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Related Experiment Video

Updated: Apr 15, 2026

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

Identifying Prognostic Factors in Brain Metastasis Patients Using MRI Morphological Features: A Machine Learning and

Daniela Pomohaci^1,2, Emilia-Adriana Marciuc^2,3, Bogdan-Ionuț Dobrovăț^2,3

¹Doctoral School, Grigore T. Popa University of Medicine and Pharmacy Iasi, 16 Universității Str., 700115 Iasi, Romania.

Diagnostics (Basel, Switzerland)

|April 14, 2026

Summary

This summary is machine-generated.

New MRI features can predict brain metastases (BMs) overall survival (OS). Machine learning models incorporating these features, like solid enhancement, significantly improve survival prediction accuracy.

Keywords:

MRI data analysis artificial intelligence brain metastasis machine learning overall survival

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Last Updated: Apr 15, 2026

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Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Area of Science:

Radiology
Oncology
Machine Learning

Background:

Brain metastases (BMs) significantly impact patient prognosis.
Accurate prediction of overall survival (OS) is crucial for treatment planning.
Novel imaging biomarkers are needed to enhance prognostic models.

Purpose of the Study:

To identify novel magnetic resonance imaging (MRI) features of BMs.
To assess the utility of these MRI features in predicting patient overall survival (OS).
To integrate these features into machine learning (ML) algorithms for improved survival prediction.

Main Methods:

Analysis of MRI data from 109 patients with BMs.
Survival analysis using Kaplan-Meier, log-rank test, and Cox Regression.
Development and evaluation of four ML models (including shallow Neural Networks) to predict six-month survival.

Main Results:

Multiple brain lesions and synchronous presentation were associated with poor prognosis (HR > 1).
Absence of extracranial lesions and solid enhancement showed a protective effect on OS (HR < 1).
Shallow Neural Networks model achieved high performance (AUC = 0.93); solid enhancement positively impacted OS, while lesion number/volume/morphology negatively impacted it.

Conclusions:

Morphological MRI features of BMs are valuable predictors of overall survival.
Integrating these features into ML algorithms significantly enhances their predictive and discriminatory capacity.
This approach offers a promising tool for personalized prognostic assessment in brain metastases.