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Updated: Sep 16, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Toward New Assessment in Sarcoma Identification and Grading Using Artificial Intelligence Techniques.

Arnar Evgení Gunnarsson¹, Simona Correra², Carol Teixidó Sánchez¹

¹Institute of Biomedical and Neural Engineering, Reykjavik University, 102 Reykjavik, Iceland.

Diagnostics (Basel, Switzerland)

|July 12, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) using radiomics and machine learning (ML) shows promise for sarcoma diagnosis. Wavelet transforms significantly improved MRI-based classification accuracy for detecting cancerous tissue and grading tumors.

Keywords:

classification image transform machine learning radiomics sarcoma

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Oncology
Radiology
Artificial Intelligence
Medical Imaging

Background:

Sarcomas are rare, heterogeneous malignant tumors, posing diagnostic and grading challenges.
Current diagnostic methods rely on subjective, time-consuming interpretation of biopsies and imaging.
Inter-observer variability impacts the reliability of traditional sarcoma diagnosis and grading.

Purpose of the Study:

To explore artificial intelligence (AI), specifically radiomics and machine learning (ML), for sarcoma diagnosis and grading using MRI.
To evaluate the utility of quantitative imaging features, including texture analysis, for classifying healthy versus pathological tissue.
To assess the performance of ML models in grading sarcomas according to the French FNCLCC system.

Main Methods:

Extraction of quantitative radiomic features from raw and wavelet-transformed MRI scans.
Inclusion of first-order statistics and texture descriptors (GLCM, GLSZM, GLRLM, NGTDM).
Training of machine learning models for binary classification (healthy vs. pathological) and FNCLCC grade classification.

Main Results:

Binary classification of healthy versus pathological tissue achieved 76.02% accuracy.
FNCLCC grade classification reached 57.6% accuracy.
Wavelet transforms of MRI images significantly enhanced classification performance for both tasks.

Conclusions:

Combining multiple radiomic features improves sarcoma classification accuracy.
Wavelet image transforms are valuable for enhancing AI-based diagnostic performance in sarcoma.
AI-driven radiomics holds potential for developing decision support systems to aid clinicians in sarcoma diagnostics.