Evidence map›Paper›PMID 41966754›Full record

ArticleInternational journal of dermatology2026

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

Nieves Martínez Campayo, Sabela Paradela de la Morena, Sebastian Podlipnik, Susana Puig, Josep Malvehy, Cristina Carrera, Eduardo Nagore, Ruggero Moro, Aram Boada, Nina Richarz and 11 more

Abstract readMulticenter Study
In one paragraph

Article in International journal of dermatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Nieves Martínez CampayoDermatology Department, Complejo Hospitalario Universitario de A Coruña, A Coruña, Spain.ORCID https://orcid.org/0000-0003-2297-2970
Sabela Paradela de la MorenaDermatology Department, Complejo Hospitalario Universitario de A Coruña, A Coruña, Spain.ORCID https://orcid.org/0000-0002-6779-6459
Sebastian PodlipnikDermatology Department, Hospital Clínic, IDIBAPS, University of Barcelona/CIBERER, Instituto de Salud Carlos III, Barcelona, Spain.ORCID https://orcid.org/0000-0003-4150-0522
Susana PuigDermatology Department, Hospital Clínic, IDIBAPS, University of Barcelona/CIBERER, Instituto de Salud Carlos III, Barcelona, Spain.ORCID https://orcid.org/0000-0003-1337-9745
Josep MalvehyDermatology Department, Hospital Clínic, IDIBAPS, University of Barcelona/CIBERER, Instituto de Salud Carlos III, Barcelona, Spain.ORCID https://orcid.org/0000-0002-6998-914X
Cristina CarreraDermatology Department, Hospital Clínic, IDIBAPS, University of Barcelona/CIBERER, Instituto de Salud Carlos III, Barcelona, Spain.ORCID https://orcid.org/0000-0003-1608-8820
Eduardo NagoreDermatology Department, Instituto Valenciano de Oncología (IVO), Valencia, Spain.ORCID https://orcid.org/0000-0003-3433-8707
Ruggero MoroDermatology Department, Instituto Valenciano de Oncología (IVO), Valencia, Spain.ORCID https://orcid.org/0000-0002-4837-4998
Aram BoadaDermatology Department, Hospital Universitari Germans Trias i Pujol/Institut D'investigació Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain.ORCID https://orcid.org/0000-0001-9809-5308
Nina RicharzDermatology Department, Hospital Universitari Germans Trias i Pujol/Institut D'investigació Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain.ORCID https://orcid.org/0000-0003-0027-8769
Pedro Rodríguez-JiménezDermatology Department, Hospital Universitario de La Princesa, Madrid, Spain.ORCID https://orcid.org/0000-0002-7061-3027
Pablo BoneteDermatology Department, Hospital Universitario de La Princesa, Madrid, Spain.ORCID https://orcid.org/0009-0007-7291-4441
Clara Buendía-MartínezDermatology Department, Hospital Universitario de La Princesa, Madrid, Spain.ORCID https://orcid.org/0009-0007-2621-9472
Blanca de UnamunoDermatology Department, Hospital Universitari i Politècnic La Fe/Instituto de Investigación Sanitaria La Fe (IIS La Fe) / Facultad de Medicina, Universidad de Valencia, Valencia, Spain.ORCID https://orcid.org/0000-0003-4073-145X
Rafael Botella-EstradaDermatology Department, Hospital Universitari i Politècnic La Fe/Instituto de Investigación Sanitaria La Fe (IIS La Fe) / Facultad de Medicina, Universidad de Valencia, Valencia, Spain.ORCID https://orcid.org/0000-0002-6153-8192
Pablo Fernández-Fernández-Dermatology Department, Hospital Universitario de Salamanca, Salamanca, Spain.ORCID https://orcid.org/0009-0006-5185-573X
Javier Cañueto-Dermatology Department, Hospital Universitario de Salamanca, Salamanca, Spain.ORCID https://orcid.org/0000-0003-0037-7353
Juan Ignacio Bugallo SanzPlastic Surgery Department, Complejo Hospitalario Universitario de A Coruña, A Coruña, Spain.ORCID https://orcid.org/0000-0002-7020-6624
Oihane García-SenosiainPlastic Surgery Department, Hospital Universitari Germans Trias i Pujol, Institut D'investigació Germans Trias i Pujol/Universitat Autònoma de Barcelona, Badalona, Spain.
Eduardo FonsecaDermatology Department, Complejo Hospitalario Universitario de A Coruña, A Coruña, Spain.ORCID https://orcid.org/0000-0002-8048-2681
Antonio Tejera VaquerizoDermatology Department, Instituto Dermatológico GlobalDerm, Córdoba, Spain.ORCID https://orcid.org/0000-0002-8987-0744

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSentinel 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.

objectivesThe 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.

methodsA 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.

resultsThe 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.

conclusionsThe 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.

Indexed as

MelanomaNomogramsSentinel Lymph Node BiopsySkin NeoplasmsAgedAged, 80 and overFemaleHumansLogistic ModelsLymphatic MetastasisMaleRetrospective StudiesRisk AssessmentSoftware Validationelderly patientsmelanomanomogrampredictive modelrisk stratificationsentinel lymph node biopsy

Identifiers

PMID41966754
PMCPMC13569241

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.