Evidence map›Paper›PMID 41767911›Full record

ArticleBJUI compass2026

Preoperative predictors of adverse pathology and recurrence-free survival for patients with renal masses.

Akira Kazama, Carlos Munoz-Lopez, Worapat Attawettayanon, Eran Maina, Nityam Rathi, Kieran Lewis, Anne Wong, Angelica Bartholomew, Rebecca A Campbell, Jihad Kaouk and 9 more

Abstract read
In one paragraph

Article in BJUI compass, 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

19 authors.

Akira KazamaGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.ORCID https://orcid.org/0000-0002-8354-6388
Carlos Munoz-LopezGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.
Worapat AttawettayanonGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.
Eran MainaGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.
Nityam RathiGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.ORCID https://orcid.org/0000-0002-6718-8170
Kieran LewisGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.
Anne WongGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.
Angelica BartholomewGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.
Rebecca A CampbellGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.
Jihad KaoukGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.
Samuel HaywoodGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.
Nima AlmassiGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.
Christopher J WeightGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.
Nick HellerGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.ORCID https://orcid.org/0000-0001-8516-8707
Shetal ShahImaging Institute Cleveland Clinic Cleveland Ohio USA.
Erick M RemerGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.
Ryan WardImaging Institute Cleveland Clinic Cleveland Ohio USA.
Amy S NowackiDepartment of Quantitative Health Sciences Cleveland Clinic Cleveland Ohio USA.
Steven C CampbellGlickman Urological and Kidney Institute Cleveland Clinic Cleveland Ohio USA.ORCID https://orcid.org/0000-0002-9110-0343

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Our objective was to develop algorithms to predict adverse pathology (AP) and recurrence-free survival (RFS) for patients with renal tumours primarily based on multifaceted analysis of preoperative CT imaging. Patients/Methods: Seven hundred forty-eight patients with non-metastatic renal tumours managed with definitive surgery at Cleveland Clinic (2011-2014) were retrospectively evaluated (median follow-up 9.1 years). All patients underwent contrast-enhanced CT and parenchymal volume analysis using semi-automated software. A variety of conventional radiological features were evaluated in addition to parenchymal volume replacement (PVR) due to invasive tumour growth, using the contralateral kidney as a control. Adverse pathology (AP) was defined as stage ≥pT3a, grade 3/4 or sarcomatoid/rhabdoid features. Multivariable logistic regression and Cox proportional hazards regression analyses were used to develop predictive models. Results: Overall, 339/748 patients (45%) had AP, which significantly associated with reduced RFS. On univariable analysis, tumour-size, degree of vascularity, heterogeneity, irregular contour, sinus margin irregularity, necrosis, non-cystic tumour and increased PVR significantly associated with AP. On multivariable logistic regression, male sex, R.E.N.A.L. Nearness, heterogeneity, necrosis, sinus margin irregularity and PVR ≥ 25% independently associated with AP. Multivariable analysis indicated that tumour size, heterogeneity, necrosis, PVR ≥ 25% and tumour-related symptoms significantly associated with reduced RFS. Models for AP and RFS at 3, 5 and 10 years showed area under the curve (AUC) values of 0.81 and 0.84-0.86, respectively. Conclusions: These findings confirm that radiological features and PVR are associated with AP and reduced RFS after definitive renal cancer surgery. Our predictive models are entirely based on preoperative parameters and may improve patient counselling and occasionally preclude the need for renal mass biopsy.

Indexed as

adverse pathologynomogramparenchymal volume replacementpartial nephrectomyradical nephrectomyrenal cell carcinoma

Identifiers

PMID41767911
PMCPMC12948496

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.