An axilla scoring system to predict non-SLN status in breast cancer patients with SLN involvement
Department of Gynecologic and Breast Cancers, Hôpital Tenon, 4 rue de la Chine, 75020 Paris, France. Breast Cancer Research and Treatment
(Impact Factor: 3.94).
05/2005; 91(2):113-9. DOI: 10.1007/s10549-004-5781-z
Axillary lymph node dissection (ALND) is the current standard of care for breast cancer patients with sentinel lymph node (SN) involvement. However, the SN is the only involved axillary node in a significant proportion of these patients. Here we examined factors predictive of non-SN involvement in patients with a metastatic SN, in order to develop a scoring system for predicting non-SN involvement.
This study was based on a prospective database of 337 patients who underwent SN biopsy for breast cancer, of whom 81 (24%) were SN-positive; we examined factors predictive of non SN involvement in the 71 of these 81 women who underwent complementary ALND. All clinical and histological criteria were recorded and analysed according to non-SN status, by using Chi-2 analysis, Student's t-test, and multivariate logistic regression.
Univariate analysis showed a significant association between non-SN involvement and histological primary tumor size (p=0.0001), SN macrometastasis (p=0.01), the method used to detect SN metastasis (H&E versus immunohistochemistry) (p=0.03), the number of positive SNs (p=0.049), the proportion of involved SNs among all identified SNs (p=0.0001) and lymphovascular invasion (p=0.006). Histological primary tumor size (p=0.006), SN macrometastasis (p=0.02) and the proportion of involved SNs among all identified SNs (p=0.03) remained significantly associated with non-SN status in multivariate analysis. Based on the multivariate analysis, we developed an axilla scoring system (range 0-7) to predict the likelihood of non-SN metastasis in breast cancer patients with SN involvement.
In patients with invasive breast cancer and a positive SN, histological primary tumor size, the size of SN metastases, and the proportion of involved SNs among all identified SNs were independently predictive of non-SN involvement.
Available from: Serhan Z. Derici
- "In our study, we found the AUC value for the Stanford nomogram to be 0.53. Barranger et al. (2005) defined the Tenon nomogram in 2008 based on the data from Hospital Tenon in Paris. "
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The aim of the study was to evaluate the available breast nomograms (MSKCC, Stanford, Tenon) to predict non-sentinel lymph node metastasis (NSLNM) and to determine variables for NSLNM in SLN positive breast cancer patients in our population.
Materials and methods:
We retrospectively reviewed 170 patients who underwent completion axillary lymph node dissection between Jul 2008 and Aug 2010 in our hospital. We validated three nomograms (MSKCC, Stanford, Tenon). The likelihood of having positive NSLNM based on various factors was evaluated by use of univariate analysis. Stepwise multivariate analysis was applied to estimate a predictive model for NSLNM. Four factors were found to contribute significantly to the logistic regression model, allowing design of a new formula to predict non-sentinel lymph node metastasis. The AUCs of the ROCs were used to describe the performance of the diagnostic value of MSKCC, Stanford, Tenon nomograms and our new nomogram.
After stepwise multiple logistic regression analysis, multifocality, proportion of positive SLN to total SLN, LVI, SLN extracapsular extention were found to be statistically significant. AUC results were MSKCC: 0.713/Tenon: 0.671/Stanford: 0.534/DEU: 0.814.
The MSKCC nomogram proved to be a good discriminator of NSLN metastasis in SLN positive BC patients for our population. Stanford and Tenon nomograms were not as predictive of NSLN metastasis. Our newly created formula was the best prediction tool for discriminate of NSLN metastasis in SLN positive BC patients for our population. We recommend that nomograms be validated before use in specific populations, and more than one validated nomogram may be used together while consulting patients.
