Expanding applications of MRI-based radiomics in HER2-positive breast cancer

Park, Vivian Youngjean

doi:10.1016/j.ebiom.2020.103085

Cited by 7 publications

(8 citation statements)

References 11 publications

(12 reference statements)

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“…However, the relationship between ultrasound imaging features and HER2 status in BRCA has not been determined using radiomics yet. Zhou et al (9) found that a radiomics model based on dynamic contrast-enhanced T1 weighted magnetic resonance images has outstanding ability for evaluating HER2 status in BRCA (AUC = 0.81). Most studies have focused on MRI images and reveal that the radiomics approach can e ciently extract radiomic features (10)(11)(12)(13).…”

Section: Discussionmentioning

confidence: 99%

Radiogenomic Analysis of Prediction HER2 Status in Breast Cancer by Linking Ultrasound Radiomic Feature Modules with Biological Functions

et al. 2022

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Section: Discussionmentioning

confidence: 99%

Radiogenomic Analysis of Prediction HER2 Status in Breast Cancer by Linking Ultrasound Radiomic Feature Modules with Biological Functions

et al. 2022

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“…19,20 This technology allows information to be obtained that radiologists cannot discern with the naked eye, thereby assisting in the diagnosis and treatment of diseases. [21][22][23] Despite the growing interest in radiomics, the relationship between radiomics and the evaluation of the efficacy of bone metastases has not been explored in previously published studies. Therefore, this study aimed to investigate the value of CT-based radiomics in evaluating the response of osteolytic bone metastases to systemic drug therapy in breast cancer patients.…”

Section: Introductionmentioning

confidence: 99%

“…Radiomics, which involves quantitative high‐throughput extraction and exploration of effective data characteristics from different images, has shown its unique value in determining disease types, predicting risks, predicting efficacy, and guiding treatment 19,20 . This technology allows information to be obtained that radiologists cannot discern with the naked eye, thereby assisting in the diagnosis and treatment of diseases 21–23 …”

Section: Introductionmentioning

confidence: 99%

Value of CT‐based radiomics in evaluating the response of bone metastases to systemic drug therapy in breast cancer patients

He,

Wang,

et al. 2023

Thoracic Cancer

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BackgroundThis study aimed to investigate the value of nonenhanced computed tomography (CT)‐based radiomics in determining disease progression in breast cancer patients with bone marrow metastases and to develop a model for assessing treatment efficacy.MethodsA total of 134 breast cancer patients with bone metastases were enrolled from three hospitals. Nonenhanced CT was performed after two cycles of drug treatment. The images were categorized into an invalid and a valid group according to disease progression status. The largest osteolytic lesions' maximum cross‐sections in the CT images were selected as regions of interest (ROIs) for feature extraction. Variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used to reduce feature dimensionality. K‐nearest neighbor algorithm (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT) algorithms were trained to establish radiomics models. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic performance of the models.ResultsThe KNN classifier demonstrated the best performance compared to the random grouping method. In the validation group, the area under the ROC curve (AUC) was 0.810. In the cross‐validation method, the RF classifier showed the best performance with an AUC of 0.84.ConclusionNonenhanced CT‐based radiomics provides a promising method for evaluating the efficacy of systemic drug therapy in breast cancer patients with osteolytic bone metastases.

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“…In recent years, the radiomics analysis based on massive data and artificial intelligence has shown significant advantages in judging disease types, predicting risk, and guiding treatment (15)(16)(17). Radiomics converts medical images into highdimensional images and mines effective data features through quantitative high-throughput extraction for data analysis, so various information that cannot be identified by the naked eye of radiologists, such as texture features, can be extracted, which is helpful for the diagnosis and treatment of diseases (18). At the same time, this technology is simple and quick, and has a potential to solve the identification problems between both the pneumonias.…”

Section: Introductionmentioning

confidence: 99%

The value of CT radiomic in differentiating mycoplasma pneumoniae pneumonia from streptococcus pneumoniae pneumonia with similar consolidation in children under 5 years

Wang

Zhao²,

Zhang³

et al. 2022

Front. Pediatr.

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ObjectiveTo investigate the value of CT radiomics in the differentiation of mycoplasma pneumoniae pneumonia (MPP) from streptococcus pneumoniae pneumonia (SPP) with similar CT manifestations in children under 5 years.MethodsA total of 102 children with MPP (n = 52) or SPP (n = 50) with similar consolidation and surrounding halo on CT images in Qilu Hospital and Qilu Children’s Hospital between January 2017 and March 2022 were enrolled in the retrospective study. Radiomic features of the both lesions on plain CT images were extracted including the consolidation part of the pneumonia or both consolidation and surrounding halo area which were respectively delineated at region of interest (ROI) areas on the maximum axial image. The training cohort (n = 71) and the validation cohort (n = 31) were established by stratified random sampling at a ratio of 7:3. By means of variance threshold, the effective radiomics features, SelectKBest and least absolute shrinkage and selection operator (LASSO) regression method were employed for feature selection and combined to calculate the radiomics score (Rad-score). Six classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT) were used to construct the models based on radiomic features. The diagnostic performance of these models and the radiomic nomogram was estimated and compared using the area under the receiver operating characteristic (ROC) curve (AUC), and the decision curve analysis (DCA) was used to evaluate which model achieved the most net benefit.ResultsRF outperformed other classifiers and was selected as the backbone in the classifier with the consolidation + the surrounding halo was taken as ROI to differentiate MPP from SPP in validation cohort. The AUC value of MPP in validation cohort was 0.822, the sensitivity and specificity were 0.81 and 0.81, respectively.ConclusionThe RF model has the best classification efficiency in the identification of MPP from SPP in children, and the ROI with both consolidation and surrounding halo is most suitable for the delineation.

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Expanding applications of MRI-based radiomics in HER2-positive breast cancer

Cited by 7 publications

References 11 publications

Radiogenomic Analysis of Prediction HER2 Status in Breast Cancer by Linking Ultrasound Radiomic Feature Modules with Biological Functions

Radiogenomic Analysis of Prediction HER2 Status in Breast Cancer by Linking Ultrasound Radiomic Feature Modules with Biological Functions

Value of CT‐based radiomics in evaluating the response of bone metastases to systemic drug therapy in breast cancer patients

The value of CT radiomic in differentiating mycoplasma pneumoniae pneumonia from streptococcus pneumoniae pneumonia with similar consolidation in children under 5 years

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