■探讨基于MRI的多参数影像列线图在评估乳腺癌(BC)HER-22状态中的应用。
■病理证实为HER-22+侵袭性BC的患者,接受术前MRI检查的患者分为训练(72例,21HER-2阳性和51HER-2阴性)和验证(32例患者,9个HER-2阳性和23个HER-2阴性)通过随机化设置。根据IHC和FISH,全部分类为HER-2+FISH阳性(HER-2阳性)或阴性(HER-2阴性)。3DVOI是由两名放射科医生在MR图像上绘制的。ADC,T2WI,和DCE图像分别分析以提取特征(n=1906)。L1正则化,F-test,和其他方法被用来减少维度。然后使用逻辑回归(LR)分类器构建使用来自单个或组合成像序列的特征的二元放射组学预测模型,并在验证数据集上进行验证。为了建立放射组学列线图,进行多变量LR分析以确定独立指标。使用AUC评估模型的预测功效。
■在组合ADC的基础上,T2WI,和DCE图像,在特征降维之后,提取了十个放射学特征。与一个或两个序列(训练组的AUC:0.883;验证组的AUC:0.816)相比,使用所有三个序列的放射组学特征具有优异的诊断效率。基于多变量LR分析,影像组学特征和瘤周水肿是鉴定HER-22+的独立预测因子.在训练和验证数据集中,结合瘤周水肿和影像组学特征的列线图显示了有效的区分(AUC分别为0.966和0.884).
■结合瘤周水肿和基于多参数MRI的影像组学特征的列线图可用于有效预测BC的HER-22状态。
UNASSIGNED: To explore the application of multiparametric MRI-based radiomic nomogram for assessing HER-2 2+ status of breast cancer (BC).
UNASSIGNED: Patients with pathology-proven HER-2 2+ invasive BC, who underwent preoperative MRI were divided into training (72 patients, 21 HER-2-positive and 51 HER-2-negative) and validation (32 patients, 9 HER-2-positive and 23 HER-2-negative) sets by randomization. All were classified as HER-2 2+ FISH-positive (HER-2-positive) or -negative (HER-2-negative) according to IHC and FISH. The 3D VOI was drawn on MR images by two radiologists. ADC, T2WI, and DCE images were analyzed separately to extract features (n = 1906). L1 regularization, F-test, and other methods were used to reduce dimensionality. Binary radiomics prediction models using features from single or combined imaging sequences were constructed using logistic regression (LR) classifier then and validated on a validation dataset. To build a radiomics nomogram, multivariate LR analysis was conducted to identify independent indicators. An evaluation of the model\'s predictive efficacy was made using AUC.
UNASSIGNED: On the basis of combined ADC, T2WI, and DCE images, ten radiomic features were extracted following feature dimensionality reduction. There was superior diagnostic efficiency of radiomic signature using all three sequences compared to either one or two sequences (AUC for training group: 0.883; AUC for validation group: 0.816). Based on multivariate LR analysis, radiomic signature and peritumoral edema were independent predictors for identifying HER-2 2 +. In both training and validation datasets, nomograms combining peritumoral edema and radiomics signature demonstrated an effective discrimination (AUCs were respectively 0.966 and 0. 884).
UNASSIGNED: The nomogram that incorporated peritumoral edema and multiparametric MRI-based radiomic signature can be used to effectively predict the HER-2 2+ status of BC.