CIGNS-CRG was created to serve as the computational and AI-driven research arm within CIGNS while supporting innovation across the broader Department of Neurosurgery. The goal is to build clinically meaningful tools and scalable pipelines that improve neurosurgical decision-making, streamline workflows, and accelerate translation from research to real-world clinical impact.

Our work spans stereotactic radiosurgery as well as neuro-oncology and neuroimaging, leveraging modern computational methods including machine learning, radiomics, multimodal imaging integration, and generative AI. In doing so, CIGNS-CRG complements the longstanding mission of CIGNS by extending its tradition of clinical innovation into a modern computational framework that is focused on deployability, rigor, and measurable clinical value.

Research Aims

1. Validated predictive models

Develop and validate robust AI models for outcome prediction, treatment personalization, and post-treatment assessment.

2. Multimodal neuroimaging workflows

Advance multimodal neuroimaging workflows that integrate structural imaging with functional and connectomic information.

3. Reproducible infrastructure

Create standardized, reproducible infrastructure that enables internal scaling and multi-institutional validation.

Founders and Members

Founders

Jheremy S. Reyes, MD; Constantinos G. Hadjipanayis, MD, PhD; Ajay Niranjan, MD, MBA.

Current Members

Alexandros Bouras, MD; L. Dade Lunsford, MD; Andrew H. Zureick, MD.

Publications

Reyes JS, Bouras A, Niranjan A, Lunsford LD, Hadjipanayis CG. Predicting time to local failure after gamma knife radiosurgery for melanoma brain metastases using survival machine learning. Clin Transl Oncol. Published online June 7, 2026. Read article.

Reyes JS, Bouras A, Hadjipanayis CG, Niranjan A, Lunsford LD. Local control and dose selection for lung cancer brain metastases treated with radiosurgery: an artificial intelligence model. Clin Exp Metastasis. 2026;43:27. Read article

Reyes JS, Bouras A, Niranjan A, Lunsford LD, Hadjipanayis CG. Clinical GBM hybrid artificial intelligence for prescription dose recommendation and outcome prediction after gamma knife radiosurgery treatment: a proof-of-concept. Frontiers in Oncology. 2026;16:1837357. Read article

Reyes JS, Lohia VN, Almeida T, Niranjan A, Lunsford LD, Hadjipanayis CG. Artificial intelligence in neurosurgery: a systematic review of applications, model comparisons, and ethical implications. Neurosurgical Review. 2025;48(1):455. Read article

Reyes JS, Bouras A, Hadjipanayis CG, Lunsford LD, Niranjan A. Gamma knife radiosurgery for cerebellar brain metastases: clinical outcomes and artificial intelligence-based predictive modeling. Clinical & Experimental Metastasis. 2026;43(1):12. Read article

Reyes JS, Dhol VK, Bouras A, Zenonos G, Lunsford LD, Niranjan A, Hadjipanayis CG. Individualized gamma knife radiosurgery prescription dosing for pituitary adenomas: development and internal validation of a feedforward neural network model. Pituitary. 2026;29(1):42. Read article

Reyes JS, Almeida T, Bouras A, Mettenburg J, Niranjan A, Lunsford LD, Hadjipanayis CG. Radiomics-based artificial intelligence models in brain tumors: a systematic review and meta-analysis of diagnostic performance. Neuroradiology. 2026. Read article