Huang Yan, Xiao Qin, Sun Yiqun, Wang Zhe, Li Qin, Wang He, Gu Yajia
Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Front Oncol. 2021 Feb 16;10:607235. doi: 10.3389/fonc.2020.607235. eCollection 2020.
To develop and validate an imaging-radiomics model for the diagnosis of male benign and malignant breast lesions.
Ninety male patients who underwent preoperative mammography from January 2011 to December 2018 were enrolled in this study (63 in the training cohort and 27 in the validation cohort). The region of interest was segmented into a mediolateral oblique view, and 104 radiomics features were extracted. The minimum redundancy and maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) methods were used to exclude radiomics features to establish the radiomics score (rad-score). Mammographic features were evaluated by two radiologists. Univariate logistic regression was used to select for imaging features, and multivariate logistic regression was used to construct an imaging model. An imaging-radiomics model was eventually established, and a nomogram was developed based on the imaging-radiomics model. Area under the curve (AUC) and decision curve analysis (DCA) were applied to assess the clinical value.
The AUC based on the imaging model in the validation cohort was 0.760, the sensitivity was 0.750, and the specificity was 0.727. The AUC, sensitivity and specificity based on the radiomics in the validation cohort were 0.820, 0.750, and 0.867, respectively. The imaging-radiomics model was better than the imaging and radiomics models; the AUC, sensitivity, and specificity of the imaging-radiomics model in the validation cohort were 0.870, 0.824, and 0.900, respectively.
The imaging-radiomics model created by the imaging characteristics and radiomics features exhibited a favorable discriminatory ability for male breast cancer.
开发并验证一种用于诊断男性乳腺良恶性病变的影像组学模型。
纳入2011年1月至2018年12月期间接受术前乳腺钼靶检查的90例男性患者（训练队列63例，验证队列27例）。将感兴趣区域在内外侧斜位视图上进行分割，并提取104个影像组学特征。采用最小冗余最大相关（mRMR）和最小绝对收缩与选择算子（LASSO）方法排除影像组学特征以建立影像组学评分（rad-score）。由两名放射科医生评估乳腺钼靶特征。采用单因素逻辑回归选择影像特征，多因素逻辑回归构建影像模型。最终建立影像组学模型，并基于该模型绘制列线图。应用曲线下面积（AUC）和决策曲线分析（DCA）评估临床价值。
验证队列中基于影像模型的AUC为0.760，灵敏度为0.750，特异度为0.727。验证队列中基于影像组学的AUC、灵敏度和特异度分别为0.820、0.750和0.867。影像组学模型优于影像模型和影像组学模型；验证队列中影像组学模型的AUC、灵敏度和特异度分别为0.870、0.824和0.900。
由影像特征和影像组学特征创建的影像组学模型对男性乳腺癌具有良好的鉴别能力。
