| 周汇明,肖际东,刘梦涵,聂淼淼,戴美雪.基于乳腺二维超声及自动乳腺容积扫描构建影像组学及列线图模型预测乳腺癌分子分型[J].中国医学影像技术,2024,40(1):55~61 |
| 基于乳腺二维超声及自动乳腺容积扫描构建影像组学及列线图模型预测乳腺癌分子分型 |
| Radiomics and nomogram models based on two-dimensional ultrasound and automated breast volume scanning for predicting molecular types of breast cancer |
| 投稿时间：2023-07-13 修订日期：2023-12-01 |
| DOI：10.13929/j.issn.1003-3289.2024.01.011 |
| 中文关键词: 乳腺肿瘤 超声检查 影像组学 分子分型 |
| 英文关键词:breast neoplasms ultrasonography radiomics molecular typing |
| 基金项目:湖南省自然科学基金（2019JJ40459）、湖南省卫生健康委员会课题（B2019177）。 |
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| 中文摘要: |
| 目的 观察基于乳腺二维超声及自动乳腺容积扫描（ABVS）构建的影像组学及列线图模型预测乳腺癌分子分型的价值。方法 回顾性分析326例经病理证实的女性单发乳腺癌患者资料，以8 ∶ 2比例将其分为训练集（n=260）及验证集（n=66），根据免疫组织化学结果划分Luminal与非Luminal亚组；基于乳腺二维超声及ABVS图像提取影像组学特征构建相应模型及联合模型。采用单因素及多因素logistic回归分析筛选乳腺癌分子分型的独立预测因素，联合影像组学评分构建列线图模型。绘制受试者工作特征（ROC）曲线，评估各模型预测乳腺癌分子分型的效能。结果 肿瘤最大径（OR=1.029）及有无汇聚征（OR=0.408）均为乳腺癌分子分型的独立预测因素（P均<0.05）。二维超声、ABVS、联合影像组学模型及列线图模型预测验证集乳腺癌分子分型的曲线下面积（AUC）分别为0.67、0.75、0.84及0.83，其中，联合影像组学模型与列线图模型AUC差异无统计学意义（P>0.05）并均高于二维超声及ABVS模型（P均<0.05）。结论 基于二维超声及ABVS构建的联合影像组学模型及列线图模型均可有效预测乳腺癌分子分型。 |
| 英文摘要: |
| Objective To observe the value of radiomics models and nomogram model based on two-dimensional ultrasound and automated breast volume scanning (ABVS) for predicting molecular types of breast cancer. Methods Data of 326 female patients of single breast cancer confirmed by pathology were analyzed retrospectively. The patients were randomly divided into training set (n=260) or validation set (n=66) at the ratio of 8:2, and further divided into Luminal subgroup and non-Luminal subgroup. Radiomics features were extracted based on two-dimensional ultrasound of breast and ABVS imaging, then model2DUS,modelABVS and modelcombined radiomics were constructed, respectively. Univariate analysis and multivariate logistic regression analysis were used to screen independent factors for predicting molecular types of breast cancer, and nomogram model(modelnomogram) was constructed combined with independent factors and radiomics Radscores. The receiver operating characteristic (ROC) curve was used to evaluate the efficacy of each model for molecular type of breast cancer. Results The maximum diameter of tumor (OR=1.029) and the retraction phenomenon (OR=0.408) were both independent predictive factors for molecular type of breast cancer (both P<0.05). The area under the curve (AUC) of model2DUS, modelABVS, modelcombined radiomics and modelnomogram for predicting molecular type of breast cancer in validation set was 0.67, 0.75, 0.84 and 0.83, respectively. No significant difference of AUC of modelcombined radiomics and modelnomogram was found (P>0.05), which were both higher than AUC of model2DUS and modelABVS(all P<0.05). Conclusion Combined radiomics model and nomogram model based on two-dimensional ultrasound and ABVS could effectively predict molecular type of breast cancer. |
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