Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma

作者：Aqiao Xu, Xiufeng Chu, Shengjian Zhang, Jing Zheng, Dabao Shi, Shasha Lv, Feng Li, Xiaobo Weng · 发表于：BMC Cancer · 年份：2022 · DOI：10.1186/s12885-022-09967-6 · 被引用次数：41 · 研究领域：Radiomics and Machine Learning in Medical Imaging、HER2/EGFR in Cancer Research、Breast Cancer Treatment Studies

BACKGROUND: The determination of HER2 expression status contributes significantly to HER2-targeted therapy in breast carcinoma. However, an economical, efficient, and non-invasive assessment of HER2 is lacking. We aimed to develop a clinicoradiomic nomogram based on radiomics scores extracted from multiparametric MRI (mpMRI, including ADC-map, T2W1, DCE-T1WI) and clinical risk factors to assess HER2 status. METHODS: We retrospectively collected 214 patients with pathologically confirmed invasive ductal carcinoma between January 2018 to March 2021 from Fudan University Shanghai Cancer Center, and randomly divided this cohort into training set (n = 128, 42 HER2-positive and 86 HER2-negative cases) and validation set (n = 86, 28 HER2-positive and 58 HER2-negative cases) at a ratio of 6:4. The original and transformed pretherapy mpMRI images were treated by semi-automated segmentation and manual modification on the DeepWise scientific research platform v1.6 ( http://keyan.deepwise.com/ ), then radiomics feature extraction was implemented with PyRadiomics library. Recursive feature elimination (RFE) based on logistic regression (LR) and LASSO regression were adpoted to identify optimal features before modeling. LR, Linear Discriminant Analysis (LDA), support vector machine (SVM), random forest (RF), naive Bayesian (NB) and XGBoost (XGB) algorithms were used to construct the radiomics signatures. Independent clinical predictors were identified through univariate logistic analysis (...