Asian Pacific journal of cancer prevention: APJCP 12/2012; 13(12):6181-5. DOI:10.7314/APJCP.2012.13.12.6181 · 2.51 Impact Factor
Available from: ncbi.nlm.nih.gov
- "The Memorial Sloan Kettering Cancer Center (MSKCC) nomogram showed a receiver-operator characteristic curve (ROC) of 0.76 . Three additional nomograms from France, Tenon Hospital, Paris , Cambridge, England  and Stanford, USA  have been developed more recently. The predictability of these four different nomograms on NSLNM in breast cancer patients with positive sentinel lymph node biopsy was evaluated in a multi-centre study, the AUC values were 0.705, 0.711, 0.730, and 0.582 for the MSKCC, Cambridge, Stanford, and Tenon models respectively . "
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ABSTRACT: Nodal staging in breast cancer is a key predictor of prognosis. This paper presents the results of potential clinicopathological predictors of axillary lymph node involvement and develops an efficient prediction model to assist in predicting axillary lymph node metastases. Seventy patients with primary early breast cancer who underwent axillary dissection were evaluated. Univariate and multivariate logistic regression were performed to evaluate the association between clinicopathological factors and lymph node metastatic status. A logistic regression predictive model was built from 50 randomly selected patients; the model was also applied to the remaining 20 patients to assess its validity. Univariate analysis showed a significant relationship between lymph node involvement and absence of nm-23 (p = 0.010) and Kiss-1 (p = 0.001) expression. Absence of Kiss-1 remained significantly associated with positive axillary node status in the multivariate analysis (p = 0.018). Seven clinicopathological factors were involved in the multivariate logistic regression model: menopausal status, tumor size, ER, PR, HER2, nm-23 and Kiss-1. The model was accurate and discriminating, with an area under the receiver operating characteristic curve of 0.702 when applied to the validation group. Moreover, there is a need discover more specific candidate proteins and molecular biology tools to select more variables which should improve predictive accuracy.
Sensors 12/2012; 12(7):9936-50. DOI:10.3390/s120709936 · 2.25 Impact Factor
Available from: Jean-Yves Pierga
- "It remains to be determined whether ALND is always required for women with positive SNs on final histology, given that 40% to 70% of these patients have no metastatic non-sentinel lymph nodes (non-SNs) , , , . The likelihood of non-SN metastasis depends on several factors, such as histologic primary tumour size, the size of SN metastasis, the number of positive SNs, the ratio of positive SNs to all removed SNs, and the extracapsular extension status of the positive SNs , , , , , , , . However, none of these characteristics by themselves can identify a subset of patients for whom ALND is unnecessary. "
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ABSTRACT: To decipher the interaction between the molecular subtype classification and the probability of a non-sentinel node metastasis in breast cancer patients with a metastatic sentinel lymph-node, we applied two validated predictors (Tenon Score and MSKCC Nomogram) on two large independent datasets.
Our datasets consisted of 656 and 574 early-stage breast cancer patients with a metastatic sentinel lymph-node biopsy treated at first by surgery. We applied both predictors on the whole dataset and on each molecular immune-phenotype subgroups. The performances of the two predictors were analyzed in terms of discrimination and calibration. Probability of non-sentinel lymph node metastasis was detailed for each molecular subtype.
Similar results were obtained with both predictors. We showed that the performance in terms of discrimination was as expected in ER Positive HER2 negative subgroup in both datasets (MSKCC AUC Dataset 1 = 0.73 [0.69-0.78], MSKCC AUC Dataset 2 = 0.71 (0.65-0.76), Tenon Score AUC Dataset 1 = 0.7 (0.65-0.75), Tenon Score AUC Dataset 2 = 0.72 (0.66-0.76)). Probability of non-sentinel node metastatic involvement was slightly under-estimated. Contradictory results were obtained in other subgroups (ER negative HER2 negative, HER2 positive subgroups) in both datasets probably due to a small sample size issue. We showed that merging the two datasets shifted the performance close to the ER positive HER2 negative subgroup.
We showed that validated predictors like the Tenon Score or the MSKCC nomogram built on heterogeneous population of breast cancer performed equally on the different subgroups analyzed. Our present study re-enforce the idea that performing subgroup analysis of such predictors within less than 200 samples subgroup is at major risk of misleading conclusions.
PLoS ONE 10/2012; 7(10):e47390. DOI:10.1371/journal.pone.0047390 · 3.23 Impact Factor
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