Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: A multicenter study

Fang, Caiyun; Zhang, Juntao; Li, Jizhen; Shang, Hui; Li, Kejian; Jiao, Tianyu; Yin, Di; Li, Fuyan; Cui, Yi; Zeng, Qingshi

doi:10.3389/fonc.2022.922185

Cited by 15 publications

(5 citation statements)

References 26 publications

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“…In addition, the radiomic logistic regression model could noninvasively predict the likelihood of malignancy of breast lesions amenable to breast biopsy. Although in the medical literature many studies are devoted to assessing the possibility of differentiating between benign and malignant lesions for MRI data [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ], there are currently no public and widely accepted radiomics-based guidelines for the pre-operative prediction of malignancy likelihood in patients amenable to MR-VABB. Some recent studies have paved the way to a radiomics-driven exploratory research phase [ 33 , 34 ], and much effort should be made to realize translation into clinical settings.…”

Section: Discussionmentioning

confidence: 99%

“…A total of 93 radiomics features were extracted from raw VOI images with only adjustments of contrast and no imaging filters. The radiomic features were divided into different groups, including shape, first-order statistics and texture features: comprehensive definitions, accurate descriptions and subdivisions into classes of radiomics features are available in the literature [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 32 ]. In summary, two- and three-dimensional shape features include descriptors of the size and geometric shape of the ROI; first-order statistics describe the distribution of the voxel intensities throughout the ROI; and texture features refer to the properties of the gray level values.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, it has been estimated that around 50–70% of MRI findings requiring biopsy turn out to be non-cancer cases [ 5 ]. In recent years, radiomics and artificial intelligence have been increasingly applied in breast MRI [ 6 ], not only to classify breast lesions or predict response to treatment but also to differentiate benign from malignant lesions [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Indeed, radiological practice includes a qualitative and subjective evaluation of the visible lesion describing different characteristics of the tumor aspect (e.g., the presence of spiculations and the presence of necrosis and microcalcifications).…”

Section: Introductionmentioning

confidence: 99%

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Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions

et al. 2023

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Radiomics and artificial intelligence have been increasingly applied in breast MRI. However, the advantages of using radiomics to evaluate lesions amenable to MR-guided vacuum-assisted breast biopsy (MR-VABB) are unclear. This study includes patients scheduled for MR-VABB, corresponding to subjects with MRI-only visible lesions, i.e., with a negative second-look ultrasound. The first acquisition of the multiphase dynamic contrast-enhanced MRI (DCE-MRI) sequence was selected for image segmentation and radiomics analysis. A total of 80 patients with a mean age of 55.8 years ± 11.8 (SD) were included. The dataset was then split into a training set (50 patients) and a validation set (30 patients). Twenty out of the 30 patients with a positive histology for cancer were in the training set, while the remaining 10 patients with a positive histology were included in the test set. Logistic regression on the training set provided seven features with significant p values (<0.05): (1) ‘AverageIntensity’, (2) ‘Autocorrelation’, (3) ‘Contrast’, (4) ‘Compactness’, (5) ‘StandardDeviation’, (6) ‘MeanAbsoluteDeviation’ and (7) ‘InterquartileRange’. AUC values of 0.86 (95% C.I. 0.73–0.94) for the training set and 0.73 (95% C.I. 0.54–0.87) for the test set were obtained for the radiomics model. Radiological evaluation of the same lesions scheduled for MR-VABB had AUC values of 0.42 (95% C.I. 0.28–0.57) for the training set and 0.4 (0.23–0.59) for the test set. In this study, a radiomics logistic regression model applied to DCE-MRI images increased the diagnostic accuracy of standard radiological evaluation of MRI suspicious findings in women scheduled for MR-VABB. Confirming this performance in large multicentric trials would imply that using radiomics in the assessment of patients scheduled for MR-VABB has the potential to reduce the number of biopsies, in suspicious breast lesions where MR-VABB is required, with clear advantages for patients and healthcare resources.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions

et al. 2023

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“…Before extracting radiomics features, a series of image preprocessing steps were conducted, including gray-level discretization, intensity normalization, and voxel resampling to decrease feature variability. [ 29 – 31 ] Wavelet imaging filters and Laplacian of Gaussian (LoG) filtering were used to process the original images and generate supplementary images to enhance the abundance of features. From the original, LoG, and wavelet images, 1046 quantitative radiomics features were extracted using PyRadiomics ( https://github.com/Radiomics/pyradiomics ).…”

Section: Methodsmentioning

confidence: 99%

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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“…Hence, we performed a multivariate analysis considering more potential variables and con rmed border, shape, and microcalci cation as independent clinical risk factors. Unfortunately, the new model's prediction e ciency remained subpar, which is a common aw in radiomics investigations on the molecular subtyping of BC(Fang et al 2022). It is not surprising as the great biological intricacies, per the variation of Her2 gene expression and somatic mutations of HER2-low BC have been well-documented(Marchiò et al 2021).…”

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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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Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: A multicenter study

Cited by 15 publications

References 26 publications

Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions

Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions

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

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

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