BACKGROUND: The threshold and indications for sentinel lymph node (SLN) biopsy in patients with melanoma remain somewhat arbitrary. Many variables associated with SLN positivity have previously been identified, including a significant association between the American Joint Committee on Cancer (AJCC) staging system and SLN status. We developed a user-friendly nomogram that takes several characteristics into account simultaneously to more accurately predict the presence of SLN metastasis for an individual patient. METHODS: A total of 979 patients who underwent successful SLN biopsy for cutaneous melanoma at a single institution between February 1991 and November 2003 were included in the analysis. Predictors were used to develop a nomogram, based on logistic regression analysis, to predict the probability of SLN positivity. A large multi-institutional trial with 3108 patients was used to validate the predictive accuracy of the nomogram compared with the AJCC staging system. RESULTS: The nomogram was developed and found to be accurate and discriminating. The concordance index of the nomogram, a measure of predictive ability, was .694 when evaluated with the validation dataset. In contrast, the concordance index of the AJCC staging system was lower (.663; P < .001). CONCLUSIONS: Using commonly available clinicopathologic information, we developed a nomogram to accurately predict the probability of a positive SLN in patients with melanoma. This tool takes several characteristics into account simultaneously. This model should enable improved patient counseling and treatment selection.
BACKGROUND: The threshold and indications for sentinel lymph node (SLN) biopsy in patients with melanoma remain somewhat arbitrary. Many variables associated with SLN positivity have previously been identified, including a significant association between the American Joint Committee on Cancer (AJCC) staging system and SLN status. We developed a user-friendly nomogram that takes several characteristics into account simultaneously to more accurately predict the presence of SLN metastasis for an individual patient. METHODS: A total of 979 patients who underwent successful SLN biopsy for cutaneous melanoma at a single institution between February 1991 and November 2003 were included in the analysis. Predictors were used to develop a nomogram, based on logistic regression analysis, to predict the probability of SLN positivity. A large multi-institutional trial with 3108 patients was used to validate the predictive accuracy of the nomogram compared with the AJCC staging system. RESULTS: The nomogram was developed and found to be accurate and discriminating. The concordance index of the nomogram, a measure of predictive ability, was .694 when evaluated with the validation dataset. In contrast, the concordance index of the AJCC staging system was lower (.663; P < .001). CONCLUSIONS: Using commonly available clinicopathologic information, we developed a nomogram to accurately predict the probability of a positive SLN in patients with melanoma. This tool takes several characteristics into account simultaneously. This model should enable improved patient counseling and treatment selection.
Authors: Andrea Maurichi; Rosalba Miceli; Hanna Eriksson; Julia Newton-Bishop; Jérémie Nsengimana; May Chan; Andrew J Hayes; Kara Heelan; David Adams; Roberto Patuzzo; Francesco Barretta; Gianfranco Gallino; Catherine Harwood; Daniele Bergamaschi; Dorothy Bennett; Konstantinos Lasithiotakis; Paola Ghiorzo; Bruna Dalmasso; Ausilia Manganoni; Francesca Consoli; Ilaria Mattavelli; Consuelo Barbieri; Andrea Leva; Umberto Cortinovis; Vittoria Espeli; Cristina Mangas; Pietro Quaglino; Simone Ribero; Paolo Broganelli; Giovanni Pellacani; Caterina Longo; Corrado Del Forno; Lorenzo Borgognoni; Serena Sestini; Nicola Pimpinelli; Sara Fortunato; Alessandra Chiarugi; Paolo Nardini; Elena Morittu; Antonio Florita; Mara Cossa; Barbara Valeri; Massimo Milione; Giancarlo Pruneri; Odysseas Zoras; Andrea Anichini; Roberta Mortarini; Mario Santinami Journal: J Clin Oncol Date: 2020-03-13 Impact factor: 44.544
Authors: Jacob S Ankeny; Brian Labadie; Jason Luke; Eddy Hsueh; Jane Messina; Jonathan S Zager Journal: Clin Exp Metastasis Date: 2018-05-02 Impact factor: 5.150
Authors: Aimeé M Crago; Brian Denton; Sébastien Salas; Armelle Dufresne; James J Mezhir; Meera Hameed; Mithat Gonen; Samuel Singer; Murray F Brennan Journal: Ann Surg Date: 2013-08 Impact factor: 12.969
Authors: A Mitra; C Conway; C Walker; M Cook; B Powell; S Lobo; M Chan; M Kissin; G Layer; J Smallwood; C Ottensmeier; P Stanley; H Peach; H Chong; F Elliott; M M Iles; J Nsengimana; J H Barrett; D T Bishop; J A Newton-Bishop Journal: Br J Cancer Date: 2010-09-21 Impact factor: 7.640