Predicting axillary sentinel node status in patients with primary breast cancer.

Kolařík, Dušan; Pecha, Václav; Skovajsová, Miroslava; Záhumenský, J; Trnková, Markéta; Petruželka, Luboš; Halaška, Michael; Sottner, Oldrich; Otčenášek, M; Kolářová, Hana

doi:10.4149/neo_2013_045

Cited by 13 publications

(11 citation statements)

References 19 publications

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“…Reyal et al [ 11 ] reported that molecular subtype approximation, including ER, PR and HER2, is also a determinant of ALN status, and another nomogram was later developed (the Paris model). Additionally, several more models [ 9 , 12 – 15 ] have been developed to predict ALN status. However, none of these models has been widely accepted by treatment guidelines, and clinical practice has not significantly changed.…”

Section: Discussionmentioning

confidence: 99%

Development of nomograms to predict axillary lymph node status in breast cancer patients

Chen

Liu

et al. 2017

BMC Cancer

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BackgroundPrediction of axillary lymph node (ALN) status preoperatively is critical in the management of breast cancer patients. This study aims to develop a new set of nomograms to accurately predict ALN status.MethodsWe searched the National Cancer Database to identify eligible female breast cancer patients with profiles containing critical information. Patients diagnosed in 2010–2011 and 2012–2013 were designated the training (n = 99,618) and validation (n = 101,834) cohorts, respectively. We used binary logistic regression to investigate risk factors for ALN status and to develop a new set of nomograms to determine the probability of having any positive ALNs and N2–3 disease. We used ROC analysis and calibration plots to assess the discriminative ability and accuracy of the nomograms, respectively.ResultsIn the training cohort, we identified age, quadrant of the tumor, tumor size, histology, ER, PR, HER2, tumor grade and lymphovascular invasion as significant predictors of ALNs status. Nomogram-A was developed to predict the probability of having any positive ALNs (P_any) in the full population with a C-index of 0.788 and 0.786 in the training and validation cohorts, respectively. In patients with positive ALNs, Nomogram-B was developed to predict the conditional probability of having N2–3 disease (P_con) with a C-index of 0.680 and 0.677 in the training and validation cohorts, respectively. The absolute probability of having N2–3 disease can be estimated by P_any*P_con. Both of the nomograms were well-calibrated.ConclusionsWe developed a set of nomograms to predict the ALN status in breast cancer patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-017-3535-7) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Development of nomograms to predict axillary lymph node status in breast cancer patients

Chen

Liu

et al. 2017

BMC Cancer

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“…Breast cancer is among the most common cancer worldwide and the second cancer-related cause of death in women 1 . Axillary lymph node (ALN) status is one of the most important prognostic and diagnose factor for disease free survival and overall survival in patients with breast cancer 2 . Accurate preoperative identification of ALN status can provide clinicians with important information about their treatment decisions, such as whether axillary lymph node dissection (ALND) in surgery and postoperative adjuvant therapy are needed 2 .…”

Section: Introductionmentioning

confidence: 99%

“…Axillary lymph node (ALN) status is one of the most important prognostic and diagnose factor for disease free survival and overall survival in patients with breast cancer 2 . Accurate preoperative identification of ALN status can provide clinicians with important information about their treatment decisions, such as whether axillary lymph node dissection (ALND) in surgery and postoperative adjuvant therapy are needed 2 . Currently, the intraoperative ALN status in patients with breast cancer is determined by the sentinel lymph node biopsy (SLNB) 3 .…”

Section: Introductionmentioning

confidence: 99%

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Mammography-Based Radiomics Method

Yang

Wang

Yang

et al. 2019

Sci Rep

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It is difficult to accurately assess axillary lymph nodes metastasis and the diagnosis of axillary lymph nodes in patients with breast cancer is invasive and has low-sensitivity preoperatively. This study aims to develop a mammography-based radiomics nomogram for the preoperative prediction of ALN metastasis in patients with breast cancer. This study enrolled 147 patients with clinicopathologically confirmed breast cancer and preoperative mammography. Features were extracted from each patient’s mammography images. The least absolute shrinkage and selection operator regression method was used to select features and build a signature in the primary cohort. The performance of the signature was assessed using support vector machines. We developed a nomogram by incorporating the signature with the clinicopathologic risk factors. The nomogram performance was estimated by its calibration ability in the primary and validation cohorts. The signature was consisted of 10 selected ALN-status-related features. The AUC of the signature from the primary cohort was 0.895 (95% CI, 0.887–0.909) and 0.875 (95% CI, 0.698–0.891) for the validation cohort. The C-Index of the nomogram from the primary cohort was 0.779 (95% CI, 0.752–0.793) and 0.809 (95% CI, 0.794–0.833) for the validation cohort. Our nomogram is a reliable and non-invasive tool for preoperative prediction of ALN status and can be used to optimize current treatment strategy for breast cancer patients.

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“…Patient age, tumor size, and the preoperative axilla lymph node status from US are parameters considered prognostic indicators in breast cancer and can also be acquired before surgery. We hypothesized that these characteristics might have added value in predicting the proliferation and invasiveness of IBC when combined with US features.…”

mentioning

confidence: 99%

The Association Between Ultrasound Features and Biological Properties of Invasive Breast Carcinoma Is Modified by Age, Tumor Size, and the Preoperative Axilla Status

Tong

Sun

Zhou

et al. 2019

J of Ultrasound Medicine

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Objectives-To investigate the value of ultrasound (US) feature-based models in predicting the proliferation and invasiveness of invasive breast cancer (IBC) and to compare the performance of models based solely on US features with models that combined US features, patient age, tumor size, and axilla status from US.Methods-With ethical approval, 746 patients with a pathologic diagnosis of IBC were reviewed for preoperative clinical, US, and postoperative pathologic data. The proliferation and invasiveness properties of the IBC included the histologic grade and Ki-67 status and lymphovascular invasion (LVI) and axillary lymph node metastasis (ALNM), respectively. Logistic regression analyses were used to identify independent risk factors for tumor proliferation and invasiveness.Results-Posterior echo enhancement, calcification, a tumor size larger than 2 cm, and suspicion of ALNM from axillary US were independent risk factors for a high histologic grade and high Ki-67 expression of IBC (P < .05). A posterior echo shadow, patient age younger than 45 years, and suspicious findings on axillary US imaging were independent variables for predicting the presence of LVI and ALNM in IBC (P < .05). Calcification was the independent factor for predicting LVI (P = .013). The predictive performance of the combined models was improved compared with the US feature-based models, with a higher accuracy rate and negative predictive value. The area under curve of the combined models was also significantly higher than that of the single models (P < .05).Conclusions-Compared with the US feature-based models, the combined models yielded better predictive performance. This may provide a more robust model to predict the tumor biological properties of IBC before surgery.

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Predicting axillary sentinel node status in patients with primary breast cancer.

Cited by 13 publications

References 19 publications

Development of nomograms to predict axillary lymph node status in breast cancer patients

Development of nomograms to predict axillary lymph node status in breast cancer patients

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Mammography-Based Radiomics Method

The Association Between Ultrasound Features and Biological Properties of Invasive Breast Carcinoma Is Modified by Age, Tumor Size, and the Preoperative Axilla Status

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