American journal of surgery · 2020
Performance of models predicting residual lymph node disease in melanoma patients following sentinel lymph node biopsy.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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MacDonald Sandra, Siever Jodi, Baliski Christopher
Аннотация
BACKGROUND: Among melanoma patients with a tumor-positive sentinel node biopsy (SNB), approximately 20% harbor disease in non-sentinel nodes (nSN), as determined by a completion lymph node dissection (CLND). CLND lacks a survival benefit and has high morbidity. This study assesses predictive factors for nSN metastasis and validates five models predicting nSN metastasis.
METHODS: Patients with invasive melanoma were identified from the BC Cancer Agency (2005-2015). Clinicopathological data were collected from 296 patients who underwent a CLND after a positive SNB. Multivariate analysis was completed to assess predictive variables in the study population. Five models were externally validated using overall model performance (Brier score [calibration and discrimination]) and discrimination (area under the ROC curve [AUC]).
RESULTS: Seventy-three patients had nSN metastasis at the time of CLND. The variable most predictive of nSN involvement was lymphovascular invasion (odds ratio [OR] 3.99; 95% confidence interval [CI] 1.67-9.54; p = 0.002). The highest discrimination was Lee et al. (2004) (AUC 0.68 [95% CI 0.61-0.75]), Rossi et al. (2018) (AUC 0.68 [95% CI 0.57-0.77]), and Bertolli et al. (2019) (AUC 0.68 [95% CI 0.60-0.75]). Rossi et al. (2018) had the lowest overall model performance (Brier score 0.44). Rossi et al. (2018) and Bertolli et al. (2019) had the ability to stratify patients to a risk of nSN involvement up to 99% and 95%, respectively.
CONCLUSION: Bertolli et al. (2019) had amongst the highest overall model performance, was the most clinically meaningful and is recommended as the preferred model for predicting nSN metastasis.
METHODS: Patients with invasive melanoma were identified from the BC Cancer Agency (2005-2015). Clinicopathological data were collected from 296 patients who underwent a CLND after a positive SNB. Multivariate analysis was completed to assess predictive variables in the study population. Five models were externally validated using overall model performance (Brier score [calibration and discrimination]) and discrimination (area under the ROC curve [AUC]).
RESULTS: Seventy-three patients had nSN metastasis at the time of CLND. The variable most predictive of nSN involvement was lymphovascular invasion (odds ratio [OR] 3.99; 95% confidence interval [CI] 1.67-9.54; p = 0.002). The highest discrimination was Lee et al. (2004) (AUC 0.68 [95% CI 0.61-0.75]), Rossi et al. (2018) (AUC 0.68 [95% CI 0.57-0.77]), and Bertolli et al. (2019) (AUC 0.68 [95% CI 0.60-0.75]). Rossi et al. (2018) had the lowest overall model performance (Brier score 0.44). Rossi et al. (2018) and Bertolli et al. (2019) had the ability to stratify patients to a risk of nSN involvement up to 99% and 95%, respectively.
CONCLUSION: Bertolli et al. (2019) had amongst the highest overall model performance, was the most clinically meaningful and is recommended as the preferred model for predicting nSN metastasis.