Potential Antihuman Epidermal Growth Factor Receptor 2 Target Therapy Beneficiaries: The Role of <scp>MRI</scp>‐Based Radiomics in Distinguishing Human Epidermal Growth Factor Receptor 2‐Low Status of Breast Cancer

Bian, Xiaoqian; Du, Siyao; Yue, Zhibin; Gao, Shijun; Zhao, Ruimeng; Huang, Guoliang; Guo, Liangcun; Peng, Can; Zhang, Lína

doi:10.1002/jmri.28628

Cited by 16 publications

(6 citation statements)

References 40 publications

(78 reference statements)

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“…The inclusion criteria were as follows: patients with suspicious BC (detected by mammography or ultrasound) who underwent CE-CBBCT examinations at Guangxi Medical University Cancer Hospital (Nanning, China) between Aug 2022 and Jun 2023 and CE-CBBCT examinations completed 2 weeks before surgery. [ 25 ] The exclusion criteria were preoperative interventions and therapies, poor image quality with noticeable artifacts, tumor diameter ≤ 1 cm, ductal carcinoma in situ and HER2 positive BC, equivocal FISH results, and incomplete clinical and pathological data. A total of 137 patients were enrolled in this study; 81 patients were excluded.…”

Section: Methodsmentioning

confidence: 99%

“…A cutoff value of 20% was used to assess the Ki-67 index. [ 25 ] HER2 status was confirmed postoperatively using IHC or FISH. Further FISH validation was performed for tumors with equivocal 2+ results.…”

Section: Methodsmentioning

confidence: 99%

“…By constructing predictive models using various ML algorithms and comparing their performance, the most effective predictive models can be extracted. [23,24] Previous studies [25,26] have demonstrated that ML models based on magnetic resonance imaging (MRI) radiomic features can predict HER2-low BC; however, whether CE-CBBCT radiomic features can discriminate HER2-low from HER2-negative BCs is uncertain. Therefore, this study aimed to develop and validate ML models based on CE-CBBCT radiomics features to predict HER2-low BC.…”

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of ML models for preoperative prediction of HER2-low BC based on CE-CBBCT radiomic features: A prospective study

Chen,

Li,

Liang

et al. 2024

Medicine

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To explore the value of machine learning (ML) models based on contrast-enhanced cone-beam breast computed tomography (CE-CBBCT) radiomics features for the preoperative prediction of human epidermal growth factor receptor 2 (HER2)-low expression breast cancer (BC). Fifty-six patients with HER2-negative invasive BC who underwent preoperative CE-CBBCT were prospectively analyzed. Patients were randomly divided into training and validation cohorts at approximately 7:3. A total of 1046 quantitative radiomic features were extracted from CE-CBBCT images and normalized using z-scores. The Pearson correlation coefficient and recursive feature elimination were used to identify the optimal features. Six ML models were constructed based on the selected features: linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), logistic regression (LR), AdaBoost (AB), and decision tree (DT). To evaluate the performance of these models, receiver operating characteristic curves and area under the curve (AUC) were used. Seven features were selected as the optimal features for constructing the ML models. In the training cohort, the AUC values for SVM, LDA, RF, LR, AB, and DT were 0.984, 0.981, 1.000, 0.970, 1.000, and 1.000, respectively. In the validation cohort, the AUC values for the SVM, LDA, RF, LR, AB, and DT were 0.859, 0.880, 0.781, 0.880, 0.750, and 0.713, respectively. Among all ML models, the LDA and LR models demonstrated the best performance. The DeLong test showed that there were no significant differences among the receiver operating characteristic curves in all ML models in the training cohort (P > .05); however, in the validation cohort, the DeLong test showed that the differences between the AUCs of LDA and RF, AB, and DT were statistically significant (P = .037, .003, .046). The AUCs of LR and RF, AB, and DT were statistically significant (P = .023, .005, .030). Nevertheless, no statistically significant differences were observed when compared to the other ML models. ML models based on CE-CBBCT radiomics features achieved excellent performance in the preoperative prediction of HER2-low BC and could potentially serve as an effective tool to assist in precise and personalized targeted therapy.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of ML models for preoperative prediction of HER2-low BC based on CE-CBBCT radiomic features: A prospective study

