Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma
We present a novel approach for the non-invasive prediction of deregulation in oncogenic signaling pathways in glioblastoma (GBM) by integrating radiogenomic data with machine learning models. This research contributes to advancing precision medicine in GBM management, highlighting the importance of integrating radiomics with genomic data to understand tumor behavior and treatment response better.
Authors
- Abdul Basit Ahanger (ORCID: https://orcid.org/0000-0003-3014-8244)
- Muzafar Rasool Bhat
- Syed Wajid Aalam (ORCID: https://orcid.org/0000-0002-1554-6845)
- Muzafar A. Macha (ORCID: https://orcid.org/0000-0003-4468-4435)
- Archit Shah (ORCID: https://orcid.org/0000-0003-4009-709X)
- Tariq Masoodi (ORCID: https://orcid.org/0000-0002-9186-6349)
- Assif Assad (ORCID: https://orcid.org/0000-0003-0323-6863)
- Ajaz Ahmad Bhat (ORCID: https://orcid.org/0000-0003-3640-6275)
- Meraj Alam Khan
Institutions
- Islamic University of Science and Technology (IN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-01-27
- DOI
- https://doi.org/10.1186/s12967-025-06101-5
- Citations
- 17
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 23.12
Funders
- Indian Council of Medical Research