Evidence map›Paper›PMID 36908599›Full record

ArticleFrontiers in neurology2023

Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease.

Giulia Colelli, Leonardo Barzaghi, Matteo Paoletti, Mauro Monforte, Niels Bergsland, Giulia Manco, Xeni Deligianni, Francesco Santini, Enzo Ricci, Giorgio Tasca and 3 more

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In one paragraph

Article in Frontiers in neurology, 2023. 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

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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

13 authors.

Giulia ColelliDepartment of Mathematics, University of Pavia, Pavia, Italy.
Leonardo BarzaghiDepartment of Mathematics, University of Pavia, Pavia, Italy.
Matteo PaolettiNeuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy.
Mauro MonforteUOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
Niels BergslandDepartment of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo Neuroimaging Analysis Center, University of Buffalo, The State University of New York, Buffalo, NY, United States.
Giulia MancoNeuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy.
Xeni DeligianniDepartment of Radiology, University Hospital Basel, Basel, Switzerland.
Francesco SantiniDepartment of Radiology, University Hospital Basel, Basel, Switzerland.
Enzo RicciUOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
Giorgio TascaUOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
Antonietta MiraData Science Lab, Università della Svizzera italiana, Lugano, Switzerland.
Silvia FiginiDepartment of Political and Social Sciences, University of Pavia, Pavia, Italy.
Anna PichiecchioNeuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Quantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion recovery (STIR) sequence to predict fat fraction (FF) and water T2 (wT2) in skeletal muscle introducing a radiomic workflow with standardized feature extraction combined with machine learning algorithms. Methods: Twenty-five patients with facioscapulohumeral muscular dystrophy (FSHD) were scanned at calf level using conventional STIR sequence and qMRI techniques. We applied and compared three different radiomics workflows ( Results: The combination of WF3 and K-nearest neighbor resulted to be the best predictor model of qMRI parameters with a mean absolute error about ± 5 Conclusion: This pilot study demonstrated the possibility to predict qMRI parameters in a cohort of FSHD subjects starting from conventional STIR sequence.

Indexed as

FSHDmachine learningmuscle MRIradiomicsstir

Identifiers

PMID36908599
PMCPMC9999017

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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.