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Multiparametric MRI-based Radiomic Nomogram for Predicting HER-2 2+ Status of Breast Cancer

Overview
Journal Heliyon
Specialty Social Sciences
Date 2024 May 9
PMID 38720718
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Abstract

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.

References
1.
Zhou H, Yu Y, Wang C, Zhang S, Gao Y, Pan J . A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics. Nat Biomed Eng. 2023; 7(6):743-755. DOI: 10.1038/s41551-023-01045-x. View

2.
Wolff A, Somerfield M, Dowsett M, Hammond M, Hayes D, McShane L . Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: ASCO-College of American Pathologists Guideline Update. J Clin Oncol. 2023; 41(22):3867-3872. DOI: 10.1200/JCO.22.02864. View

3.
Liu Z, Zhang X, Shi Y, Wang L, Zhu H, Tang Z . Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Clin Cancer Res. 2017; 23(23):7253-7262. DOI: 10.1158/1078-0432.CCR-17-1038. View

4.
Dietzel M, Schulz-Wendtland R, Ellmann S, Zoubi R, Wenkel E, Hammon M . Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer. Sci Rep. 2020; 10(1):3664. PMC: 7048934. DOI: 10.1038/s41598-020-60393-9. View

5.
Zhou J, Tan H, Li W, Liu Z, Wu Y, Bai Y . Radiomics Signatures Based on Multiparametric MRI for the Preoperative Prediction of the HER2 Status of Patients with Breast Cancer. Acad Radiol. 2020; 28(10):1352-1360. DOI: 10.1016/j.acra.2020.05.040. View