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Updated: Jun 28, 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

ASLNet: an explainable deep learning framework for glioma grading and survival prediction.

Rafail C Christodoulou¹, Georgios Vamvouras², Platon S Papageorgiou³

¹Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford University, Stanford, CA, United States.

Frontiers in Oncology

|June 3, 2026

Summary

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This summary is machine-generated.

Deep learning models using Arterial Spin Labeling (ASL) MRI can predict diffuse glioma grade and survival. ASLNet identifies key perfusion regions, offering a noninvasive tool for glioma risk stratification.

Area of Science:

Neuroimaging
Oncology
Artificial Intelligence

Background:

Arterial Spin Labeling (ASL) MRI offers noninvasive quantitative perfusion imaging.
Vascular heterogeneity in gliomas correlates with aggressiveness and prognosis.
Deep learning (DL) may enhance ASL's predictive capabilities for tumor grade and survival.

Purpose of the Study:

To develop and validate ASLNet, an interpretable 3D residual network for predicting histopathologic grade and overall survival (OS) in diffuse glioma patients using ASL MRI.
To assess the clinical utility of ASL-based DL for glioma risk stratification.

Main Methods:

Retrospective analysis of 471 diffuse glioma patients with ASL MRI data.
Development of two 3D residual networks: one for WHO grade classification and another for OS prediction (incorporating clinical variables).

Keywords:

arterial spin label (ASL) MRI artificial intelligence deep learning gliomas diagnosis grade prediction survival & prognosis

Related Experiment Videos

Last Updated: Jun 28, 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

Performance evaluation using AUC, macro-F1 score, accuracy, and recall; saliency maps generated via integrated gradients.

Main Results:

The ASLNet grading model achieved an AUC of 0.79 and macro-F1 of 0.74.
The OS prediction model achieved an AUC of 0.70 and macro-F1 of 0.73, with high recall for the long-survival class (0.94).
Saliency analysis identified hyperperfused tumor cores and peritumoral regions as influential for prediction, aligning with glioma biology.

Conclusions:

ASLNet demonstrates the feasibility of interpretable, perfusion-based DL for glioma grade and survival prediction using ASL MRI.
ASL MRI contains clinically relevant prognostic information for gliomas.
ASL-based DL shows potential as a noninvasive tool for glioma risk stratification, pending external validation.