Evidence map›Paper›PMID 40024391›Full record

ArticleJournal of the American Academy of Dermatology2025

riSCC: A personalized risk model for the development of poor outcomes in cutaneous squamous cell carcinoma.

Anokhi Jambusaria-Pahlajani, Vincent Jeanselme, David M Wang, Nina A Ran, Emily E Granger, Javier Cañueto, David G Brodland, David R Carr, Joi B Carter, John A Carucci and 12 more

Abstract readMulticenter StudyComparative Study
In one paragraph

Article in Journal of the American Academy of Dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

22 authors.

Anokhi Jambusaria-PahlajaniDivision of Dermatology, University of Texas, Austin, Texas.
Vincent JeanselmeMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
David M WangDepartment of Dermatology, Brigham and Women's Hospital, Boston, Massachusetts.
Nina A RanDepartment of Dermatology, Brigham and Women's Hospital, Boston, Massachusetts.
Emily E GrangerDepartment of Dermatology, University of Maryland School of Medicine, Baltimore, Maryland.
Javier CañuetoDepartment of Dermatology, Complejo Asistencial Universitario de Salamanca, Salamanca, Spain.
David G BrodlandZitelli and Brodland P.C., Pittsburgh, Pennsylvania.
David R CarrDepartment of Dermatology, The Ohio State University Medical Center, Columbus, Ohio.
Joi B CarterDepartment of Dermatology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
John A CarucciDepartment of Dermatology, New York University School of Medicine, New York, New York.
Kelsey E HirotsuDepartment of Dermatology, Stanford School of Medicine, Palo Alto, California.
Emily E KarnDepartment of Dermatology, Brigham and Women's Hospital, Boston, Massachusetts.
Shlomo A KoyfmanDepartment of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio.
Aaron R MangoldDepartment of Dermatology, Mayo Clinic Arizona, Scottsdale, Arizona.
Fabio Muradás GirardiDepartment of Head and Neck Surgery, Ana Nery Hospital, Santa Cruz do Sul, RS, Brazil.
Kathryn T ShahwanDepartment of Dermatology, The Ohio State University Medical Center, Columbus, Ohio.
Divya SrivastavaDepartment of Dermatology, University of Texas Southwestern School of Medicine, Dallas, Texas.
Allison T VidimosDepartment of Dermatology, Cleveland Clinic, Cleveland, Ohio.
Tyler J WillenbrinkZitelli and Brodland P.C., Pittsburgh, Pennsylvania.
Ashley WysongDepartment of Dermatology, University of Nebraska Medical Center, Omaha, Nebraska.
William LotterDepartment of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.
Emily S RuizDepartment of Dermatology, Brigham and Women's Hospital, Boston, Massachusetts. Electronic address: esruiz@bwh.harvard.edu.

Funding

Improving prognosis prediction and therapy selection for cutaneous squamous cell carcinomas using artificial intelligenceR21EB035247 · NIBIB · DANA-FARBER CANCER INST · PI LOTTER, WILLIAM EDWARD · 2024 to 2024
$693k
NIBIB NIH HHS R21 EB035247
6 · The paper itself

Abstract

backgroundCutaneous squamous cell carcinoma (CSCC) is a prevalent disease for which improved risk stratification strategies are needed.

objectiveTo develop a novel prognostic model (herein "riSCC") for CSCC and compare riSCC performance to Brigham and Women's Hospital and American Joint Committee on Cancer Staging eighth edition T staging systems.

methodsRetrospective 12-center, multinational cohort study of CSCCs from 1991 to 2023. Clinical and pathologic risk factors, treatments, and outcomes were collected. Fine-Gray model was employed for each outcome with inverse probability of treatment weighting. A final model was trained for prospective use and estimation of hazard ratios.

resultsTwenty-three thousand one hundred sixty-six localized CSCC tumors were included. riSCC prognostic model performed superiorly to American Joint Committee on Cancer eighth edition and Brigham and Women's Hospital T staging for all outcomes. At 5 years, the C-index for riSCC ranged from 0.74 for local recurrence to 0.87 for disease specific death. LIMITATIONS: Retrospective study design.

conclusionriSCC prognostic model offers fine-grained risk estimates and improved stratification for important CSCC outcomes compared to T staging systems.

Indexed as

Carcinoma, Squamous CellNeoplasm Recurrence, LocalSkin NeoplasmsAgedAged, 80 and overFemaleHumansMaleMiddle AgedNeoplasm StagingPrognosisRetrospective StudiesRisk AssessmentRisk FactorsAmerican Joint Committee on Cancer eighth editionBrigham and Women's Hospital T Stagingcutaneous oncologycutaneous squamous cell carcinomametastasisMohs micrographic surgerynonmelanoma skin canceroncologyoutcomesrecurrence

Identifiers

PMID40024391
PMCPMC13401509

What OpenQuestion holds

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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.