Clinical GBM hybrid artificial intelligence for prescription dose recommendation and outcome prediction after gamma knife radiosurgery treatment: a proof-of-concept - Takeaways - MDSpire

Clinical GBM hybrid artificial intelligence for prescription dose recommendation and outcome prediction after gamma knife radiosurgery treatment: a proof-of-concept

  • By

  • Jheremy S. Reyes

  • Alexandros Bouras

  • Ajay Niranjan

  • L Dade Lunsford

  • Constantinos G. Hadjipanayis

  • May 8, 2026

  • 0 min

Share

  • 1

    CGH-AI is a hybrid artificial intelligence framework developed to optimize dose recommendations for recurrent glioblastoma treated with Gamma Knife radiosurgery.

  • 2

    The study utilized a retrospective cohort from 2014 to 2024 at the University of Pittsburgh Medical Center, focusing on local tumor control outcomes.

  • 3

    A Random Survival Forest model achieved a C-index of 0.80, indicating strong predictive performance for local control in recurrent glioblastoma.

  • 4

    The integrated dose recommendation engine identified optimal prescription doses, enhancing local control predictions while considering patient-specific factors.

  • 5

    CGH-AI supports personalized decision-making in GKRS by providing interpretable outcome predictions and improving follow-up planning for recurrent glioblastoma.

Original Source(s)

Clinical GBM hybrid artificial intelligence for prescription dose recommendation and outcome prediction after gamma knife radiosurgery treatment: a proof-of-concept

  • by Jheremy S. Reyes, Alexandros Bouras, Ajay Niranjan, L Dade Lunsford, Constantinos G. Hadjipanayis

  • May 8, 2026

Related Content

Gastric-type endocervical adenocarcinoma with Peutz-Jeghers syndrome: a case report and literature review

Myelodysplastic syndromes complicated by atypical Sweet syndrome: a brief research report

Trametinib as second-line therapy for advanced KRAS G12C-mutant non-small cell lung cancer: a single-center clinical analysis of 20 cases