Evidence map›Paper›PMID 38383255›Full record

ArticleClinical radiology2024

CT-derived radiomics predict the growth rate of renal tumours in von Hippel-Lindau syndrome.

S Singh, F Dehghani Firouzabadi, A Chaurasia, F Homayounieh, M W Ball, F Huda, E B Turkbey, W M Linehan, A A Malayeri

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical radiology, 2024. 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
0.8field-weighted citation impact, top 30% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025
    Review
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

9 authors at 2 institutions in 1 country.

S SinghRadiology and Imaging Sciences, Warren Grant Magnuson Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA.
F Dehghani FirouzabadiRadiology and Imaging Sciences, Warren Grant Magnuson Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA.
A ChaurasiaUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA.
F HomayouniehRadiology and Imaging Sciences, Warren Grant Magnuson Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA.
M W BallUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA.
F HudaRadiology and Imaging Sciences, Warren Grant Magnuson Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA.
E B TurkbeyRadiology and Imaging Sciences, Warren Grant Magnuson Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA.
W M LinehanUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA.
A A MalayeriRadiology and Imaging Sciences, Warren Grant Magnuson Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA. Electronic address: ashkan.malayeri@nih.gov.
National Institutes of Health Clinical Center · USNational Institutes of Health · US

Funding

Molecular Therapeutics of Kidney Cancer: VHL Gene and Fumarate Hydratase GeneZIABC011038 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI LINEHAN, WILLIAM MARSTON · 2009 to 2025
$29.8M
Clinical Studies of the Molecular Genetic Basis of Kidney CancerZIABC011028 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI LINEHAN, WILLIAM MARSTON · 2009 to 2025
$17.9M
Clinical Nursing and Data Management CoreZIDBC011089 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI LINEHAN, WILLIAM MARSTON · 2009 to 2025
$17.4M
Intramural NIH HHS Z99 CL999999
6 · The paper itself

Abstract

aimTo predict renal tumour growth patterns in von Hippel-Lindau syndrome by utilising radiomic features to assist in developing personalised surveillance plans leading to better patient outcomes. MATERIALS AND

methodsThe study evaluated 78 renal tumours in 55 patients with histopathologically-confirmed clear cell renal cell carcinomas (ccRCCs), which were segmented and radiomics were extracted. Volumetric doubling time (VDT) classified the tumours into fast-growing (VDT <365 days) or slow-growing (VDT ≥365 days). Volumetric and diametric growth analyses were compared between the groups. Multiple logistic regression and random forest classifiers were used to select the best features and models based on their correlation and predictability of VDT.

resultsFifty-five patients (mean age 42.2 ± 12.2 years, 27 men) with a mean time difference of 3.8 ± 2 years between the baseline and preoperative scans were studied. Twenty-five tumours were fast-growing (low VDT, i.e., <365 days), and 53 tumours were slow-growing (high VDT, i.e., ≥365 days). The median volumetric and diametric growth rates were 1.71 cm

conclusionRadiomic features correlated with VDT and were able to predict the growth pattern of renal tumours in patients with VHL syndrome.

Indexed as

Carcinoma, Renal CellKidney Neoplasmsvon Hippel-Lindau DiseaseAdultHumansMaleMiddle AgedRadiomicsTomography, X-Ray Computed

Identifiers

PMID38383255
PMCPMC11075775
OpenAlexW4391651332

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.