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Abstract：
UNASSIGNED: Breast cancer is a heterogeneous disease with different morphological and biological characteristics. The molecular subtypes of breast cancer are closely related to the treatment and prognosis of patients. In order to predict the luminal type of breast cancer in a noninvasive manner, our study developed and validated a radiomics nomogram combining clinical factors with a radiomics score based on the features of the intratumoral subregion to distinguish between luminal and nonluminal breast cancer.
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.
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.
摘要：
■乳腺癌是一种异质性疾病，具有不同的形态学和生物学特征。乳腺癌的分子亚型与患者的治疗和预后密切相关。为了以无创的方式预测乳腺癌的管腔类型，我们的研究开发并验证了将临床因素与基于瘤内次区域特征的影像组学评分相结合的影像组学列线图,以区分腔内乳腺癌和非腔内乳腺癌.
■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。
■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。
