PDF(Pubmed)
Abstract：
BACKGROUND: The purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass mastitis (MM) and invasive breast cancer (IBC).
METHODS: 50 cases of MM and 180 cases of IBC with ultrasound Breast Imaging Reporting and Data System 4 category were recruited (training cohort, n = 161, validation cohort, n = 69). Based on PyRadiomics and ResNet50 extractors, radiomics and deep learning features were extracted, respectively. Based on supervised machine learning methods such as logistic regression, random forest, and support vector machine, as well as unsupervised machine learning methods using K-means clustering analysis, the differences in features between MM and IBC were analyzed to develop DLRN. The performance of DLRN had been evaluated by receiver operating characteristic curve, calibration, and clinical practicality.
RESULTS: Supervised machine learning results showed that compared with radiomics models, especially random forest models, deep learning models were better at recognizing MM and IBC. The area under the curve (AUC) of the validation cohort was 0.84, the accuracy was 0.83, the sensitivity was 0.73, and the specificity was 0.83. Compared to radiomics or deep learning models, DLRN even further improved discrimination ability (AUC of 0.90 and 0.90, accuracy of 0.83 and 0.88 for training and validation cohorts), which had better clinical benefits and good calibratability. In addition, the information heterogeneity of deep learning features in MM and IBC was validated again through unsupervised machine learning clustering analysis, indicating that MM had a unique features phenotype.
CONCLUSIONS: The DLRN developed based on radiomics and deep learning features of ultrasound images has potential clinical value in effectively distinguishing between MM and IBC. DLRN breaks through visual limitations and quantifies more image information related to MM based on computers, further utilizing machine learning to effectively utilize this information for clinical decision-making. As DLRN becomes an autonomous screening system, it will improve the recognition rate of MM in grassroots hospitals and reduce the possibility of incorrect treatment and overtreatment.
METHODS: 50 cases of MM and 180 cases of IBC with ultrasound Breast Imaging Reporting and Data System 4 category were recruited (training cohort, n = 161, validation cohort, n = 69). Based on PyRadiomics and ResNet50 extractors, radiomics and deep learning features were extracted, respectively. Based on supervised machine learning methods such as logistic regression, random forest, and support vector machine, as well as unsupervised machine learning methods using K-means clustering analysis, the differences in features between MM and IBC were analyzed to develop DLRN. The performance of DLRN had been evaluated by receiver operating characteristic curve, calibration, and clinical practicality.
RESULTS: Supervised machine learning results showed that compared with radiomics models, especially random forest models, deep learning models were better at recognizing MM and IBC. The area under the curve (AUC) of the validation cohort was 0.84, the accuracy was 0.83, the sensitivity was 0.73, and the specificity was 0.83. Compared to radiomics or deep learning models, DLRN even further improved discrimination ability (AUC of 0.90 and 0.90, accuracy of 0.83 and 0.88 for training and validation cohorts), which had better clinical benefits and good calibratability. In addition, the information heterogeneity of deep learning features in MM and IBC was validated again through unsupervised machine learning clustering analysis, indicating that MM had a unique features phenotype.
CONCLUSIONS: The DLRN developed based on radiomics and deep learning features of ultrasound images has potential clinical value in effectively distinguishing between MM and IBC. DLRN breaks through visual limitations and quantifies more image information related to MM based on computers, further utilizing machine learning to effectively utilize this information for clinical decision-making. As DLRN becomes an autonomous screening system, it will improve the recognition rate of MM in grassroots hospitals and reduce the possibility of incorrect treatment and overtreatment.
摘要：
背景：这项研究的目的是开发和验证基于超声的深度学习影像组学列线图（DLRN）的潜在价值，以区分块状乳腺炎（MM）和浸润性乳腺癌（IBC）。
方法：纳入50例MM和180例IBC超声乳腺影像报告和数据系统4类（训练队列，n=161，验证队列，n=69)。基于PyRadiomics和ResNet50提取器，提取了影像组学和深度学习特征，分别。基于逻辑回归等监督机器学习方法，随机森林,和支持向量机，以及使用K均值聚类分析的无监督机器学习方法，分析MM和IBC之间的特征差异以开发DLRN。DLRN的性能已通过接收器工作特性曲线进行了评估，校准,和临床实用性。
结果：监督机器学习结果显示，与影像组学模型相比，特别是随机森林模型，深度学习模型在识别MM和IBC方面表现更好。验证队列的曲线下面积（AUC）为0.84，准确性为0.83，敏感性为0.73，特异性为0.83。与影像组学或深度学习模型相比，DLRN甚至进一步提高了辨别能力（AUC为0.90和0.90，训练和验证队列的准确性为0.83和0.88），具有较好的临床疗效和良好的校准性。此外,通过无监督机器学习聚类分析再次验证了MM和IBC中深度学习特征的信息异质性,表明MM具有独特的特征表型。
结论：基于影像组学和超声图像的深度学习特征开发的DLRN在有效区分MM和IBC方面具有潜在的临床价值。DLRN突破视觉限制，基于计算机量化更多与MM相关的图像信息，进一步利用机器学习来有效地利用这些信息进行临床决策。随着DLRN成为一个自主筛查系统，这将提高基层医院对MM的识别率，减少不正确治疗和过度治疗的可能性。
方法：纳入50例MM和180例IBC超声乳腺影像报告和数据系统4类（训练队列，n=161，验证队列，n=69)。基于PyRadiomics和ResNet50提取器，提取了影像组学和深度学习特征，分别。基于逻辑回归等监督机器学习方法，随机森林,和支持向量机，以及使用K均值聚类分析的无监督机器学习方法，分析MM和IBC之间的特征差异以开发DLRN。DLRN的性能已通过接收器工作特性曲线进行了评估，校准,和临床实用性。
结果：监督机器学习结果显示，与影像组学模型相比，特别是随机森林模型，深度学习模型在识别MM和IBC方面表现更好。验证队列的曲线下面积（AUC）为0.84，准确性为0.83，敏感性为0.73，特异性为0.83。与影像组学或深度学习模型相比，DLRN甚至进一步提高了辨别能力（AUC为0.90和0.90，训练和验证队列的准确性为0.83和0.88），具有较好的临床疗效和良好的校准性。此外,通过无监督机器学习聚类分析再次验证了MM和IBC中深度学习特征的信息异质性,表明MM具有独特的特征表型。
结论：基于影像组学和超声图像的深度学习特征开发的DLRN在有效区分MM和IBC方面具有潜在的临床价值。DLRN突破视觉限制，基于计算机量化更多与MM相关的图像信息，进一步利用机器学习来有效地利用这些信息进行临床决策。随着DLRN成为一个自主筛查系统，这将提高基层医院对MM的识别率，减少不正确治疗和过度治疗的可能性。
