王雪莹,赵露,王诗淳,罗梦琳,任琳,张茂春.基于超声影像组学鉴别早期与中晚期子宫内膜癌[J].中国医学影像技术,2024,40(11):1739~1744 |
| 基于超声影像组学鉴别早期与中晚期子宫内膜癌 |
| Ultrasound radiomics for distinguishing early and middle-late stage endometrial cancer |
| 投稿时间：2024-04-25 修订日期：2024-08-29 |
| DOI：10.13929/j.issn.1003-3289.2024.11.022 |
| 中文关键词: 子宫内膜肿瘤 肿瘤分期 超声检查 机器学习 影像组学 |
| 英文关键词:endometrial neoplasms neoplasm staging ultrasonography machine learning radiomics |
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| 中文摘要: |
| 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[DK (]:[DK)]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。 |
| 英文摘要: |
| 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. |
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