Chen,

Li,

Liang

et al. 2024

Medicine

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“…However, multiparametric data are now also used to tackle more complex tasks such as predicting subtypes and treatment response after neoadjuvant treatment. We have summarized selected recent research investigating the prediction of subtypes 10,27,29,30,91,[103][104][105][106][107][108][109] (Table 2), response after neoadjuvant treatment [110][111][112] (Table 3), and evaluating TILs or CAF [113][114][115][116] (Table 4) to provide an overview of the trends in parameters and methods used for analysis. The most popular combinations of imaging include DCE, DWI (ADC value), and T2WI.…”

Section: Multiparametric Approaches To Breast Cancer: Recent Advancesmentioning

confidence: 99%

Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment

Kataoka,

Iima,

Miyake

et al. 2023

Invest Radiol

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A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters. The increased use of ultrafast DCE-MRI has facilitated the introduction of novel kinetic parameters. In addition to DCE-MRI, diffusion-weighted imaging provides information associated with tumor cell density, with advanced diffusion-weighted imaging techniques such as intravoxel incoherent motion, diffusion kurtosis imaging, and time-dependent diffusion MRI opening up new horizons in microscale tissue evaluation. Furthermore, T2-weighted imaging plays a key role in measuring the degree of tumor aggressiveness, which may be related to the tumor microenvironment. Magnetic resonance imaging is, however, not the only imaging modality providing semiquantitative and quantitative parameters from breast tumors. Breast positron emission tomography demonstrates superior spatial resolution to whole-body positron emission tomography and allows comparable delineation of breast cancer to MRI, as well as providing metabolic information, which often precedes vascular and morphological changes occurring in response to treatment. The integration of these imaging-derived factors is accomplished through multiparametric imaging. In this article, we explore the relationship among the key imaging parameters, breast cancer diagnosis, and histological characteristics, providing a technical and theoretical background for these parameters. Furthermore, we review the recent studies on the application of multiparametric imaging to breast cancer and the significance of the key imaging parameters.

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“…The wavelet features, which advanced the information extraction from medical images, accounted for the largest number and weight in our prediction model of HER2-low BC.It is known that heterogenous tumors tend to exhibit intricate local texture, rapid texture variations, and irregular lesion boundaries. In a recent MRI-based radiomics study of BC her2-low expression by Bian et al, wavelet features are the most important both from intratumoral and peritumoral features(Bian et al 2023). The wavelet transform is a versatile method for time-frequency analysis that permits the accurate localization of useful signals in time or space while successfully separating them from noise (Wahab et of MYCN oncogene ampli cation status, thereby offering additional information for the characteristics of the lesion, such as burrs and vascularity(Chen et al 2021).…”

mentioning

confidence: 99%

Detection of low expression of molecular subtype HER2 in breast cancer using ultrasound-based radiomics

Zhang,

Wu,

et al. 2023

Preprint

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Purpose: HER2-low expression breast cancer (BC) accounts for nearly half of all breast cancers which may benefit from new antibody-drug conjugates targeted treatments. HER2-low BC is gradually being recognized as a distinct subtype. Therefore, we aimed to use ultrasound-based radiomics（USR）to develop an efficient evaluation approach of HER2-low status. Methods: 222 patients with a histologically diagnosis of BC were retrospectively analyzed and randomly divided into training and test cohort. Radiomics features were extracted from the preoperative ultrasound images, followed by Lasso regression for dimension reduction.Based on the selected features, the optimal machine learning classifier was selected to construct a USR model to predict HER2-low expression. Multivariable logistic regression was used to identify independent clinical risk factors.Finally, a clinical-USR model incorporating the radiomics features and the clinical risk factors was constructed. Model performance was assessed using receiver operating characteristic curve and decision curve analysis. Results: The USR model and clinical-USR model had good predictive ability in the training cohort ( AUC 0.91; 0.86 , respectively) and test cohort ( AUC 0.83; 0.78 , respectively).Both of them outperformed the clinical model (p < 0.05, DeLong test).Decision curve analysis confirmed that the model had clinical utility. Conclusion: The machine learning model based on ultrasound images had high prediction value for HER2-low BC.

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Potential Antihuman Epidermal Growth Factor Receptor 2 Target Therapy Beneficiaries: The Role of MRI‐Based Radiomics in Distinguishing Human Epidermal Growth Factor Receptor 2‐Low Status of Breast Cancer

Cited by 16 publications

References 40 publications

Performance evaluation of ML models for preoperative prediction of HER2-low BC based on CE-CBBCT radiomic features: A prospective study

Performance evaluation of ML models for preoperative prediction of HER2-low BC based on CE-CBBCT radiomic features: A prospective study

Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment

Detection of low expression of molecular subtype HER2 in breast cancer using ultrasound-based radiomics

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