Academic Appointments

  • Clinical Scholar, Radiology

Contact

  • Academic
    acampion@stanford.edu
    University - Staff Department: Rad/Neuroimaging and Neurointervention Position: Clinical Scholar
    University - Affiliate Department: Department Funds

Additional Info

  • Mail Code: 5659

All Publications

  • Brain Tumor Imaging: Review of Conventional and Advanced Techniques. Seminars in neurology Campion, A., Iv, M. 2023

    Abstract

    Approaches to central nervous system (CNS) tumor classification and evaluation have undergone multiple iterations over the past few decades, in large part due to our growing understanding of the influence of genetics on tumor behavior and our refinement of brain tumor imaging techniques. Computed tomography and magnetic resonance imaging (MRI) both play a critical role in the diagnosis and monitoring of brain tumors, although MRI has become especially important due to its superior soft tissue resolution. The purpose of this article will be to briefly review the fundamentals of conventional and advanced techniques used in brain tumor imaging. We will also highlight the applications of these imaging tools in the context of commonly encountered tumors based on the most recently updated 2021 World Health Organization (WHO) classification of CNS tumors framework.

    View details for DOI 10.1055/s-0043-1776765

    View details for PubMedID 37963581

  • MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study. Neuro-oncology advances Tam, L. T., Yeom, K. W., Wright, J. N., Jaju, A., Radmanesh, A., Han, M., Toescu, S., Maleki, M., Chen, E., Campion, A., Lai, H. A., Eghbal, A. A., Oztekin, O., Mankad, K., Hargrave, D., Jacques, T. S., Goetti, R., Lober, R. M., Cheshier, S. H., Napel, S., Said, M., Aquilina, K., Ho, C. Y., Monje, M., Vitanza, N. A., Mattonen, S. A. 2021; 3 (1): vdab042

    Abstract

    Background: Diffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model.Methods: We isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naive DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables.Results: All selected features were intensity and texture-based on the wavelet-filtered images (3 T1 gray-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first-order mean). This multivariable Cox model demonstrated a concordance of 0.68 (95% CI: 0.61-0.74) in the training dataset, significantly outperforming the clinical-only model (C = 0.57 [95% CI: 0.49-0.64]). Adding clinical features to radiomics slightly improved performance (C = 0.70 [95% CI: 0.64-0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C = 0.59 [95% CI: 0.51-0.67], Noether's test P = .02).Conclusions: In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance.

    View details for DOI 10.1093/noajnl/vdab042

    View details for PubMedID 33977272

  • MRI-BASED RADIOMICS PROGNOSTIC MARKERS OF POSTERIOR FOSSA EPENDYMOMA Tam, L., Yecies, D., Han, M., Toescu, S., Wright, J., Mankad, K., Ho, C., Lober, R., Cheshier, S., Vitanza, N., Fisher, P., Hargrave, D., Jacques, T., Aquilina, K., Grant, G., Taylor, M., Mattonen, S., Ramaswamy, V., Yeom, K. OXFORD UNIV PRESS INC. 2020: 357

    View details for Web of Science ID 000606080100349

  • MRI-BASED RADIOMIC PROGNOSTIC MARKERS OF DIFFUSE MIDLINE GLIOMA Tam, L., Han, M., Wright, J., Toescu, S., Campion, A., Shpanskaya, K., Mankad, K., Ho, C., Lober, R., Cheshier, S., Hargrave, D., Jacques, T., Aquilina, K., Monje, M., Grant, G., Mattonen, S., Vitanza, N., Yeom, K. OXFORD UNIV PRESS INC. 2020: 357

    View details for Web of Science ID 000606080100347

  • Automatic Extraction of Skeletal Maturity from Whole Body Pediatric Scoliosis X-rays Using Regional Proposal and Compound Scaling Convolutional Neural Networks Ha, A., Vorhies, J., Campion, A., Fang, C., Fadell, M., Dou, S., Halabi, S., Larson, D., Wang, E., Lee, Y., Langner, J., Kaur, J., Do, B., Park, T., Cho, Y. R., Hu, Yoo, Woo, H. G., Wang, J., Facelli, J., Nam, S., Kang, M. IEEE COMPUTER SOC. 2020: 996-1000

    View details for DOI 10.1109/BIBM49941.2020.9313251

    View details for Web of Science ID 000659487101011