Improving Risk Stratification for SLNB in Elderly Patients With Melanoma: The SENTINOLD Nomogram

BACKGROUND: Sentinel lymph node biopsy (SLNB) is one of the strongest prognostic indicators to date for cutaneous melanoma. However, its indication in elderly patients is controversial due to lower prognostic benefits, comorbidities, and surgical risks. OBJECTIVES: The aim of this study was to develop and validate a predictive model specifically tailored to patients aged ≥ 75 years with cutaneous melanoma, and to compare its performance with the Melanoma Institute Australia (MIA) and Memorial Sloan Kettering Cancer Center (MSKCC) calculators. METHODS: A multicenter, retrospective cohort study (SENTINOLD) including 795 patients aged ≥ 75 years was conducted across seven Spanish hospitals, representing one of the largest European datasets of elderly melanoma patients. A multivariable logistic regression model was developed using 12 clinicopathologic variables. Model performance was assessed by discrimination (AUC), calibration, and decision curve analysis (DCA), and compared with MIA and MSKCC calculators. Temporal validation was performed using a hold-out dataset representing the most recent diagnoses. RESULTS: The SENTINOLD nomogram demonstrated robust discrimination (AUC = 0.71), outperforming the MSKCC (0.66) and MIA (0.64) models (p < 0.05). Calibration and decision curve analysis confirmed greater clinical usefulness within the 5%-20% decision threshold range. Temporal validation showed stable performance (AUC = 0.68; Brier = 0.187). An interactive web-based calculator is freely available at https://sentinold.netlify.app, allowing clinicians to estimate the risk of SLN positivity using key tumor and patient characteristics. CONCLUSIONS: The SENTINOLD nomogram provides a reliable, user-friendly, and population-specific tool for individualized prediction of sentinel lymph node positivity in elderly melanoma patients. Its use may assist clinicians in guiding SLNB decision-making in this age group, although independent external validation and prospective evaluation of clinical impact are warranted.

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Journal
International Journal of Dermatology
Published
2026-04-12
DOI
https://doi.org/10.1111/ijd.70413
Primary Topic
Cutaneous Melanoma Detection and Management
Type
article
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article

Improving Risk Stratification for SLNB in Elderly Patients With Melanoma: The SENTINOLD Nomogram

Antonio Tejera Vaquerizo, Juan Ignacio Bugallo Sanz, Pedro Rodríguez‐Jiménez, N. Martínez Campayo et al.
International Journal of Dermatology
Cutaneous Melanoma Detection and Management
article

Improving Risk Stratification for SLNB in Elderly Patients With Melanoma: The SENTINOLD Nomogram

Antonio Tejera Vaquerizo, Juan Ignacio Bugallo Sanz, Pedro Rodríguez‐Jiménez, N. Martínez Campayo, Nina A. Richarz, Cristina Carrera, Sebastián Podlipnik, A. Boada, Blanca de Unamuno, Eduardo Nagore, Eduardo Fonseca, Susana Puig, Javier Cañueto, Rafael Botella Estrada, Clara Buendía‐Martínez, Sabela Paradela de la Morena, Ruggero Moro, Pablo Fernández‐Fernández, Oihane García‐Senosiain, Josep Malvehy, Pablo Bonete
article en

Abstract

BACKGROUND: Sentinel lymph node biopsy (SLNB) is one of the strongest prognostic indicators to date for cutaneous melanoma. However, its indication in elderly patients is controversial due to lower prognostic benefits, comorbidities, and surgical risks. OBJECTIVES: The aim of this study was to develop and validate a predictive model specifically tailored to patients aged ≥ 75 years with cutaneous melanoma, and to compare its performance with the Melanoma Institute Australia (MIA) and Memorial Sloan Kettering Cancer Center (MSKCC) calculators. METHODS: A multicenter, retrospective cohort study (SENTINOLD) including 795 patients aged ≥ 75 years was conducted across seven Spanish hospitals, representing one of the largest European datasets of elderly melanoma patients. A multivariable logistic regression model was developed using 12 clinicopathologic variables. Model performance was assessed by discrimination (AUC), calibration, and decision curve analysis (DCA), and compared with MIA and MSKCC calculators. Temporal validation was performed using a hold-out dataset representing the most recent diagnoses. RESULTS: The SENTINOLD nomogram demonstrated robust discrimination (AUC = 0.71), outperforming the MSKCC (0.66) and MIA (0.64) models (p < 0.05). Calibration and decision curve analysis confirmed greater clinical usefulness within the 5%-20% decision threshold range. Temporal validation showed stable performance (AUC = 0.68; Brier = 0.187). An interactive web-based calculator is freely available at https://sentinold.netlify.app, allowing clinicians to estimate the risk of SLN positivity using key tumor and patient characteristics. CONCLUSIONS: The SENTINOLD nomogram provides a reliable, user-friendly, and population-specific tool for individualized prediction of sentinel lymph node positivity in elderly melanoma patients. Its use may assist clinicians in guiding SLNB decision-making in this age group, although independent external validation and prospective evaluation of clinical impact are warranted.

International Journal of Dermatology
Instituto de Salud Carlos III (ES), Hospital Universitari i Politècnic La Fe (ES), Complejo Hospitalario de Salamanca (ES), Fundación Instituto Valenciano de Oncología (ES), Hospital Clínic de Barcelona (ES), Hospital Universitario de La Princesa (ES), Institute of Dermatology (TH), Complexo Hospitalario Universitario A Coruña (ES), Hospital Universitari Germans Trias i Pujol (ES), Instituto de Investigación Sanitaria La Fe (ES)
Peace, Justice and strong institutions
Openalex Percentile: Top 25%
Cutaneous Melanoma Detection and Management
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