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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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Related Experiment Video

Updated: Jun 25, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Radiomics-Based Model Using Tumor and Peritumoral Features with Semi-Supervised and Privileged Learning for

Dimitrios Filos1, Ekaterini Xinou2, Ioanna Chouvarda1

  • 1Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Greece.

Computer Methods and Programs in Biomedicine
|August 30, 2025
PubMed
Summary

This study developed an AI model to predict lung cancer metastasis using radiomics and advanced machine learning. The model accurately identifies high-risk patients, improving early detection and treatment planning for lung cancer.

Keywords:
GeneralizabilityIntratumoral areaLung cancerMetastasis predictionPeritumoral areaPrivileged learningRadiomicsSemi-supervised

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Area of Science:

  • Radiomics and Machine Learning in Oncology
  • Medical Imaging Analysis
  • Cancer Metastasis Prediction

Background:

  • Lung cancer is a leading global cause of cancer mortality.
  • Early detection of metastasis is crucial for patient management but remains challenging.
  • Existing machine learning models face limitations due to data scarcity and heterogeneity.

Purpose of the Study:

  • To develop a generalizable radiomics-based model for predicting lung cancer metastasis within two years.
  • To integrate supervised, semi-supervised, and privileged learning for enhanced prediction accuracy.
  • To improve early identification of high-risk lung cancer patients.

Main Methods:

  • Extracted radiomic features from CT images, including tumor and surrounding regions.
  • Applied feature harmonization to address data heterogeneity from different CT vendors.
  • Developed a Support Vector Machines (SVM) model incorporating feature selection, semi-supervised, and privileged learning.
  • Evaluated model performance on an external dataset and compared it with human expert predictions.

Main Results:

  • The optimal tumor rim analysis extended 3mm inside and 3mm outside the tumor edge.
  • Radiomic features indicated higher heterogeneity in patients with metastasis.
  • The final SVM model achieved a balanced accuracy of 81.65%, outperforming human experts.
  • Semi-supervised and privileged learning significantly improved model specificity and sensitivity.

Conclusions:

  • Tumor rim analysis combined with clinical data offers insights into metastasis risk.
  • Integrated learning techniques substantially enhance SVM model performance for metastasis prediction.
  • The developed model shows promise in identifying lung cancer patients at risk for metastasis.
  • Further validation is needed to confirm the model's generalizability and clinical utility.