PDF(Pubmed)
Abstract：
UNASSIGNED: To develop a clinical-radiomics model using a multimodal machine learning method for distinguishing ductal carcinoma in situ (DCIS) from breast fibromatosis.
UNASSIGNED: The clinical factors, ultrasound features, and related ultrasound images of 306 patients (198 DCIS patients) were retrospectively collected. Patients in the development and validation cohort were 184 and 122, respectively. The independent clinical and ultrasound factors identified by the multivariable logistic regression analysis were used for the clinical-ultrasound model construction. Then, the region of interest of breast lesions was delineated and radiomics features were extracted. Six machine learning algorithms were trained to develop a radiomics model. The algorithm with higher and more stable prediction ability was chosen to convert the output of the results into the Radscore. Further, the independent clinical predictors and Radscore were enrolled into the logistic regression analysis to generate a combined clinical-radiomics model. The receiver operating characteristic curve analysis, DeLong test, and decision curve analysis were adopted to compare the prediction ability and clinical efficacy of three different models.
UNASSIGNED: Among the six classifiers, logistic regression model was selected as the final radiomics model. Besides, the combined clinical-radiomics model exhibited a superior ability in distinguishing DCIS from breast fibromatosis to the clinical-ultrasound model and the radiomics model.
UNASSIGNED: The combined model by integrating clinical-ultrasound factors and radiomics features performed well in predicting DCIS, which might promote prompt interventions to improve the early diagnosis and prognosis of the patients.
UNASSIGNED: The clinical factors, ultrasound features, and related ultrasound images of 306 patients (198 DCIS patients) were retrospectively collected. Patients in the development and validation cohort were 184 and 122, respectively. The independent clinical and ultrasound factors identified by the multivariable logistic regression analysis were used for the clinical-ultrasound model construction. Then, the region of interest of breast lesions was delineated and radiomics features were extracted. Six machine learning algorithms were trained to develop a radiomics model. The algorithm with higher and more stable prediction ability was chosen to convert the output of the results into the Radscore. Further, the independent clinical predictors and Radscore were enrolled into the logistic regression analysis to generate a combined clinical-radiomics model. The receiver operating characteristic curve analysis, DeLong test, and decision curve analysis were adopted to compare the prediction ability and clinical efficacy of three different models.
UNASSIGNED: Among the six classifiers, logistic regression model was selected as the final radiomics model. Besides, the combined clinical-radiomics model exhibited a superior ability in distinguishing DCIS from breast fibromatosis to the clinical-ultrasound model and the radiomics model.
UNASSIGNED: The combined model by integrating clinical-ultrasound factors and radiomics features performed well in predicting DCIS, which might promote prompt interventions to improve the early diagnosis and prognosis of the patients.
摘要：
■使用多模式机器学习方法开发临床影像组学模型，以区分导管原位癌（DCIS）与乳腺纤维瘤病。
■临床因素，超声特征，回顾性收集306例患者(198例DCIS患者)的相关超声图像。开发和验证队列中的患者分别为184和122。通过多变量逻辑回归分析确定的独立临床和超声因素用于临床超声模型的构建。然后，我们勾画了乳腺病变的感兴趣区域,并提取了影像组学特征.训练了六种机器学习算法以开发影像组学模型。选择具有更高和更稳定预测能力的算法将结果的输出转换为Radscore。Further,将独立的临床预测因子和Radscore纳入logistic回归分析,以生成联合临床-影像组学模型.接收机工作特性曲线分析,DeLong测试,采用决策曲线分析比较3种不同模型的预测能力和临床疗效。
■在六个分类器中，选择logistic回归模型作为最终的影像组学模型。此外,与临床超声模型和影像组学模型相比,临床-影像组学联合模型在区分DCIS和乳腺纤维瘤病方面表现出优异的能力.
■通过整合临床超声因素和影像组学特征的组合模型在预测DCIS方面表现良好，这可能会促进及时干预，以改善患者的早期诊断和预后。
■临床因素，超声特征，回顾性收集306例患者(198例DCIS患者)的相关超声图像。开发和验证队列中的患者分别为184和122。通过多变量逻辑回归分析确定的独立临床和超声因素用于临床超声模型的构建。然后，我们勾画了乳腺病变的感兴趣区域,并提取了影像组学特征.训练了六种机器学习算法以开发影像组学模型。选择具有更高和更稳定预测能力的算法将结果的输出转换为Radscore。Further,将独立的临床预测因子和Radscore纳入logistic回归分析,以生成联合临床-影像组学模型.接收机工作特性曲线分析,DeLong测试,采用决策曲线分析比较3种不同模型的预测能力和临床疗效。
■在六个分类器中，选择logistic回归模型作为最终的影像组学模型。此外,与临床超声模型和影像组学模型相比,临床-影像组学联合模型在区分DCIS和乳腺纤维瘤病方面表现出优异的能力.
■通过整合临床超声因素和影像组学特征的组合模型在预测DCIS方面表现良好，这可能会促进及时干预，以改善患者的早期诊断和预后。
