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Comparison of five different popular scoring systems to predict nonsentinel lymph node status in patients with metastatic sentinel lymph nodes: a tertiary care center experience.

Yıldız R, Urkan M, Hancerliogulları O, Kılbaş Z, Ozturk E, Mentes MO, Gorgulu S - Springerplus (2015)

Bottom Line: We have determined only two clinicopathologic (multifocality and size of the primary tumor) situations which have a statistically significant association between SLN metastasis with using a multivariate logistic regression analysis.Multifocality (P = 0.001) and size of the primary tumor (P = 0.001) were associated with a higher probability of-SLN metastasis.Currently published predictive models lack accuracy when applied to a different population.

View Article: PubMed Central - PubMed

Affiliation: Department of Surgery, Gulhane Military Medical Academy, Etlik, 06018 Ankara, Turkey.

ABSTRACT
Sentinel lymph node biopsy (SLNB) is the current standard of care for breast cancers with no clinically palpable axillary lymph nodes. Almost 50 % of sentinel lymph node positive patients have negative non-sentinel nodes and undergo non-therapeutic axillary dissection. Five different scoring systems, reported in the literature, were compared for their predictive ability of non-SLN involvement in patients with SLN positive breast cancer. 242 patients who underwent breast surgery and SLNB were included in the study. Of these, 70 who were confirmed to have SLN metastasis and received complementary ALND and constituted the final study population. The nomograms (MSKCC, M.D. Anderson Cancer Center, Tenon model, Stanford and Turkish) were statistically compared for their prediction of non-SLN metastasis (95 % confidence interval). We have determined only two clinicopathologic (multifocality and size of the primary tumor) situations which have a statistically significant association between SLN metastasis with using a multivariate logistic regression analysis. Multifocality (P = 0.001) and size of the primary tumor (P = 0.001) were associated with a higher probability of-SLN metastasis. No predictive model was constructed that showed good area under the curve (AUC) discrimination in the validation series. Currently published predictive models lack accuracy when applied to a different population. Multi-institutional heterogenic population studies are important to determine the exact combination of scoring systems and/or nomograms.

No MeSH data available.


Related in: MedlinePlus

AUC values for different nomograms. AUC area under the curve, MSKCC Memorial Sloan Kettering Cancer Center, ROC receiver operating characteristic
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Fig1: AUC values for different nomograms. AUC area under the curve, MSKCC Memorial Sloan Kettering Cancer Center, ROC receiver operating characteristic

Mentions: We determined five scoring systems (MSKCC, M.D. Anderson Cancer Center Tenon model, Stanford and Turkish) which were adopted for the determination of the Non-SLN metastasis. The ROC curve of these scoring systems and nomograms (MSKCC, MD Anderson, Tenon, Stanford and Turkish) were drawn using substantiation (n: 70) datasets. Then, discrimination of score systems and nomograms was assessed by generating Receiver Operating Characteristic (ROC) curves, calculating the area under the receiver operating characteristic curve (AUC) with a 95 % confidence interval (95 % CI) for each model. The AUC varies between 0.5 and 1.0, and a value greater than 0.70 was considered to demonstrate a good discrimination. The AUCs in the validation datasets were 0.525, 0.534, 0.520, 0.534 and 0.6050 (MSKCC, M.D. Anderson Cancer Center Tenon model, Stanford and Turkish) respectively (Fig. 1). When an identical validation dataset was applied, the AUCs of the five models were not significantly different. The AUCs of Turkısh scoring system was slightly higher than the other four scoring systems. However, no statistically significant difference was observed between Turkısh and the other scoring systems (Table 3). The values for predicting the probability of having further metastases in NSLNs for the MSKCC, MDACC, Tenon, Stanford and Turkish models were 45, 54.3, 45, 48.6, and 61.5 %, respectively. And, for negative predictive probability was 46.7, 57.11, 46.7, 51.5, and 67.7 % respectively. Positive and negative predicted probability (P) values (%) and their sensitivity and specificity values are shown in Table 3.Fig. 1


Comparison of five different popular scoring systems to predict nonsentinel lymph node status in patients with metastatic sentinel lymph nodes: a tertiary care center experience.

