A mathematical prediction model incorporating molecular subtype for risk of non-sentinel lymph node metastasis in sentinel lymph node-positive breast cancer patients: a retrospective analysis and nomogram development

Wang, Nana; Yang, Zhengjun; Wang, Xue; Chen, Li‐Xuan; Zhao, Hongmeng; Cao, Weijun; Zhang, Bin

doi:10.1007/s12282-018-0863-7

Cited by 22 publications

(23 citation statements)

References 35 publications

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“…First, our research used the clinical information of TNBC patients to predict lymph node metastasis. Meanwhile, existing researches (10,13) assessed TNBC at the genetic level, using IRGS to predict lymph node metastasis, and the obtained results were also good. However, clinical information is more intuitive to make decisions easily in clinic.…”

Section: Discussionmentioning

confidence: 99%

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Nomogram for Predicting Lymph Node Involvement in Triple-Negative Breast Cancer

Cui

Zhu

2020

Front. Oncol.

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BackgroundLymph node metastasis of triple-negative breast cancer (TNBC) is essential in treatment strategy formulation. This study aimed to build a nomogram that predicts lymph node metastasis in patients with TNBC.Materials and MethodsA total of 28,966 TNBC patients diagnosed from 2010 to 2017 in the Surveillance, Epidemiology and End Results (SEER) database were enrolled, and randomized 1:1 into the training and validation sets, respectively. Univariate and multivariate logistic regression analysis were applied to identify the predictive factors, which composed the nomogram. The receiver operating characteristic curves showed the efficacy of the nomogram.ResultMultivariate logistic regression analyses revealed that age, race, tumor size, tumor primary site, and pathological grade were independent predictive factors of lymph node status. Integrating these independent predictive factors, a nomogram was successfully developed for predicting lymph node status, and further validated in the validation set. The areas under the receiver operating characteristic curves of the nomogram in the training and validation sets were 0.684 and 0.689 respectively, showing a satisfactory performance.ConclusionWe constructed a nomogram to predict the lymph node status in TNBC patients. After further validation in additional large cohorts, the nomogram developed here would do better in predicting, providing more information for staging and treatment, and enabling tailored treatment in TNBC patients.

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Section: Discussionmentioning

confidence: 99%

“…Previous studies have constructed nomograms to predict both sentinel and non-sentinel lymph node metastases in breast cancer, performing well in cohorts at different institutions (13)(14)(15). Several well-designed nomograms have been accepted worldwide, with some adopted by clinicians (16)(17)(18)(19)(20).…”

Section: Discussionmentioning

confidence: 99%

Nomogram for Predicting Lymph Node Involvement in Triple-Negative Breast Cancer

Cui

Zhu

2020

Front. Oncol.

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“…Whether NSLN metastasis is associated with the molecular subtype remains controversial. The results from a recent single-centre study of 291 patients demonstrated that patients with luminal B and HER2 overexpression breast cancer had a signi cantly higher possibility of having at least one positive NSLN than patients with luminal A breast cancer [25]. However, in another retrospective study, investigators failed to identify molecular subtype as an independent predictor of NSLN metastasis.…”

Section: Discussionmentioning

confidence: 99%

Development of a prediction model based on LASSO regression to evaluate the risk of non-sentinel lymph node metastasis in Chinese breast cancer patients with 1-2 positive sentinel lymph nodes

Meng

Tian

Wang

et al. 2020

Preprint

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Background: Our study aimed to investigate the correlative factors influencing non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer patients with 1-2 positive sentinel lymph nodes (SLNs) and to develop an intraoperative prediction model based on the least absolute shrinkage and selection operator (LASSO) algorithm to evaluate the risk of NSLN metastasis.Methods: The factors affecting NSLN status were investigated in a cohort of 714 patients with 1-2 positive SLNs treated at The First Affiliated Hospital of Chongqing Medical University between January 2013 and December 2018. A new mathematical prediction model based on the LASSO algorithm was developed and was validated in a cohort of 131 patients treated between January 2019 and December 2019.Results: In the training cohort, 266/714 (37.3%) patients had NSLN metastasis. In univariate analysis, the histologic grade (P =0.010), number of positive SLNs (P<0.001), number of negative SLNs (P<0.001), number of SLNs dissected (P<0.001), SLN metastasis ratio (P <0.001), lymphovascular invasion (LVI) status (P <0.001), estrogen receptor (ER) status (P =0.011), human epidermal growth factor receptor 2 (HER2) status (P =0.005), molecular subtype (P =0.001), and risk score (P <0.001) were related with NSLN involvement. In multivariate analysis, the histologic grade (P =0.026), LVI status (P =0.005), number of positive SLNs (P=0.001), number of negative SLNs (P=0.005), SLN metastasis ratio (P =0.005), and molecular subtype (P=0.007) were identified as the independent predictors of NSLN metastasis. A LASSO regression-based mathematical prediction model was developed and had an area under the curve (AUC) of 0.764 (95% CI: 0.729-0.798). In the 131-patient validation cohort, the AUC was 0.777 (95% CI: 0.692-0.862).Conclusions: We present a new prediction model to assess the risk of NSLN metastasis in Chinese breast cancer patients with 1-2 positive SLNs. The model was further validated in the validation cohort and showed excellent clinical applicability and diagnostic performance. It can be used as an intraoperative clinical tool for clinicians to predict the risk of NSLN metastasis and make the final decision regarding axillary lymph node dissection (ALND).

