Contact

  • Academic
    macsjoh@stanford.edu
    University - Student Department: School of Medicine Position: Graduate, Medicine

All Publications

  • Analyzing Tumor Treating Fields (TTFields) therapy concomitantly with checkpoint inhibitors in a GBM mouse model Lee, S., Cho, K., Wu, C., Liu, J., Ha, J., Sjoholm, A., Lim, M., Li, G., Nitta, R. AMER ASSOC CANCER RESEARCH. 2026

    View details for DOI 10.1158/1538-7445.AM2026-1565

    View details for Web of Science ID 001734495200012

  • Biphasic, time-dependent neutrophil biology in glioblastoma revealed by <i>in vivo</i> survival and flow cytometry with single-cell transcriptomic corroboration Abikenari, M., Choi, J., Liu, J., Sjoholm, A., Nageeb, G., Poe, J., Hwa-Lin Bergsneider, B., Tran, A., Bakalov, D., Medikonda, R., Kim, L., Verma, R., Wu, C., Cho, K., Banu, M., Lim, M. AMER ASSOC CANCER RESEARCH. 2026

    View details for DOI 10.1158/1538-7445.AM2026-170

    View details for Web of Science ID 001734193700022

  • SPP1-driven immunometabolic reprogramming of tumor-associated neutrophils in glioblastoma Abikenari, M., Choi, J., Medikonda, R., Kim, L., Verma, R., Liu, J., Sjoholm, A., Nageeb, G., Bergsneider, B., Wu, C., Cho, K., Tran, A., Bakalov, D., Banu, M., Lim, M. AMER ASSOC CANCER RESEARCH. 2026

    View details for DOI 10.1158/1538-7445.AM2026-3879

    View details for Web of Science ID 001734050100001

  • Next-generation immunotherapy biologics for glioblastoma. Frontiers in immunology Schonfeld, E., Sjoholm, A., Ha, J., Liu, J., Nageeb, G., Annagiri, S., Kim, L., Choi, J., Lim, M. 2026; 17: 1775093

    Abstract

    Glioblastoma (GBM) remains largely resistant to immunotherapy despite the success of immune checkpoint inhibitors in other solid tumors. Phase III trials have not demonstrated survival benefit for anti-PD-1 monotherapy, likely reflecting the GBM tumor microenvironment's profound myeloid-driven immunosuppression, low neoantigen burden, intratumoral heterogeneity, and adaptive resistance. These challenges have driven the development of next-generation immunotherapy biologics designed to reprogram the tumor microenvironment and overcome the innate and adaptive resistance of GBM. This review synthesizes advances in immunotherapy biologics including immune checkpoint combinations, cytokine and immunomodulatory proteins, adoptive cell therapies, vaccines, and oncolytic viruses, highlighting key preclinical insights and emerging clinical trial results. We conclude that improved tumor subtyping and immune profiling will be crucial to guide combination strategies that may achieve durable clinical benefit in GBM.

    View details for DOI 10.3389/fimmu.2026.1775093

    View details for PubMedID 41929511

    View details for PubMedCentralID PMC13039018

  • Immune-Mediated Recurrent Glioblastoma Growth: Insights from a Novel Murine Model Schonfeld, E., Choi, J., Annagiri, S., Kim, L., Ha, J., Liu, J., Sjoholm, A., Lim, J., Lee, S., Lau, C., Cho, K., Verma, R., Lim, M. OXFORD UNIV PRESS INC. 2025: v219

    View details for DOI 10.1093/neuonc/noaf201.0867

    View details for Web of Science ID 001612030100009

  • Publicly Available Datasets for Artificial Intelligence in Neurosurgery: A Systematic Review. Journal of clinical medicine Chan, B., Kim, B., Schonfeld, E., Nageeb, G., Pant, A., Sjoholm, A., Medikonda, R., Hani, U., Veeravagu, A. 2025; 14 (16)

    Abstract

    Introduction: The advancement of artificial intelligence (AI) in neurosurgery is dependent on high quality, large, labeled datasets. Labeled neurosurgical datasets are rare, driven by the high expertise required for labeling neurosurgical data. A comprehensive resource overviewing available datasets for AI in neurosurgery is essential to identify areas for potential model building and areas of needed data construction. Methods: We conducted a systematic review according to PRISMA guidelines to identify publicly available neurosurgical datasets suitable for machine learning. A PubMed search on 8 February 2025, yielded 267 articles, of which 86 met inclusion criteria. Each study was reviewed to extract dataset characteristics, model development details, validation status, availability, and citation impact. Results: Among the 86 included studies, 83.7% focused on spine pathology, with tumor (3.5%), vascular (4.7%), and trauma (7.0%) comprising the remaining. The majority of datasets were image-based, particularly X-ray (37.2%), MRI (29.1%), and CT (20.9%). Label types included segmentation (36.0%), diagnosis (26.7%), and detection/localization (20.9%), with only 2.3% including outcome labels. While 97.7% of studies reported training a model, only 22.6% performed external validation, 20.2% shared code, and just 7.1% provided public applications. Accuracy was the most frequently reported performance metric, even for segmentation tasks, where only 60% of studies used the Dice score metric. Studies often lacked task-appropriate evaluation metrics. Conclusions: We conducted a systematic review to capture all publicly accessible datasets that can be applied to build AI models for neurosurgery. Current datasets are heavily skewed towards spine imaging and lack both clinical patient specific and outcomes information. Provided baseline models from these datasets are limited by poor external validation, lack of reproducibility, and reliance on suboptimal evaluation metrics. Future efforts should prioritize developing multi-institutional datasets with outcome labels, validated models, public access, and domain diversity to accelerate the safe and effective integration of AI into neurosurgical care.

    View details for DOI 10.3390/jcm14165674

    View details for PubMedID 40869500