Use of Established Nomograms to Predict Non-Sentinel Lymph Node Metastasis

Matsen, Cindy B.; Zee, Kimberly J. Van

doi:10.1007/s12609-013-0137-5

Cited by 2 publications

(3 citation statements)

References 76 publications

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“…Axillary lymph node status is an important prognostic factor in disease staging and treatment planning after surgery for breast cancer. This study shows that high nodal burden in early breast cancer could be predicted with an appropriate predictive model as seen in other studies before [4][5][6][7][8][9][10][11]. It is complementary to the recent randomized clinical trials that have changed the look on axillary nodal clearance [1][2][3]28].…”

Section: Discussionsupporting

confidence: 57%

“…Many breast cancer nomograms have been developed to help the clinical decision making between ALND and no ALND [4][5][6]. After the publication of ACOSOG Z0011 trial's results [3], there has been a trend to develop nomograms that use preoperative factors and that are better at predicting a high risk of three to four or more LN metastases i.e., finding the patients that must undergo complete ALND [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer?

et al. 2020

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Background:The current standard for evaluating axillary nodal burden in clinically node negative breast cancer is sentinel lymph node biopsy (SLNB). However, the accuracy of SLNB to detect nodal stage N2-3 remains debatable. Nomograms can help the decision-making process between axillary treatment options. The aim of this study was to create a new model to predict the nodal stage N2-3 after a positive SLNB using machine learning methods that are rarely seen in nomogram development. Material and methods: Primary breast cancer patients who underwent SLNB and axillary lymph node dissection (ALND) between 2012 and 2017 formed cohorts for nomogram development (training cohort, N ¼ 460) and for nomogram validation (validation cohort, N ¼ 70). A machine learning method known as the gradient boosted trees model (XGBoost) was used to determine the variables associated with nodal stage N2-3 and to create a predictive model. Multivariate logistic regression analysis was used for comparison. Results: The best combination of variables associated with nodal stage N2-3 in XGBoost modeling included tumor size, histological type, multifocality, lymphovascular invasion, percentage of ER positive cells, number of positive sentinel lymph nodes (SLN) and number of positive SLNs multiplied by tumor size. Indicating discrimination, AUC values for the training cohort and the validation cohort were 0.80 (95%CI 0.71-0.89) and 0.80 (95%CI 0.65-0.92) in the XGBoost model and 0.85 (95%CI 0.77-0.93) and 0.75 (95%CI 0.58-0.89) in the logistic regression model, respectively. Conclusions: This machine learning model was able to maintain its discrimination in the validation cohort better than the logistic regression model. This indicates advantages in employing modern artificial intelligence techniques into nomogram development. The nomogram could be used to help identify nodal stage N2-3 in early breast cancer and to select appropriate treatments for patients.

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Section: Discussionsupporting

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer?

et al. 2020

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“…Breast cancer nomograms are commonly designed for predicting the risk of axillary lymph node (ALN) metastases. Their relevance has been discussed after the publication of notable clinical trials, minimizing axillary operations [1][2][3]. Nomograms could help extrapolate trial results into other populations [4] and support the decision-making for those whose nodal status is unknown [5].…”

Section: Introductionmentioning

confidence: 99%

Are Breast Cancer Nomograms Still Valid to Predict the Need for Axillary Dissection?

et al. 2021

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Background: Nomograms can help in estimating the nodal status among clinically node-negative patients. Yet their validity in external cohorts over time is unknown. If the nodal stage can be estimated preoperatively, the need for axillary dissection can be decided. Objectives: The aim of this study was to validate three existing nomograms predicting 4 or more axillary lymph node metastases. Method: The risk for ≥4 lymph node metastases was calculated for n = 529 eligible breast cancer patients using the nomograms of Chagpar et al. [Ann Surg Oncol. 2007;14:670–7], Katz et al. [J Clin Oncol. 2008;26(13):2093–8], and Meretoja et al. [Breast Cancer Res Treat. 2013;138(3):817–27]. Discrimination and calibration were calculated for each nomogram to determine their validity. Results: In this cohort, the AUC values for the Chagpar, Katz, and Meretoja models were 0.79 (95% CI 0.74–0.83), 0.87 (95% CI 0.83–0.91), and 0.82 (95% CI 0.76–0.86), respectively, showing good discrimination between patients with and without high nodal burdens. Conclusion: This study presents support for the use of older breast cancer nomograms and confirms their current validity in an external population.

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Use of Established Nomograms to Predict Non-Sentinel Lymph Node Metastasis

Cited by 2 publications

References 76 publications

Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer?

Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer?

Are Breast Cancer Nomograms Still Valid to Predict the Need for Axillary Dissection?

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