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“…18,19 In this study, multivariate analysis showed a correlation between molecular subtype and ALN involvement in early breast cancer, which was consistent with the findings of previous studies. 20 However, Xin et al found that there was no significant correlation between molecular subtype and ALN metastasis in 323 patients with newly diagnosed breast cancer and suggested that molecular subtype could not predict ALN involvement currently and could not fully reveal the biological characteristics of tumors. 21 The nomograms based on US characteristics were successfully established to predict the ALN metastasis rate and high tumor burden rate; the AUC values verified externally in the validation group were 0.731 and 0.802, respectively.…”

Section: Discussionmentioning

confidence: 99%

<p>Establishment of Simple Nomograms for Predicting Axillary Lymph Node Involvement in Early Breast Cancer</p>

Zong¹,

Deng²,

Ge³

et al. 2020

CMAR

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These authors contributed equally to this workPurpose: Axillary lymph node (ALN) involvement is an important prognostic factor of early invasive breast cancer. The objective of this study was to establish simple nomograms for predicting ALN involvement based on ultrasound (US) characteristics and evaluate the predictive value of US in the detection of ALN involvement. Patients and Methods: A total of 1328 patients with cT1-2N0 breast cancer by physical exam were retrospectively analyzed. Univariate analysis was used for the comparison of variables, and multivariate analysis was performed by binary logistic regression analysis. The R software was used to establish simple nomograms based on the US characteristics alone. The receiver operating characteristic (ROC) curves of the prediction model and the verification group were drawn, and the area under the curve (AUC) was calculated to evaluate the discrimination of the prediction model. A calibration curve was plotted to assess the nomogram predictions vs the actual observations of the ALN metastasis rate and axillary tumor burden rate. Results: The ALN metastasis rates of the training group and the validation group were 35.1% and 34.1%, respectively. Multivariate analysis showed that molecular subtype, lymphovascular invasion, mass descriptors (size, margin, microcalcification and blood flow signal) and LN descriptors (shape, cortical thickness and long-to-short ratio) were independent impact factors in early breast cancer. The AUC of ALN metastasis rate of prediction model based on US features was 0.802, the AUC of high tumor burden rate was 0.873, and the AUC of external validation group was 0.731 and 0.802, respectively. The calibration curve of the nomogram showed that the nomogram predictions are consistent with the actual metastasis rate and the high tumor burden rate. The results showed that preoperative US had a sensitivity of 59.4% and a specificity of 88.9% for predicting the ALN metastasis rate. Conclusion: The successfully established nomograms based on US characteristics to predict ALN metastasis rate and high axillary tumor burden rate in early breast cancer can achieve individual prediction. Compared with other nomogram predictions, it is more intuitive, and can help clinical decision-making; thus, it should be promoted. However, at this time US features alone are insufficient to replace sentinel lymph node biopsy.

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A mathematical prediction model incorporating molecular subtype for risk of non-sentinel lymph node metastasis in sentinel lymph node-positive breast cancer patients: a retrospective analysis and nomogram development

Cited by 22 publications

References 35 publications

Nomogram for Predicting Lymph Node Involvement in Triple-Negative Breast Cancer

Nomogram for Predicting Lymph Node Involvement in Triple-Negative Breast Cancer

Development of a prediction model based on LASSO regression to evaluate the risk of non-sentinel lymph node metastasis in Chinese breast cancer patients with 1-2 positive sentinel lymph nodes

<p>Establishment of Simple Nomograms for Predicting Axillary Lymph Node Involvement in Early Breast Cancer</p>

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