Evidence map›Paper›PMID 37311925›Full record

Trial reportLa Radiologia medica2023

Uterine mesenchymal tumors: development and preliminary results of a magnetic resonance imaging (MRI) diagnostic algorithm.

Francesca Rosa, Carola Martinetti, Silvia Magnaldi, Stefania Rizzo, Lucia Manganaro, Stefania Migone, Silvia Ardoino, Daria Schettini, Pierangelo Marchiolè, Tommaso Ragusa and 1 more

Open access · greenAbstract readMulticenter StudyRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in La Radiologia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 11% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. 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

11 authors at 5 institutions in 2 countries.

Francesca RosaDiagnostic Imaging Department, San Paolo Hospital-ASL 2, via Genova, 30, Savona, Italy. francescarosa892@gmail.com.ORCID http://orcid.org/0000-0002-7744-4385
Carola MartinettiDiagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, Genoa, Italy.
Silvia MagnaldiDiagnostic Imaging Unit, Esperia Medical Center, Porcia, PN, Italy.
Stefania RizzoService of Radiology, Imaging Institute of Southern Switzerland, Clinica di Radiologia EOC, 6900, Lugano, Switzerland.
Lucia ManganaroDepartment of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, 00185, Rome, Italy.
Stefania MigoneDiagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, Genoa, Italy.
Silvia ArdoinoAnatomic Pathology Unit, San Paolo Hospital-ASL 2, via Genova, 30, Savona, Italy.
Daria SchettiniDiagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, Genoa, Italy.
Pierangelo MarchiolèObstetrics and Gynecology Unit, Villa Scassi Hospital-ASL 3, Corso Scassi 1, Genoa, Italy.
Tommaso RagusaAnatomic Pathology Unit, Villa Scassi Hospital-ASL 3, Corso Scassi 1, Genoa, Italy.
Nicoletta GandolfoDiagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, Genoa, Italy.
Villa Torri Hospital · ITOspedale San Paolo · ITLaboratory for Biomedical Neurosciences · CHOspedale Policlinico San Martino · ITSapienza University of Rome · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe aim of our study is to propose a diagnostic algorithm to guide MRI findings interpretation and malignancy risk stratification of uterine mesenchymal masses with a multiparametric step-by-step approach.

methodsA non-interventional retrospective multicenter study was performed: Preoperative MRI of 54 uterine masses was retrospectively evaluated. Firstly, the performance of MRI with monoparametric and multiparametric approach was assessed. Reference standard for final diagnosis was surgical pathologic result (n = 53 patients) or at least 1-year MR imaging follow-up (n = 1 patient). Subsequently, a diagnostic algorithm was developed for MR interpretation, resulting in a Likert score from 1 to 5 predicting risk of malignancy of the uterine lesion. The accuracy and reproducibility of the MRI scoring system were then tested: 26 preoperative pelvic MRI were double-blind evaluated by a senior (SR) and junior radiologist (JR). Diagnostic performances and the agreement between the two readers with and without the application of the proposed algorithm were compared, using histological results as standard reference.

resultsMultiparametric approach showed the best diagnostic performance in terms of accuracy (94.44%,) and specificity (97.56%). DWI was confirmed as the most sensible parameter with a relative high specificity: low ADC values (mean 0.66) significantly correlated to uterine sarcomas diagnosis (p < 0.01). Proposed algorithm allowed to improve both JR and SR performance (algorithm-aided accuracy 88.46% and 96%, respectively) and determined a significant increase in inter-observer agreement, helping even the less-experienced radiologist in this difficult differential diagnosis.

conclusionsUterine leiomyomas and sarcomas often show an overlap of clinical and imaging features. The application of a diagnostic algorithm can help radiologists to standardize their approach to a complex myometrial mass and to easily identify suspicious MRI features favoring malignancy.

Indexed as

SarcomaSoft Tissue NeoplasmsUterine NeoplasmsAlgorithmsDiagnosis, DifferentialDiffusion Magnetic Resonance ImagingFemaleHumansMagnetic Resonance ImagingReproducibility of ResultsRetrospective StudiesSensitivity and SpecificityDiagnostic algorithmDiffusion-weighted imagingLeiomyomaMagnetic resonance imagingUterine sarcoma

Identifiers

PMID37311925
OpenAlexW4380442441

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.