Evaluation of axillary lymph node metastasis burden by preoperative ultrasound in early-stage breast cancer with needle biopsy-proven metastasis

Wang, X.; Chen, L.; Sun, Yan; Zhang, B.

doi:10.1007/s12094-019-02162-3

Cited by 15 publications

(7 citation statements)

References 24 publications

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“…US-reported abnormal shapes of LNs were also reported in previous publications (37). Wang et al (38) discovered that HR and Her2 expression are associated with a high-burden LN status. The number of suspicious LNs detected was also related to outcome when using LN >2 as the cutoff in the prediction of high burden (38).…”

Section: Discussionsupporting

confidence: 64%

“…Wang et al (38) discovered that HR and Her2 expression are associated with a high-burden LN status. The number of suspicious LNs detected was also related to outcome when using LN >2 as the cutoff in the prediction of high burden (38). In this paper, one LN was used as the cutoff for prediction because one abnormal LN on AUS can be predictive of a low burden of LNs (N 1-2 ), as demonstrated by Puri et al (39) Other USdependent characteristics, such as the maximum LN axis and the effacement of the lymphatic hilum, are closely related to a negative LN status (40).…”

Section: Discussionmentioning

confidence: 99%

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Establishment and Validation of The Axillary Lymph Node Burden Using Cone-Beam Computerized Tomography and Ultrasound-Based Prediction Models in T1-2 Breast Cancer Patients

Shen

Cai

et al. 2021

Preprint

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Background: This study aimed to develop and validate models to preoperatively predict the risk of the lymph node (LN) burden based on the Z0011 clinical trial to assist breast cancer surgical decision-making.Methods: Data on 1394 consecutive patients who presented at Sun Yat-sen University Cancer Center for Cone-beam breast computerized tomography (CBBCT) examinations between April 3, 2019, and July 17, 2020, were retrospectively collected. 387 patients who met the inclusion criteria were included and randomly divided into training and validation cohorts. Clinical-pathological information of all patients was recorded, and images were reinterpreted in this study. A bidirectional stepwise method followed by multi-variable analysis was used to incorporate preoperative features and build optimal model sets with the training cohort for prediction of N0 versus N+ and N＜3 versus N≥3.Results: The ROC curves of two models were generated with the training cohort, and their calibration abilities were estimated using 1000 bootstrap resamples. The bias-corrected C-index of the models were 0.779 (95% CI, 0.752–0.793) in model one and 0.809 (95% CI, 0.794–0.833) in model two for the training cohort. Decision curves and clinical impact curves were plotted to evaluate prediction performance for further clinical application. Delong’s test showed comparable performance of both cohorts.Conclusions: Our models were developed as reliable and noninvasive tools for the preoperative prediction of nodal status, and we hope that they can serve as useful tools for the early planning of treatment strategies for breast cancer patients.

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Section: Discussionsupporting

confidence: 64%

Section: Discussionmentioning

confidence: 99%

Establishment and Validation of The Axillary Lymph Node Burden Using Cone-Beam Computerized Tomography and Ultrasound-Based Prediction Models in T1-2 Breast Cancer Patients

Shen

Cai

et al. 2021

Preprint

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“…Some studies demonstrated that the number and morphology of abnormal lymph nodes detected by axillary US were predictors of nodal burden 28 , but the diagnostic performances were poor with AUC of 0.725-0.747 (refs. 33,34 ). In our study, the overall diagnostic performance of preoperative axillary US results was low with an AUC of 0.735, which was concordant with previous studies 35,36 .…”

Section: Discussionmentioning

confidence: 99%

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

et al. 2020

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Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Here, we report deep learning radiomics (DLR) of conventional ultrasound and shear wave elastography of breast cancer for predicting ALN status preoperatively in patients with early-stage breast cancer. Clinical parameter combined DLR yields the best diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastasis with areas under the receiver operating characteristic curve (AUC) of 0.902 (95% confidence interval [CI]: 0.843, 0.961) in the test cohort. This clinical parameter combined DLR can also discriminate between low and heavy metastatic burden of axillary disease with AUC of 0.905 (95% CI: 0.814, 0.996) in the test cohort. Our study offers a noninvasive imaging biomarker to predict the metastatic extent of ALN for patients with early-stage breast cancer.

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“…These studies concluded that the mean number of positive axillary nodes is significantly higher amongst patients with their positive nodes identified by AxUS than by SNB [14, 19, 24]. Further, the presence of extensive nodal involvement is significantly associated with the number of sonographically suspicious nodes in these studies [12, 17, 18, 21, 25–31]. For instance, Kim et al [18] showed that the percentage of patients with ≥3 positive nodes was 3.1% vs. 38.5% vs. 62.5% in case of ≤1, 2, or ≥3 sonographically suspicious nodes ( P < 0.001).…”

Section: Resultsmentioning

confidence: 88%

Can We Identify or Exclude Extensive Axillary Nodal Involvement in Breast Cancer Patients Preoperatively?

Leenders¹,

Kramer²,

Belghazi³

et al. 2019

Journal of Oncology

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Background Breast cancer treatment has rapidly changed in the last few years. Particularly, treatment of patients with axillary nodal involvement has evolved after publication of several randomized clinical trials. Omitting axillary lymph node dissection in selected early breast cancer patients with one or two positive sentinel nodes did not compromise overall survival nor regional disease control in these trials. Hence, either excluding or identifying extensive axillary nodal involvement becomes increasingly important. Purpose To evaluate whether the current diagnostic modalities can accurately identify or exclude extensive axillary nodal involvement. Evaluated modalities were axillary ultrasound, ultrasound-guided needle biopsy, MRI, and PET/CT. Methods A literature search was performed in the Cochrane Library, EMBASE, and PubMed databases up to June 2019. The search strategy included terms for breast cancer, lymph nodes, and the different imaging modalities. Only articles that reported pathological N-stage or the total number of positive axillary lymph nodes were considered for inclusion. Studies with patients undergoing neoadjuvant systemic therapy were excluded. Conclusion There is no evidence that any of the current preoperative axillary imaging modalities can accurately exclude or identify breast cancer patients with extensive nodal involvement. Both negative PET/CT and negative MRI scans (with gadolinium-based contrast agents) are promising in excluding extensive nodal involvement. Larger studies should be performed to strengthen this conclusion. False-negative rates of axillary ultrasound and ultrasound-guided needle biopsy are too high to rely on negative results of these modalities in excluding extensive nodal involvement.

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Evaluation of axillary lymph node metastasis burden by preoperative ultrasound in early-stage breast cancer with needle biopsy-proven metastasis

Cited by 15 publications

References 24 publications

Establishment and Validation of The Axillary Lymph Node Burden Using Cone-Beam Computerized Tomography and Ultrasound-Based Prediction Models in T1-2 Breast Cancer Patients

Establishment and Validation of The Axillary Lymph Node Burden Using Cone-Beam Computerized Tomography and Ultrasound-Based Prediction Models in T1-2 Breast Cancer Patients

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

Can We Identify or Exclude Extensive Axillary Nodal Involvement in Breast Cancer Patients Preoperatively?

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