Does the subtype of breast cancer affect the diagnostic performance of axillary ultrasound for nodal staging in breast cancer patients?

Vane, M.; Nijnatten, Thiemo J A van; Nelemans, PJ; Lobbes, Marc; Roozendaal, Lori M. van; Kooreman, Loes; Keymeulen, Kristien; Smidt, Marjolein L.; Schipper, Robert-Jan

doi:10.1016/j.ejso.2019.01.012

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…They also described that ER-/PR-negative tumors had a lower risk of nodal metastases than ER-/PR-positive tumors in patients with early-stage breast cancer. In addition, Vane et al [ 22 ] reported the highest NPV of axillary US in the triple-negative subtype (90.3%), and Caudle et al [ 18 ] found that triple-negative tumors were associated with less extensive nodal involvement. Our results in this regard are consistent with those of previous studies.…”

Section: Discussionmentioning

confidence: 99%

Ultrasonography and ultrasound-guided fine-needle aspiration biopsy can predict a heavy nodal metastatic burden in early-stage breast cancer

et al. 2021

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Section: Discussionmentioning

confidence: 99%

Ultrasonography and ultrasound-guided fine-needle aspiration biopsy can predict a heavy nodal metastatic burden in early-stage breast cancer

et al. 2021

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“…M L G Vane et al. found a significant difference in negative predictive value (NPV) between triple-negative tumors and HER2+ tumors and between HER2+ and ER/PR+HER2- tumors in the axillary US examination ( 26 ). Jie Fei et al.…”

Section: Discussionmentioning

confidence: 99%

Ultrasound radiomics based on axillary lymph nodes images for predicting lymph node metastasis in breast cancer

Tang,

Wang,

Ou-Yang

et al. 2023

Front. Oncol.

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ObjectivesTo determine whether ultrasound radiomics can be used to distinguish axillary lymph nodes (ALN) metastases in breast cancer based on ALN imaging.MethodsA total of 147 breast cancer patients with 41 non-metastatic lymph nodes and 109 metastatic lymph nodes were divided into a training set (105 ALN) and a validation set (45 ALN). Radiomics features were extracted from ultrasound images and a radiomics signature (RS) was built. The Intraclass correlation coefficients (ICCs), Spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) methods were used to select the ALN status–related features. All images were assessed by two radiologists with at least 10 years of experience in ALN ultrasound examination. The performance levels of the model and radiologists in the training and validation subgroups were then evaluated and compared.ResultRadiomics signature accurately predicted the ALN status, achieved an area under the receiver operator characteristic curve of 0.929 (95%CI, 0.881-0.978) and area under curve(AUC) of 0.919 (95%CI, 95%CI, 0.841-0.997) in training and validation cohorts respectively. The radiomics model performed better than two experts’ prediction of ALN status in both cohorts (P<0.05). Besides, prediction in subgroups based on baseline clinicopathological information also achieved good discrimination performance, with an AUC of 0.937, 0.918, 0.885, 0.930, and 0.913 in HR+/HER2-, HER2+, triple-negative, tumor sized ≤ 3cm and tumor sized>3 cm, respectively.ConclusionThe radiomics model demonstrated a good ability to predict ALN status in patients with breast cancer, which might provide essential information for decision-making.

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“…While there remains an association between RCB and the number of axillary lymph nodes with residual disease, there is no significant effect (p > 0.05) of subtype on this association [59]. Indeed, the sensitivity, specificity, and positive predictive value (PPV) for lymph node detection were found to be independent of subtype, although the negative predictive value (NPV) was highest for TN and lowest for HER2+ cancers [61].…”

Section: Ultrasoundmentioning

confidence: 94%

Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy

Jones

Hathi

Freimanis

et al. 2020

Cancers

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In recent years, neoadjuvant treatment trials have shown that breast cancer subtypes identified on the basis of genomic and/or molecular signatures exhibit different response rates and recurrence outcomes, with the implication that subtype-specific treatment approaches are needed. Estrogen receptor-positive (ER+) breast cancers present a unique set of challenges for determining optimal neoadjuvant treatment approaches. There is increased recognition that not all ER+ breast cancers benefit from chemotherapy, and that there may be a subset of ER+ breast cancers that can be treated effectively using endocrine therapies alone. With this uncertainty, there is a need to improve the assessment and to optimize the treatment of ER+ breast cancers. While pathology-based markers offer a snapshot of tumor response to neoadjuvant therapy, non-invasive imaging of the ER disease in response to treatment would provide broader insights into tumor heterogeneity, ER biology, and the timing of surrogate endpoint measurements. In this review, we provide an overview of the current landscape of breast imaging in neoadjuvant studies and highlight the technological advances in each imaging modality. We then further examine some potential imaging markers for neoadjuvant treatment response in ER+ breast cancers.

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Does the subtype of breast cancer affect the diagnostic performance of axillary ultrasound for nodal staging in breast cancer patients?

Cited by 5 publications

References 27 publications

Ultrasonography and ultrasound-guided fine-needle aspiration biopsy can predict a heavy nodal metastatic burden in early-stage breast cancer

Ultrasonography and ultrasound-guided fine-needle aspiration biopsy can predict a heavy nodal metastatic burden in early-stage breast cancer

Ultrasound radiomics based on axillary lymph nodes images for predicting lymph node metastasis in breast cancer

Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy

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