INNV-15. CLINICAL DATA THAT MATTERS: A DISTILLATION OF NEURO-ONCOLOGY CLINICAL TRIAL INCLUSION CRITERIA USING MACHINE LEARNING. (11th November 2019)

• Record Type: Journal Article
• Authors: Snyder, James; Wells, Michael; Poisson, Laila; Kalkanis, Steven; Noushmehr, Houtan; Robin, Adam
• Is Part Of: Neuro-oncology. Volume 21(2019)Supplement 6
• Page Start: vi133
• Page End: vi133
• Publication Date: 2019-11-11
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noz175.558
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: INTRODUCTION: Neuro-oncologic conditions have dismal outcomes, ineffective treatments, poor access to clinical trials, and variability in care. Clinical trials do not capture a patient's complete journey and are restricted to select populations. 'Real-world-evidence' (RWE) attempts to inform point of care decisions through routine collection of data with a clinical-trial-like rigor. RWE complements existing knowledge through broad patient participation, collection throughout disease course, and creation of large multidimensional datasets "knowledge network of disease" 1, 2 . RWE implementation is hindered by unstructured data, uncertainty of relevant features, and semantic heterogeneity. Clinical attributes were selected from trial inclusion criteria and prioritized for structuring in clinic notes for abstraction. METHOD: We queried Clinicaltrials.gov from 1/1/2018-12/31/2018, refined to North America, recruiting, interventional, and adult. Meningioma, pituitary, glioblastoma, astrocytoma, oligodendroglioma, and ependymoma were chosen based on incidence 3 . Lymphoma and nerve sheath tumors were omitted. "Brain tumor" and "glioma" were added. 'K-nearest-neighbor' tokenization parsed inclusion criteria 4 . Document term matrix (n-gram) converted text to vectors 5 . A generative probabilistic model using 'Latent Dirichlet Allocation' plotted words into 10 clusters 6 . Hierarchal clustering was used to compare histology with terms. RESULTS: 401 trials parsed into 3676 statements and 4008 keywords. 10 clusters of terms were similarly distributed amongst histologies, suggesting generalizability across tumor types. Cluster revealed 8 categories: 1) Time: enrollment; 2) Performance status: KPS; 3) Testing: mutations, upper limit of normal, routine hematologic laboratory assays; 4) Imaging: extent of surgery; 5) Pregnancy/childbearing; 6) Tumor grade; 7) Treatment history: recurrence, chemotherapy, radiation, time; 8) Informed consent CONCLUSIONS: Dissecting the compendium of clinical trials using machine learning can identify general parameters for trial enrollment to guide RWE clinical collection. Using practical definitions of the most germane trial data, specific information can be sought after and defined to improve research quality, maximize research yields and improve patient care whilst minimizing wasted research and clinical endeavors.