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Related Experiment Video

Updated: Jun 1, 2025

Author Spotlight: Modeling Brain Tumors In Vivo Using Electroporation-Based Delivery of Plasmid DNA Representing Patient Mutation Signatures

Author Spotlight: Modeling Brain Tumors In Vivo Using Electroporation-Based Delivery of Plasmid DNA Representing Patient Mutation Signatures

Published on: June 23, 2023

From Pixels to Prognosis: Artificial Intelligence and Machine Learning Models in Brain Tumour Mutation Prediction.

Quratulain Tariq¹, Eisha Abid Ali², Saad Bin Anis¹

¹Department of Neurosurgery, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan.

JPMA. the Journal of the Pakistan Medical Association

|January 19, 2025

Summary

Machine learning shows promise for diagnosing brain tumors non-invasively using only imaging data. This approach could improve early detection and treatment planning for brain tumor patients.

Keywords:

brain tumor, artificial intelligence, machine learning, tumor mutation

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Published on: August 16, 2020

Area of Science:

Neuro-oncology
Medical imaging
Computational biology

Background:

Brain tumors are a significant cause of mortality and morbidity.
Current diagnosis relies on MRI and pathological examination of tissue.
Limitations exist in current diagnostic methods for precise tumor characterization.

Purpose of the Study:

To review the role of machine learning in identifying brain tumor mutations.
To explore the potential of using imaging data alone for diagnosis.
To highlight advancements in AI for neuro-oncology.

Main Methods:

Literature review of studies on machine learning and brain tumors.
Analysis of research utilizing imaging data for tumor mutation prediction.
Synthesis of findings on AI-driven diagnostic and prognostic capabilities.

Main Results:

Machine learning models can analyze imaging data to infer tumor characteristics.
Pattern recognition in MRI scans shows potential for non-invasive mutation detection.
AI offers new avenues for outcome prediction in brain tumor patients.

Conclusions:

Machine learning is a powerful tool for advancing brain tumor diagnosis.
Non-invasive methods using AI and imaging data are emerging.
Further research is needed to fully integrate ML into clinical practice for brain tumor management.