文献类型：期刊文献

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

作者：Xu, Aqiao[1];Chu, Xiufeng[2];Zhang, Shengjian[3];Zheng, Jing[1];Shi, Dabao[1];Lv, Shasha[1];Li, Feng[4];Weng, Xiaobo[1]

机构：[1]Shaoxing Univ, Dept Radiol, Cent Hosp, Shaoxing Cent Hosp, Shaoxing 312030, Peoples R China;[2]Shaoxing Univ, Dept Surg, Cent Hosp, Shaoxing Cent Hosp, Shaoxing 312030, Peoples R China;[3]Fudan Univ, Dept Radiol, Shanghai Canc Ctr, Shanghai 200032, Peoples R China;[4]Beijing Deepwise & League PHD Technol Co Ltd, Dept Res Collaborat, R&D Ctr, Beijing 100080, Peoples R China

年份：2022

卷号：22

期号：1

外文期刊名：BMC CANCER

收录：SCI-EXPANDED(收录号：WOS:000838116500002)、、Scopus(收录号：2-s2.0-85135728254)、WOS

基金：This work was supported by the Medical and Health Research Project of Zhejiang Province (Grant Nos. 2021KY1161, 2022KY1316 and 2021KY1146); Zhejiang Province Chinese Medicine Science Research Fund Project (Grant No. 2021ZA138); and Institution from Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City.

语种：英文

外文关键词：Breast carcinoma; Multiparametric magnetic resonance imaging; Nomograms; HER2; Radiomics

外文摘要：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 HER2positive and 58 HER2-negative cases) at a ratio of 6:4. The original and transformed pretherapy mpMRl 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 (age, tumor location, ki-67 index, histological grade, and lymph node metastasis). Then, the radiomics signature with the best diagnostic performance (Rad score) was further combined with significant clinical risk factors to develop a clinicoradiomic model (nomogram) using multivariate logistic regression. The discriminative power of the constructed models were evaluated by AUC, DeLong test, calibration curve, and decision curve analysis (DCA). Results: 70 (32.71%) of the enrolled 214 cases were HER2-positive, while 144 (67.29%) were HER2-negative. Eleven best radiomics features were retained to develop 6 radiomcis classifiers in which RF classifier showed the highest AUC of 0.887 (95%Cl: 0.827-0.947) in the training set and acheived the AUC of 0.840 (95%Cl: 0.758-0.922) in the validation set. A nomogram that incorporated the Rad score with two selected clinical factors (Ki-67 index and histological grade) was constructed and yielded better discrimination compared with Rad score (p = 0.374, Delong test), with an AUC of 0.945 (95%CI: 0.904-0.987) in the training set and 0.868 (95%CI: 0.789-0.948; p= 0.123) in the validation set. Moreover, calibration with the p-value of 0.732 using Hosmer-Lemeshow test demonstrated good agreement, and the DCA verified the benefits of the nomogram. Conclusion: Post largescale validation, the clinicoradiomic nomogram may have the potential to be used as a noninvasive tool for determination of HER2 expression status in clinical HER2-targeted therapy prediction.