超声影像组学标签对乳腺癌HER-2状态的预测价值

Ultrasound-based radiomics to predict HER-2 status in breast cancer

目的探讨基于超声的影像组学标签在术前预测乳腺癌HER-2状态中的价值。方法回顾性收集230例浸润性乳腺癌患者的临床资料和术前超声图像。按超声检查时间顺序,将患者分为训练组(n=115)和验证组(n=115)。通过Image J软件沿肿瘤边界手动勾画超声图像中的病灶区域,使用Pyradiomics从每个病灶区域中提取1 820个特征,采用3种统计学方法筛选特征,利用逻辑回归模型构建超声影像组学标签。采用受试工作特征曲线(ROC)、校准曲线及决策曲线等评估超声影像组学标签预测HER-2状态的效能和价值。结果最终选取9个关键影像特征用于构建超声影像组学标签。该标签在训练组和验证组中的ROC曲线下面积分别为0.82[95%CI(0.74,0.90)]、0.81[95%CI(0.72,0.89)]。校准曲线显示该标签在训练集和验证集中均具有较好的校准度。结论基于超声的影像组学标签在术前预测乳腺癌HER-2状态中具有重要价值。

Objective To explore the value of a radiomics model based on ultrasound imaging in predicting the HER-2 status of breast cancer prior to surgery. Methods A total of 230 patients with invasive breast cancer were retrospectively analyzed, all the patients underwent preoperative breast ultrasound examination. According to the order of examination time, the patients were categorized into training group(n=115) and validation group(n=115). Image J software was used to manually delineate the lesion area in the ultrasound image along the tumor boundary. Pyradiomics was used to extract 1 820 features from each lesion area, and three statistical methods were used to screen features. A logistic regression model was used to construct ultrasound imaging radiomics model. The receive operating characteristic curve(ROC), calibration curve and decision curve were used to evaluate the performance and value of ultrasound imaging radiomics model in predicting HER-2 status. Results Nine key image features were identified to construct ultrasound imaging radiomics model. The area of under the ROC curve of the model in the training group and the validation group were 0.82(95%CI 0.74 to 0.90) and 0.81(95%CI 0.72 to 0.89), respectively. The calibration curve showed that the model had a good calibration in both the training and validation groups. Conclusions Ultrasound-based imaging radiomics model is of significant value in predicting the HER-2 status of breast cancer prior to surgery.