Toward New Assessment in Sarcoma Identification and Grading Using Artificial Intelligence Techniques Article Swipe
Arnar Evgení Gunnarsson
,
Simona Correra
,
C. Sánchez
,
Marco Recenti
,
Halldór Jónsson
,
Paolo Gargiulo
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics15131694
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics15131694
Background/Objectives: Sarcomas are a rare and heterogeneous group of malignant tumors, which makes early detection and grading particularly challenging. Diagnosis traditionally relies on expert visual interpretation of histopathological biopsies and radiological imaging, processes that can be time-consuming, subjective and susceptible to inter-observer variability. Methods: In this study, we aim to explore the potential of artificial intelligence (AI), specifically radiomics and machine learning (ML), to support sarcoma diagnosis and grading based on MRI scans. We extracted quantitative features from both raw and wavelet-transformed images, including first-order statistics and texture descriptors such as the gray-level co-occurrence matrix (GLCM), gray-level size-zone matrix (GLSZM), gray-level run-length matrix (GLRLM), and neighboring gray tone difference matrix (NGTDM). These features were used to train ML models for two tasks: binary classification of healthy vs. pathological tissue and prognostic grading of sarcomas based on the French FNCLCC system. Results: The binary classification achieved an accuracy of 76.02% using a combination of features from both raw and transformed images. FNCLCC grade classification reached an accuracy of 57.6% under the same conditions. Specifically, wavelet transforms of raw images boosted classification accuracy, hinting at the large potential that image transforms can add to these tasks. Conclusions: Our findings highlight the value of combining multiple radiomic features and demonstrate that wavelet transforms significantly enhance classification performance. By outlining the potential of AI-based approaches in sarcoma diagnostics, this work seeks to promote the development of decision support systems that could assist clinicians.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics15131694
- https://www.mdpi.com/2075-4418/15/13/1694/pdf?version=1751507321
- OA Status
- gold
- Cited By
- 1
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412009432
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412009432Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/diagnostics15131694Digital Object Identifier
- Title
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Toward New Assessment in Sarcoma Identification and Grading Using Artificial Intelligence TechniquesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-02Full publication date if available
- Authors
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Arnar Evgení Gunnarsson, Simona Correra, C. Sánchez, Marco Recenti, Halldór Jónsson, Paolo GargiuloList of authors in order
- Landing page
-
https://doi.org/10.3390/diagnostics15131694Publisher landing page
- PDF URL
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https://www.mdpi.com/2075-4418/15/13/1694/pdf?version=1751507321Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2075-4418/15/13/1694/pdf?version=1751507321Direct OA link when available
- Concepts
-
Artificial intelligence, Computer science, Pattern recognition (psychology), Grading (engineering), Gray level, Wavelet, Image (mathematics), Engineering, Civil engineeringTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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