Multiparametric MRI-based radiomic nomogram for predicting HER-2 2+status of breast cancer

被引：1

作者：

Wang, Haili ^{[1
]}

Sang, Li ^{[1
]}

Xu, Jingxu ^{[2
]}

Huang, Chencui ^{[2
]}

Huang, Zhaoqin ^{[1
]}

机构：

[1] Shandong First Med Univ, Dept Radiol, Shandong Prov Hosp, Jinan 250021, Shandong, Peoples R China

[2] Beijing Deepwise & League PHD Technol Co Ltd, R&D Ctr, Dept Res Collaborat, Beijing, Peoples R China

来源：

HELIYON | 2024年 / 10卷 / 09期

关键词：

Breast cancer; HER-2; Magnetic resonance imaging; Nomogram; Radiomic; DIAGNOSTIC-ACCURACY; PERITUMORAL EDEMA; MOLECULAR SUBTYPE; FEATURES; ANGIOGENESIS; PARAMETERS;

D O I：

10.1016/j.heliyon.2024.e29875

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Objective: To explore the application of multiparametric MRI-based radiomic nomogram for assessing HER -2 2 + status of breast cancer (BC) Methods: 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. Results: 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). Conclusion: 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.

引用

页数：8