UNASSIGNED: From January 2018 to January 2020, 153 women with clinically and pathologically diagnosed breast cancer with an average age of 50.08 years were retrospectively analyzed. Using a semiautomatic segmentation method, the whole tumor was divided into 3 subregions on the basis of the time required for the contrast agent to reach its peak; 540 features were extracted from 3 subregions and the whole tumor region. Subsequently, 2 machine learning classifiers were developed. The least absolute shrinkage and selection operator method was used for feature selection and radiomics score (Rad-score) construction. Moreover, multivariable logistic regression analysis was applied to select independent factors from the Rad-score and clinical factors to establish a prediction model in the form of a nomogram. The performance of the nomogram was evaluated through calibration, discrimination, and clinical usefulness.
UNASSIGNED: The prediction performance of texture features from the rapid subregion was the best in the 3 intratumoral subregions, and the area under the receiver operating characteristic curve (AUC) values in the training and validation cohort were 0.805 (95% CI: 0.719-0.892) and 0.737 (95% CI: 0.581-0.893), respectively. The Rad-score, consisting of 5 features from the rapid subregion, was associated with the luminal type of breast cancer (P=0.001 and P=0.035 in the training and validation cohorts, respectively). The predictors included in the personalized prediction nomogram included Rad-score, human epidermal growth factor receptor 2 (HER2) status, and tumor histological grade. The nomogram showed good discrimination, with an area under the receiver operating characteristic curve in the training and validation cohorts of 0.830 (95% CI: 0.746-0.896) and 0.879 (95% CI: 0.748-0.957), respectively. The calibration curve of the 2 cohorts and decision curve analysis demonstrated that the nomogram had good calibration and clinical usefulness.
UNASSIGNED: We proposed a nomogram model that combined clinical factors and Rad-score, which showed good performance in predicting the luminal type of breast cancer.
■回顾性分析2018年1月至2020年1月153例经临床和病理诊断为乳腺癌的女性,平均年龄为50.08岁。使用半自动分割方法,根据造影剂达到峰值所需的时间,将整个肿瘤分为3个子区域;从3个子区域和整个肿瘤区域中提取540个特征。随后,开发了2个机器学习分类器。使用最小绝对收缩和选择算子方法进行特征选择和放射组学评分(Rad-score)构建。此外,应用多变量logistic回归分析,从Rad评分和临床因素中选择独立因素,以列线图的形式建立预测模型。通过校准评估列线图的性能,歧视,和临床有用性。
■来自快速子区域的纹理特征的预测性能在3个肿瘤内子区域中最好,训练和验证队列中的受试者工作特征曲线下面积(AUC)值分别为0.805(95%CI:0.719-0.892)和0.737(95%CI:0.581-0.893),分别。Rad-score,由快速分区的5个特征组成,与乳腺癌的管腔类型相关(训练和验证队列中P=0.001和P=0.035,分别)。个性化预测列线图中包括的预测因子包括Rad分数,人表皮生长因子受体2(HER2)状态,和肿瘤组织学分级。列线图显示出很好的辨别力,训练和验证队列中的受试者工作特征曲线下面积为0.830(95%CI:0.746-0.896)和0.879(95%CI:0.748-0.957),分别。2个队列的校准曲线和决策曲线分析表明,列线图具有良好的校准和临床实用性。
■我们提出了一个结合临床因素和Rad评分的列线图模型,在预测乳腺癌的管腔类型方面表现良好。