Evidence map›Paper›PMID 27287995›Full record

ArticleEuropean urology2017

Application of the Stage, Size, Grade, and Necrosis (SSIGN) Score for Clear Cell Renal Cell Carcinoma in Contemporary Patients.

William P Parker, John C Cheville, Igor Frank, Harras B Zaid, Christine M Lohse, Stephen A Boorjian, Bradley C Leibovich, R Houston Thompson

Open access · greenAbstract read
In one paragraph

Article in European urology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed, 2 pooled it
8.5field-weighted citation impact, top 2% of its field
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

71 citing papers in PubMed, 2 syntheses or guidelines pooled it, 125 citations in OpenAlex.

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  7. Predictive model for clear cell renal cell carcinoma: a novel model integrating sunitinib resistance and prognosis related genes and clinical factors.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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11 more citing papers are in PubMed but not listed here.

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

8 authors at 2 institutions in 1 country.

William P ParkerDepartment of Urology, Mayo Clinic, Rochester, MN, USA.
John C ChevilleDepartment of Pathology, Mayo Clinic, Rochester, MN, USA.
Igor FrankDepartment of Urology, Mayo Clinic, Rochester, MN, USA.
Harras B ZaidDepartment of Urology, Mayo Clinic, Rochester, MN, USA.
Christine M LohseDepartment of Health Services Research, Mayo Clinic, Rochester, MN, USA.
Stephen A BoorjianDepartment of Urology, Mayo Clinic, Rochester, MN, USA.
Bradley C LeibovichDepartment of Urology, Mayo Clinic, Rochester, MN, USA.
R Houston ThompsonDepartment of Urology, Mayo Clinic, Rochester, MN, USA. Electronic address: thompson.robert@mayo.edu.
Mayo Clinic in Arizona · USMayo Clinic in Florida · US

Funding

Mayo Clinic Center for Translational Science ActivitiesUL1TR000135 · NCATS · MAYO CLINIC ROCHESTER · PI KHOSLA, SUNDEEP · 2012 to 2015
$41.2M
NCATS NIH HHS UL1 TR000135
6 · The paper itself

Abstract

backgroundThe tumor stage, size, grade, and necrosis (SSIGN) score was originally defined using patients treated with radical nephrectomy (RN) between 1970 and 1998 for clear cell renal cell carcinoma (ccRCC), excluding patients treated with partial nephrectomy (PN).

objectiveTo characterize the original SSIGN score cohort with longer follow-up and evaluate a contemporary series of patients treated with RN and PN. DESIGN, SETTING, AND

participantsRetrospective single-institution review of 3600 consecutive surgically treated ccRCC patients grouped into three cohorts: original RN, contemporary (1999-2010) RN, and contemporary PN.

interventionRN or PN. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The association of the SSIGN score with risk of death from RCC was assessed using a Cox proportional hazards regression model, and predictive ability was summarized with a C-index. RESULTS AND LIMITATIONS: The SSIGN scores differed significantly between the original RN, contemporary RN, and contemporary PN cohorts (p<0.001), with SSIGN ≥4 in 53.5%, 62.7%, and 4.7%, respectively (p<0.001). The median durations of follow-up for these groups were 20.1, 9.2, and 7.6 yr, respectively. Each increase in the SSIGN score was predictive of death from RCC (hazard ratios [HRs]: 1.41 for original RN, 1.37 for contemporary RN, and 1.70 for contemporary PN; all p<0.001). The C-indexes for these models were 0.82, 0.84, and 0.82 for original RN, contemporary RN, and contemporary PN, respectively. After accounting for an era-specific improvement in survival among RN patients (HR: 0.53 for contemporary vs original RN; p<0.001), the SSIGN score remained predictive of death from RCC (HR: 1.40; p<0.001).

conclusionsThe SSIGN score remains a useful prediction tool for patients undergoing RN with 20-yr follow-up. When applied to contemporary RN and PN patients, the score retained strong predictive ability. These results should assist in patient counseling and help guide surveillance for ccRCC patients treated with RN or PN. PATIENT SUMMARY: We evaluated the validity of a previously described tool to predict survival following surgery in contemporary patients with kidney cancer. We found that this tool remains valid even when extended to patients significantly different than were initially used to create the tool.

Indexed as

AgedCarcinoma, Renal CellFemaleHumansKidney NeoplasmsMaleMiddle AgedNecrosisNeoplasm GradingNeoplasm StagingNephrectomyProportional Hazards ModelsRetrospective StudiesTumor BurdenPartial nephrectomyPrognosisRadical nephrectomyRenal cell carcinomaSurvival

Identifiers

PMID27287995
PMCPMC5536178
OpenAlexW2415160055

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Registered trials

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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.