■评估基于超声图像的整合影像组学列线图区分乳腺纤维腺瘤(FA)和纯黏液性癌(P-MC)的能力。
■回顾性纳入了170例明确病理证实的FA或P-MC患者(训练组120例,试验组50例)。从常规超声(CUS)图像中提取了4164个影像组学特征,使用最小绝对收缩和选择算子(LASSO)算法构建影像组学评分(Radscore)。支持向量机(SVM)开发了不同的模型,并对不同模型的诊断性能进行了评估和验证.接收器工作特性(ROC)曲线的比较,校正曲线,并进行决策曲线分析(DCA)以评估不同模型的增量值。
■最后,选择了11个影像组学特征,然后在此基础上开发了Radscore,在两个队列中P-MC均较高。在测试组中,临床+CUS+影像组学(Clin+CUS+Radscore)模型与临床+影像组学(Clin+Radscore)模型(AUC=0.86,95%CI,0.733-0.942)相比(AUC=0.76,95%CI,0.618-0.869,P>0.05),临床+CUS(Clin+CUS)(AUC=0.76,95%CI,0.618-0.869,P<0.05),Clin(AUC=0.74,95%CI,0.600-0.854,P<0.05),和Radscore(AUC=0.64,95%CI,0.492-0.771,P<0.05)模型,分别。校准曲线和DCA也表明组合列线图具有优异的临床价值。
■组合的Clin+CUS+Radscore模型可能有助于改善FA与P-MC的区分。
UNASSIGNED: To evaluate the ability of integrated radiomics nomogram based on ultrasound images to distinguish between breast fibroadenoma (FA) and pure mucinous carcinoma (P-MC).
UNASSIGNED: One hundred seventy patients with FA or P-MC (120 in the training set and 50 in the test set) with definite pathological confirmation were retrospectively enrolled. Four hundred sixty-four radiomics features were extracted from conventional ultrasound (CUS) images, and radiomics score (Radscore) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different models were developed by a support vector machine (SVM), and the diagnostic performance of the different models was assessed and validated. A comparison of the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) was performed to evaluate the incremental value of the different models.
UNASSIGNED: Finally, 11 radiomics features were selected, and then Radscore was developed based on them, which was higher in P-MC in both cohorts. In the test group, the clinic + CUS + radiomics (Clin + CUS + Radscore) model achieved a significantly higher area under the curve (AUC) value (AUC = 0.86, 95% CI, 0.733-0.942) when compared with the clinic + radiomics (Clin + Radscore) (AUC = 0.76, 95% CI, 0.618-0.869, P > 0.05), clinic + CUS (Clin + CUS) (AUC = 0.76, 95% CI, 0.618-0.869, P< 0.05), Clin (AUC = 0.74, 95% CI, 0.600-0.854, P< 0.05), and Radscore (AUC = 0.64, 95% CI, 0.492-0.771, P< 0.05) models, respectively. The calibration curve and DCA also suggested excellent clinical value of the combined nomogram.
UNASSIGNED: The combined Clin + CUS + Radscore model may help improve the differentiation of FA from P-MC.