Yıldız R, Urkan M, Hancerliogulları O, Kılbaş Z, Ozturk E, Mentes MO, Gorgulu S - Springerplus (2015)

AUC values for different nomograms. AUC area under the curve, MSKCC Memorial Sloan Kettering Cancer Center, ROC receiver operating characteristic
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4628030&req=5

Fig1: AUC values for different nomograms. AUC area under the curve, MSKCC Memorial Sloan Kettering Cancer Center, ROC receiver operating characteristic
Mentions: We determined five scoring systems (MSKCC, M.D. Anderson Cancer Center Tenon model, Stanford and Turkish) which were adopted for the determination of the Non-SLN metastasis. The ROC curve of these scoring systems and nomograms (MSKCC, MD Anderson, Tenon, Stanford and Turkish) were drawn using substantiation (n: 70) datasets. Then, discrimination of score systems and nomograms was assessed by generating Receiver Operating Characteristic (ROC) curves, calculating the area under the receiver operating characteristic curve (AUC) with a 95 % confidence interval (95 % CI) for each model. The AUC varies between 0.5 and 1.0, and a value greater than 0.70 was considered to demonstrate a good discrimination. The AUCs in the validation datasets were 0.525, 0.534, 0.520, 0.534 and 0.6050 (MSKCC, M.D. Anderson Cancer Center Tenon model, Stanford and Turkish) respectively (Fig. 1). When an identical validation dataset was applied, the AUCs of the five models were not significantly different. The AUCs of Turkısh scoring system was slightly higher than the other four scoring systems. However, no statistically significant difference was observed between Turkısh and the other scoring systems (Table 3). The values for predicting the probability of having further metastases in NSLNs for the MSKCC, MDACC, Tenon, Stanford and Turkish models were 45, 54.3, 45, 48.6, and 61.5 %, respectively. And, for negative predictive probability was 46.7, 57.11, 46.7, 51.5, and 67.7 % respectively. Positive and negative predicted probability (P) values (%) and their sensitivity and specificity values are shown in Table 3.Fig. 1

Bottom Line: We have determined only two clinicopathologic (multifocality and size of the primary tumor) situations which have a statistically significant association between SLN metastasis with using a multivariate logistic regression analysis.Multifocality (P = 0.001) and size of the primary tumor (P = 0.001) were associated with a higher probability of-SLN metastasis.Currently published predictive models lack accuracy when applied to a different population.

View Article: PubMed Central - PubMed

Affiliation: Department of Surgery, Gulhane Military Medical Academy, Etlik, 06018 Ankara, Turkey.

ABSTRACT
Sentinel lymph node biopsy (SLNB) is the current standard of care for breast cancers with no clinically palpable axillary lymph nodes. Almost 50 % of sentinel lymph node positive patients have negative non-sentinel nodes and undergo non-therapeutic axillary dissection. Five different scoring systems, reported in the literature, were compared for their predictive ability of non-SLN involvement in patients with SLN positive breast cancer. 242 patients who underwent breast surgery and SLNB were included in the study. Of these, 70 who were confirmed to have SLN metastasis and received complementary ALND and constituted the final study population. The nomograms (MSKCC, M.D. Anderson Cancer Center, Tenon model, Stanford and Turkish) were statistically compared for their prediction of non-SLN metastasis (95 % confidence interval). We have determined only two clinicopathologic (multifocality and size of the primary tumor) situations which have a statistically significant association between SLN metastasis with using a multivariate logistic regression analysis. Multifocality (P = 0.001) and size of the primary tumor (P = 0.001) were associated with a higher probability of-SLN metastasis. No predictive model was constructed that showed good area under the curve (AUC) discrimination in the validation series. Currently published predictive models lack accuracy when applied to a different population. Multi-institutional heterogenic population studies are important to determine the exact combination of scoring systems and/or nomograms.

No MeSH data available.


Related in: MedlinePlus