Diagnostic value of core needle biopsy for determining HER2 status in breast cancer, especially in the HER2-low population

Chen, Ruixian; Qi, Yana; Ya, Huang; Liu, Weijing; Yang, Ruoning; Zhao, Xin; Wu, Yunhao; Li, Qintong; Wang, Zhu; Sun, Xin; Wei, Bing; Chen, Jie

doi:10.1007/s10549-022-06781-3

Cited by 14 publications

(9 citation statements)

References 36 publications

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“… 97 Surgical excision specimens may be preferable when available, as HER2-low status may be harder to detect in core-needle biopsies. 98 , 99 …”

Section: Identifying Patients With Her2-low Breast Cancer: Expert Opi...mentioning

confidence: 99%

Open questions, current challenges, and future perspectives in targeting human epidermal growth factor receptor 2-low breast cancer

Curigliano,

Dent,

Earle

et al. 2024

ESMO Open

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“… 97 Surgical excision specimens may be preferable when available, as HER2-low status may be harder to detect in core-needle biopsies. 98 , 99 …”

Section: Identifying Patients With Her2-low Breast Cancer: Expert Opi...mentioning

confidence: 99%

Open questions, current challenges, and future perspectives in targeting human epidermal growth factor receptor 2-low breast cancer

Curigliano,

Dent,

Earle

et al. 2024

ESMO Open

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“…In clinical practice, HER2 status can be determined based on IHC and/or in situ hybridization; however, owing to the heterogeneity of HER2 expression, approximately 15.3% and 7.3% of HER2-low BCs are mistakenly classified as HER2-negative and HER2-positive by core needle biopsy, respectively. [ 10 ] Core needle biopsy is an invasive procedure. Thus, the development of noninvasive methods that can accurately and quickly determine HER2 status is imperative.…”

Section: Introductionmentioning

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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“…Ultrasound can be used to detect and evaluate breast tumors, but it is operator-dependent and can miss small tumors [3]. A biopsy involves the removal of a tissue sample for examination under a microscope and is the gold standard for BC detection [4].…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature

Alsabry,

Algabri,

Ahsan

2023

jast

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Breast cancer (BC) is a major health concern worldwide. It is a complex and multifactorial disease, and identifying its risk factors is crucial for early detection and effective treatment. This review article provides an overview of the literature on breast cancer risk factors, data sources, and machine learning algorithms for prediction. The paper discusses the various risk factors associated with breast cancer, including age, family history, lifestyle choices, and environmental factors. Additionally, the article explores the different data sources used in breast cancer research, including clinical data, genomic data, and lifestyle data. The paper then reviews the different machine-learning algorithms used for breast cancer prediction, including supervised and unsupervised learning. The performance of ML algorithms in predicting BC risk using different data sources was assessed. This review provides valuable insights into the current state of research on BC risk factors and prediction using ML algorithms and data sources, and the findings will be useful for healthcare professionals and researchers working in the field of BC.

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Diagnostic value of core needle biopsy for determining HER2 status in breast cancer, especially in the HER2-low population

Cited by 14 publications

References 36 publications

Open questions, current challenges, and future perspectives in targeting human epidermal growth factor receptor 2-low breast cancer

Open questions, current challenges, and future perspectives in targeting human epidermal growth factor receptor 2-low breast cancer

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

Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature

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