All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Texture analysis of cardiovascular MRI native T1 mapping in patients with Duchenne muscular dystrophy

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F25%3A00082471" target="_blank" >RIV/00159816:_____/25:00082471 - isvavai.cz</a>

  • Alternative codes found

    RIV/00216224:14110/25:00142559

  • Result on the web

    <a href="https://link.springer.com/article/10.1186/s13023-025-03662-y" target="_blank" >https://link.springer.com/article/10.1186/s13023-025-03662-y</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1186/s13023-025-03662-y" target="_blank" >10.1186/s13023-025-03662-y</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Texture analysis of cardiovascular MRI native T1 mapping in patients with Duchenne muscular dystrophy

  • Original language description

    Background Duchenne muscular dystrophy (DMD) patients are monitored periodically for cardiac involvement, including cardiac MRI with gadolinium-based contrast agents (GBCA). Texture analysis (TA) offers an alternative approach to assess late gadolinium enhancement (LGE) without relying on GBCA administration, impacting DMD patients&apos; care. The study aimed to evaluate the prognostic value of selected TA features in the LGE assessment of DMD patients. Results We developed a pipeline to extract TA features of native T1 parametric mapping and evaluated their prognostic value in assessing LGE in DMD patients. For this evaluation, five independent TA features were selected using Boruta to identify relevant features based on their importance, least absolute shrinkage and selection operator (LASSO) to reduce the number of features, and hierarchical clustering to target multicollinearity and identify independent features. Afterward, logistic regression was used to determine the features with better discrimination ability. The independent feature inverse difference moment normalized (IDMN), which measures the pixel values homogeneity in the myocardium, achieved the highest accuracy in classifying LGE (0.857 (0.572-0.982)) and also was significantly associated with changes in the likelihood of LGE in a subgroup of patients with three yearly examinations (estimate: 23.35 (8.7), p-value = 0.008). Data are presented as mean (SD) or median (IQR) for normally and non-normally distributed continuous variables and numbers (percentages) for categorical ones. Variables were compared with the Welch t-test, Wilcoxon rank-sum, and Chi-square tests. A P-value &lt; 0.05 was considered statistically significant. Conclusion IDMN leverages the information native T1 parametric mapping provides, as it can detect changes in the pixel values of LGE images of DMD patients that may reflect myocardial alterations, serving as a supporting tool to reduce GBCA use in their cardiac MRI examinations.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    30201 - Cardiac and Cardiovascular systems

Result continuities

  • Project

    <a href="/en/project/LX22NPO5104" target="_blank" >LX22NPO5104: National Institute for Research of Metabolic and Cardiovascular Diseases</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2025

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Orphanet Journal of Rare Diseases

  • ISSN

    1750-1172

  • e-ISSN

    1750-1172

  • Volume of the periodical

    20

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    12

  • Pages from-to

    136

  • UT code for WoS article

    001449529900001

  • EID of the result in the Scopus database