Evidence map›Paper›PMID 41369816›Full record

ArticleAnnals of surgical oncology2026

Validation of Updated Sarculator Nomogram for Primary Retroperitoneal Sarcoma in the United States.

Mason Stillman, N Joseph Espat, Steve Kwon

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

2 citing papers in PubMed.

  1. Review
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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Mason StillmanDepartment of Surgery, Columbia University Irving Medical Center, New York, NY, USA.
N Joseph EspatDepartment of Surgery, Division of Surgical Oncology, Roger Williams Medical Center, Providence, RI, USA.
Steve KwonDepartment of Surgery, Division of Surgical Oncology, Roger Williams Medical Center, Providence, RI, USA. steve.kwon@chartercare.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Sarculator nomogram for retroperitoneal sarcoma (RPS) was recently updated to include the variable 'hospital volume', validated using primarily non-U.S.-based patient data. We sought to validate the updated nomogram using U.S. data, examine various cutoffs for high-volume center (HVC) designation (≥ 13 cases per year), and investigate factors that may further improve the nomogram.

methodsA nationwide retrospective analysis using the National Cancer Database evaluated patients with RPS from 2010 to 2021. The Cox proportional hazards model was used to evaluate the impact of treatment at HVCs on overall survival (OS) using multiple case/year definitions. A model mimicking Sarculator's parameters was constructed then compared against sequential additions of variables found to be predictive in the initial analysis.

resultsA total of 4537 patients were included, 352 of whom were treated at HVCs. Treatment at an HVC using this cutoff was associated with improved OS (hazard ratio [HR] 0.62; 95% confidence interval [CI] 0.43-0.90), whereas there was no association with OS using cutoffs of 10, 7, or 5 cases per year. Female sex (HR 0.81; 95% CI 0.69-0.94) and Charlson Comorbidity Index (CCI) score (HR 1.23; 95% CI 1.02-1.49) predicted improved survival. The addition of the HVC variable to the baseline model improved the fit of the model (likelihood ratio 10.23; p < 0.01) with the C-index going from 0.7126 to 0.7138. The inclusion of sex and CCI score further improved the fit of the model (likelihood ratio 44.41; p < 0.01) and increased the C-index to 0.7187.

conclusionsThe updated Sarculator with HVCs defined as ≥ 13 cases per year was validated using U.S.-only patients. The fit of the model was improved with the addition of sex and CCI score variables, and this should be explored in future studies of the Sarculator model.

Indexed as

Hospitals, High-VolumeNomogramsRetroperitoneal NeoplasmsSarcomaAdultAgedFemaleFollow-Up StudiesHumansMaleMiddle AgedPrognosisRetrospective StudiesSurvival RateUnited StatesCentralizationNomogramRetroperitonealSarcomaVolume

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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.