■低核分级导管原位癌(DCIS)患者可以采取积极的管理策略,以避免不必要的手术切除。可以基于分子标志物的表达状态来选择不同的个性化治疗方式。这也可以预测不同的结果和复发风险。DCIS超声表现多为非肿块性病变,很难确定界限。目前,研究表明,基于深度学习影像组学(DLR)的模型在肿瘤轮廓自动识别中具有优势。基于临床影像特征的机器学习模型可以解释影像特征的重要性。
■经手术病理证实的349例纯DCIS患者的可用超声数据[54低核等级,175阳性雌激素受体(ER+),163阳性孕激素受体(PR+),收集了81例阳性人表皮生长因子受体2(HER2+)]。放射科医师根据第5版乳腺影像报告和数据系统(BI-RADS)提取DCIS病变的超声特征。患者年龄和BI-RADS特征用于构建临床机器学习(CML)模型。RadImageNet预训练网络用于提取影像组学特征并作为DLR建模的输入。对于训练和验证数据集,80%和20%的数据,分别,被使用。Logistic回归(LR),支持向量机(SVM),随机森林(RF),和极限梯度提升(XGBoost)算法被执行并比较以用于最终的分类建模。每个任务都使用接收器工作特征曲线(AUC)下的面积来评估DLR和CML模型的有效性。
■在训练数据集中,低核等级,ER+,PR+,HER2+DCIS病变占19.20%,65.12%,61.21%,和30.19%,分别;验证集,他们占19.30%,62.50%,57.14%,和30.91%,分别。在我们开发的DLR模型中,识别特征的最佳AUC值为0.633,用于识别低核等级,由ResNet50的XGBoost分类器完成;0.618用于识别ER,由InceptionV3的RF分类器完成;0.755用于识别PR,由InceptionV3的XGBoost分类器完成;和0.713用于识别HER2,由ResNet50的LR分类器完成。CML模型在预测低核品位方面比DLR具有更好的性能,ER+,PR+,和HER2+DCIS病变。分类的最佳AUC值如下:对于RF分类的低核等级,AUC:0.719;对于XGBoost分类的ER+,AUC:0.761;对于XGBoost分类的PR+,AUC:0.780;对于通过RF分类的HER2+,AUC:0.723。
■基于小规模数据集,我们的研究表明,使用RadImageNet预训练网络和CML模型开发的DLR模型可能有助于预测低核等级,ER+,PR+,和HER2+DCIS病变,使患者受益于分级和个性化治疗。
UNASSIGNED: Low nuclear grade ductal carcinoma in situ (DCIS) patients can adopt proactive management strategies to avoid unnecessary surgical resection. Different personalized treatment modalities may be selected based on the expression status of molecular markers, which is also predictive of different outcomes and risks of recurrence. DCIS ultrasound findings are mostly non mass lesions, making it difficult to determine boundaries. Currently, studies have shown that models based on deep learning radiomics (DLR) have advantages in automatic recognition of tumor contours. Machine learning models based on clinical imaging features can explain the importance of imaging features.
UNASSIGNED: The available ultrasound data of 349 patients with pure DCIS confirmed by surgical pathology [54 low nuclear grade, 175 positive estrogen receptor (ER+), 163 positive progesterone receptor (PR+), and 81 positive human epidermal growth factor receptor 2 (HER2+)] were collected. Radiologists extracted ultrasonographic features of DCIS lesions based on the 5th Edition of Breast Imaging Reporting and Data System (BI-RADS). Patient age and BI-RADS characteristics were used to construct clinical machine learning (CML) models. The RadImageNet pretrained network was used for extracting radiomics features and as an input for DLR modeling. For training and validation datasets, 80% and 20% of the data, respectively, were used. Logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms were performed and compared for the final classification modeling. Each task used the area under the receiver operating characteristic curve (AUC) to evaluate the effectiveness of DLR and CML models.
UNASSIGNED: In the training dataset, low nuclear grade, ER+, PR+, and HER2+ DCIS lesions accounted for 19.20%, 65.12%, 61.21%, and 30.19%, respectively; the validation set, they consisted of 19.30%, 62.50%, 57.14%, and 30.91%, respectively. In the DLR models we developed, the best AUC values for identifying features were 0.633 for identifying low nuclear grade, completed by the XGBoost Classifier of ResNet50; 0.618 for identifying ER, completed by the RF Classifier of InceptionV3; 0.755 for identifying PR, completed by the XGBoost Classifier of InceptionV3; and 0.713 for identifying HER2, completed by the LR Classifier of ResNet50. The CML models had better performance than DLR in predicting low nuclear grade, ER+, PR+, and HER2+ DCIS lesions. The best AUC values by classification were as follows: for low nuclear grade by RF classification, AUC: 0.719; for ER+ by XGBoost classification, AUC: 0.761; for PR+ by XGBoost classification, AUC: 0.780; and for HER2+ by RF classification, AUC: 0.723.
UNASSIGNED: Based on small-scale datasets, our study showed that the DLR models developed using RadImageNet pretrained network and CML models may help predict low nuclear grade, ER+, PR+, and HER2+ DCIS lesions so that patients benefit from hierarchical and personalized treatment.