Evidence map›Paper›PMID 36057604›Full record

ArticleBMC urology2022

Differentiation of renal masses with multi-parametric MRI: the de Silva St George classification scheme.

Suresh de Silva, Kathleen R Lockhart, Peter Aslan, Peter Nash, Anthony Hutton, David Malouf, Dominic Lee, Paul Cozzi, Fiona MacLean, James Thompson

Open access · goldAbstract read
In one paragraph

Article in BMC urology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.4field-weighted citation impact, top 18% 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

6 citing papers in PubMed, 9 citations in OpenAlex.

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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

10 authors at 3 institutions in 1 country.

Suresh de SilvaFaculty of Medicine, University of NSW, Kensington, NSW, Australia. suresh.desilva@unsw.edu.au.
Kathleen R LockhartDepartment of Urology, St George Hospital, Kogarah, NSW, Australia.
Peter AslanDepartment of Urology, St George Hospital, Kogarah, NSW, Australia.
Peter NashDepartment of Urology, St George Hospital, Kogarah, NSW, Australia.
Anthony HuttonFaculty of Medicine, University of NSW, Kensington, NSW, Australia.
David MaloufDepartment of Urology, St George Hospital, Kogarah, NSW, Australia.
Dominic LeeDepartment of Urology, St George Hospital, Kogarah, NSW, Australia.
Paul CozziDepartment of Urology, Hurstville Private Hospital, Hurstville, NSW, Australia.
Fiona MacLeanDepartment of Anatomical Pathology, Sonic Healthcare, Ryde, NSW, Australia.
James ThompsonFaculty of Medicine, University of NSW, Kensington, NSW, Australia.
St George Hospital · AUUNSW Sydney · AUSt Vincent’s Private Hospital Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop a system for multi-parametric MRI to differentiate benign from malignant solid renal masses and assess its accuracy compared to the gold standard of histopathological diagnosis.

methodsThis is a retrospective analysis of patients who underwent 3 Tesla mpMRI for further assessment of small renal tumours with specific scanning and reporting protocol incorporating T2 HASTE signal intensity, contrast enhancement ratios, apparent diffusion coefficient and presence of microscopic/macroscopic fat. All MRIs were reported prior to comparison with histopathologic diagnosis and a reporting scheme was developed. 2 × 2 contingency table analysis (sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV)), Fisher Exact test were used to assess the association between suspicion of malignancy on mpMRI and histopathology, and descriptive statistics were performed.

results67 patients were included over a 5-year period with a total of 75 renal masses. 70 masses were confirmed on histopathology (five had pathognomonic findings for angiomyolipomas; biopsy was therefore considered unethical, so these were included without histopathology). Three patients were excluded due to a non-diagnostic result, non-standardised imaging and one found to be an organising haematoma rather than a mass. Therefore 72 cases were included in analysis (in 64 patients, with seven patients having multiple tumours). Unless otherwise specified, all further statistics refer to individual tumours rather than patients. 52 (72.2%) were deemed 'suspicious or malignant' and 20 (27.8%) were deemed 'benign' on mpMRI. 51 cases (70.8%) had renal cell carcinoma confirmed. The sensitivity, NPV, specificity and PPV for MRI for detecting malignancy were 96.1%, 90%, 85.7% and 94.2% respectively, Fisher's exact test demonstrated p < 0.0001 for the association between suspicion of malignancy on MRI and histopathology.

conclusionThe de Silva St George classification scheme performed well in differentiating benign from malignant solid renal masses, and may be useful in predicting the likelihood of malignancy to determine the need for biopsy/excision. Further validation is required before this reporting system can  be recommended for clinical use.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsMultiparametric Magnetic Resonance ImagingDiagnosis, DifferentialHumansMagnetic Resonance ImagingRetrospective StudiesSensitivity and SpecificityClassificationDiagnostic accuracyMagnetic resonance imagingRenal cell carcinomaRenal tumours

Identifiers

PMID36057604
PMCPMC9441035
OpenAlexW4294527322

What OpenQuestion holds

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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.