Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method

Xu, Zilong; Yang, Qiwei; Li, Minghao; Gu, Jinyan; Du, Changping; Yang, Chen; Li, Baosheng

doi:10.3389/fonc.2022.829041

Cited by 13 publications

(14 citation statements)

References 34 publications

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“…Hence, compared with the study by Wu et al, a major highlight in our study was the larger sample size and diversity of tumor types, which might increase the universality of the nomogram model. We obtained a higher AUC value compared to the aforementioned studies with regards to prediction of HER2 status by using radiomics and a machine-learning algorithm [ 29 , 37 ]. The most probable explanation for this is that we adopted seven machine learning classifiers to develop seven prediction models and selected the one with the highest AUC value.…”

Section: Discussionmentioning

confidence: 96%

“…Generally, one feature selection method is adopted in conventional radiomics analysis. In the study by Xu et al [ 37 ], six features based on ultrasound radiomics were selected by the recursive feature elimination, and a random forest model including 90 trees was built for prediction of HER2 status, with the AUC of 0.780 and 0.740 in the training and validation sets. In order to reduce overfitting effectively, we used the ICC and Mann–Whitney U test for feature selection in the first step and LASSO regression in the second step, and we achieved better predictive performance with the LR classifier than the study by Xu et al, with AUC values of 0.804 and 0.786 in the training and validation sets, respectively.…”

Section: Discussionmentioning

confidence: 99%

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Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma

Guo

Wang

et al. 2022

Diagnostics

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(1) Objective: To evaluate the performance of ultrasound-based radiomics in the preoperative prediction of human epidermal growth factor receptor 2-positive (HER2+) and HER2− breast carcinoma. (2) Methods: Ultrasound images from 309 patients (86 HER2+ cases and 223 HER2− cases) were retrospectively analyzed, of which 216 patients belonged to the training set and 93 patients assigned to the time-independent validation set. The region of interest of the tumors was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analyses using the intra-class correlation coefficient (ICC), Mann–Whitney U test, and the least absolute shrinkage and selection operator (LASSO) algorithm. The radiomics score (Rad-score) for each patient was calculated through a linear combination of the nonzero coefficient features. The support vector machine (SVM), K nearest neighbors (KNN), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction models based on the Rad-score. A clinical model based on significant clinical features was also established. In addition, the logistic regression method was used to integrate Rad-score and clinical features to generate the nomogram model. The leave-one-out cross validation (LOOCV) method was used to validate the reliability and stability of the model. (3) Results: Among the seven classifier models, the LR achieved the best performance in the validation set, with an area under the receiver operating characteristic curve (AUC) of 0.786, and was obtained as the Rad-score model, while the RF performed the worst. Tumor size showed a statistical difference between the HER2+ and HER2− groups (p = 0.028). The nomogram model had a slightly higher AUC than the Rad-score model (AUC, 0.788 vs. 0.786), but no statistical difference (Delong test, p = 0.919). The LOOCV method yielded a high median AUC of 0.790 in the validation set. (4) Conclusion: The Rad-score model performs best among the seven classifiers. The nomogram model based on Rad-score and tumor size has slightly better predictive performance than the Rad-score model, and it has the potential to be utilized as a routine modality for preoperatively determining HER2 status in BC patients non-invasively.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma

Guo

Wang

et al. 2022

Diagnostics

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“…Other researchers attempted to develop a PET-CT radiomics mechanistic learning model to predict HER2 expression status, however, the results showed that PET-CT was not sufficient to accurately predict HER2 expression status with an AUC of 0.72-0.76 (39). Xu ZL et al developed a deep learning model to predict HER2 expression in breast cancer from ultrasound images (28). In common with this study, our results also showed a lower overall diagnostic performance for HER2 status assessment using clinical parameters alone, with an AUC of 0.55-0.69.…”

Section: Discussionmentioning

confidence: 99%

“…Convolutional neural networks have had tremendous success in bioinformatics since 2012 thanks to the development of deep learning, particularly in medical imaging ( 27 ). A recent study shows that DLR can be used to analyze US images for prediction of HER2 expression status ( 28 ). However, DLR usually faces the problem of small sample learning, and this study had only 36 patients in the validation set and 108 patients in the training set.…”

Section: Introductionmentioning

confidence: 99%

Deep learning radiomics model based on breast ultrasound video to predict HER2 expression status

et al. 2023

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PurposeThe detection of human epidermal growth factor receptor 2 (HER2) expression status is essential to determining the chemotherapy regimen for breast cancer patients and to improving their prognosis. We developed a deep learning radiomics (DLR) model combining time-frequency domain features of ultrasound (US) video of breast lesions with clinical parameters for predicting HER2 expression status.Patients and MethodsData for this research was obtained from 807 breast cancer patients who visited from February 2019 to July 2020. Ultimately, 445 patients were included in the study. Pre-operative breast ultrasound examination videos were collected and split into a training set and a test set. Building a training set of DLR models combining time-frequency domain features and clinical features of ultrasound video of breast lesions based on the training set data to predict HER2 expression status. Test the performance of the model using test set data. The final models integrated with different classifiers are compared, and the best performing model is finally selected.ResultsThe best diagnostic performance in predicting HER2 expression status is provided by an Extreme Gradient Boosting (XGBoost)-based time-frequency domain feature classifier combined with a logistic regression (LR)-based clinical parameter classifier of clinical parameters combined DLR, particularly with a high specificity of 0.917. The area under the receiver operating characteristic curve (AUC) for the test cohort was 0.810.ConclusionOur study provides a non-invasive imaging biomarker to predict HER2 expression status in breast cancer patients.

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“…As a result, scientists worldwide have created a range of automated techniques for classifying HER2 status from IHC and H&E-stains. MRI and ultrasound images were also used in a study to classify HER2 status [ 13 , 14 ]. A Support Vector Machine (SVM) was used there as the approach to identify the HER2 status in MRI images.…”

Section: Related Workmentioning

confidence: 99%

Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images

et al. 2022

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Breast cancer is a significant health concern among women. Prompt diagnosis can diminish the mortality rate and direct patients to take steps for cancer treatment. Recently, deep learning has been employed to diagnose breast cancer in the context of digital pathology. To help in this area, a transfer learning-based model called ‘HE-HER2Net’ has been proposed to diagnose multiple stages of HER2 breast cancer (HER2-0, HER2-1+, HER2-2+, HER2-3+) on H&E (hematoxylin & eosin) images from the BCI dataset. HE-HER2Net is the modified version of the Xception model, which is additionally comprised of global average pooling, several batch normalization layers, dropout layers, and dense layers with a swish activation function. This proposed model exceeds all existing models in terms of accuracy (0.87), precision (0.88), recall (0.86), and AUC score (0.98) immensely. In addition, our proposed model has been explained through a class-discriminative localization technique using Grad-CAM to build trust and to make the model more transparent. Finally, nuclei segmentation has been performed through the StarDist method.

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Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method

Cited by 13 publications

References 34 publications

Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma

Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma

Deep learning radiomics model based on breast ultrasound video to predict HER2 expression status

Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images

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