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A new model for predicting non-sentinel lymph node status in Chinese sentinel lymph node positive breast cancer patients.

Liu M, Wang S, Pan L, Yang D, Xie F, Liu P, Guo J, Zhang J, Zhou B - PLoS ONE (2014)

Bottom Line: For predicted probability cut-off points of 10%, the false-negative (FN) rates of MSKCC and SOC were both 4.4%, and the negative predictive value (NPV) 75.0% and 90.0%, respectively.A new model (Peking University People's Hospital, PKUPH) was developed using these three variables.MSKCC nomogram and SOC did not perform as well as their original researches in Chinese patients.

View Article: PubMed Central - PubMed

Affiliation: Breast Disease Center, Peking University People's Hospital, Beijing, China.

ABSTRACT

Background: Our goal is to validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram and Stanford Online Calculator (SOC) for predicting non-sentinel lymph node (NSLN) metastasis in Chinese patients, and develop a new model for better prediction of NSLN metastasis.

Methods: The MSKCC nomogram and SOC were used to calculate the probability of NSLN metastasis in 120 breast cancer patients. Univariate and multivariate analyses were performed to evaluate the relationship between NSLN metastasis and clinicopathologic factors, using the medical records of the first 80 breast cancer patients. A new model predicting NSLN metastasis was developed from the 80 patients.

Results: The MSKCC and SOC predicted NSLN metastasis in a series of 120 patients with an area under the receiver operating characteristic curve (AUC) of 0.688 and 0.734, respectively. For predicted probability cut-off points of 10%, the false-negative (FN) rates of MSKCC and SOC were both 4.4%, and the negative predictive value (NPV) 75.0% and 90.0%, respectively. Tumor size, Kiss-1 expression in positive SLN and size of SLN metastasis were independently associated with NSLN metastasis (p<0.05). A new model (Peking University People's Hospital, PKUPH) was developed using these three variables. The MSKCC, SOC and PKUPH predicted NSLN metastasis in the second 40 patients from the 120 patients with an AUC of 0.624, 0.679 and 0.795, respectively.

Conclusion: MSKCC nomogram and SOC did not perform as well as their original researches in Chinese patients. As a new predictor, Kiss-1 expression in positive SLN correlated independently with NSLN metastasis strongly. PKUPH model achieved higher accuracy than MSKCC and SOC in predicting NSLN metastasis in Chinese patients.

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Related in: MedlinePlus

Area under the receiver operating characteristic curve (AUC) for MSKCC and SOC models(n = 120).Diagonal line represents an AUC of 0.5, indicating a score equal to chance.
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pone-0104117-g001: Area under the receiver operating characteristic curve (AUC) for MSKCC and SOC models(n = 120).Diagonal line represents an AUC of 0.5, indicating a score equal to chance.

Mentions: Forty five (37.5%) of the 120 patients from PKUPH had NSLN metastasis and the descriptive tumor and nodal characteristics of this cohort used in the MSKCC and Stanford nomograms are listed in Table 1. Compared with the MSKCC [11] and Stanford studies [12], difference was found in age, tumor type, nuclear grade, LVI, multifocality, method of SLN detection and size of positive SLN metastasis. The ROC curves generated by MSKCC and Stanford nomograms are shown in Figure 1. The AUC value of the Stanford and MSKCC models was 0.734(95% CI, 0.644–0.825) and 0.688(95% CI, 0.589–0.787), respectively.


A new model for predicting non-sentinel lymph node status in Chinese sentinel lymph node positive breast cancer patients.

Liu M, Wang S, Pan L, Yang D, Xie F, Liu P, Guo J, Zhang J, Zhou B - PLoS ONE (2014)

Area under the receiver operating characteristic curve (AUC) for MSKCC and SOC models(n = 120).Diagonal line represents an AUC of 0.5, indicating a score equal to chance.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4128817&req=5

pone-0104117-g001: Area under the receiver operating characteristic curve (AUC) for MSKCC and SOC models(n = 120).Diagonal line represents an AUC of 0.5, indicating a score equal to chance.
Mentions: Forty five (37.5%) of the 120 patients from PKUPH had NSLN metastasis and the descriptive tumor and nodal characteristics of this cohort used in the MSKCC and Stanford nomograms are listed in Table 1. Compared with the MSKCC [11] and Stanford studies [12], difference was found in age, tumor type, nuclear grade, LVI, multifocality, method of SLN detection and size of positive SLN metastasis. The ROC curves generated by MSKCC and Stanford nomograms are shown in Figure 1. The AUC value of the Stanford and MSKCC models was 0.734(95% CI, 0.644–0.825) and 0.688(95% CI, 0.589–0.787), respectively.

Bottom Line: For predicted probability cut-off points of 10%, the false-negative (FN) rates of MSKCC and SOC were both 4.4%, and the negative predictive value (NPV) 75.0% and 90.0%, respectively.A new model (Peking University People's Hospital, PKUPH) was developed using these three variables.MSKCC nomogram and SOC did not perform as well as their original researches in Chinese patients.

View Article: PubMed Central - PubMed

Affiliation: Breast Disease Center, Peking University People's Hospital, Beijing, China.

ABSTRACT

Background: Our goal is to validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram and Stanford Online Calculator (SOC) for predicting non-sentinel lymph node (NSLN) metastasis in Chinese patients, and develop a new model for better prediction of NSLN metastasis.

Methods: The MSKCC nomogram and SOC were used to calculate the probability of NSLN metastasis in 120 breast cancer patients. Univariate and multivariate analyses were performed to evaluate the relationship between NSLN metastasis and clinicopathologic factors, using the medical records of the first 80 breast cancer patients. A new model predicting NSLN metastasis was developed from the 80 patients.

Results: The MSKCC and SOC predicted NSLN metastasis in a series of 120 patients with an area under the receiver operating characteristic curve (AUC) of 0.688 and 0.734, respectively. For predicted probability cut-off points of 10%, the false-negative (FN) rates of MSKCC and SOC were both 4.4%, and the negative predictive value (NPV) 75.0% and 90.0%, respectively. Tumor size, Kiss-1 expression in positive SLN and size of SLN metastasis were independently associated with NSLN metastasis (p<0.05). A new model (Peking University People's Hospital, PKUPH) was developed using these three variables. The MSKCC, SOC and PKUPH predicted NSLN metastasis in the second 40 patients from the 120 patients with an AUC of 0.624, 0.679 and 0.795, respectively.

Conclusion: MSKCC nomogram and SOC did not perform as well as their original researches in Chinese patients. As a new predictor, Kiss-1 expression in positive SLN correlated independently with NSLN metastasis strongly. PKUPH model achieved higher accuracy than MSKCC and SOC in predicting NSLN metastasis in Chinese patients.

Show MeSH
Related in: MedlinePlus