Pcln-09. Leveraging Convolutional Neural Networks to Predict Adamantinomatous Craniopharyngioma Diagnosis From Preoperative Parameters
Year: 2018
Type: conference-abstract
Abstract: Adamantinomatous craniopharyngioma (ACP) is a rare epithelial tumor that arises in the suprasellar region. The clinical complexity associated with ACP requires unique surgical goals, making preoperative diagnosis highly desirable. Presently, definitive diagnosis of ACP requires operative tissue sampling. ACP harbors unique features on axial imaging, including cystic and calcified components. Convolutional neural networks (CNN) are computational systems that enable machines to learn operations via the assessment of examples that are task-independent, and are applied to various research fields such as computer vision and machine learning. CNNs are particularly useful with image recognition, as this preserves spatial relationships of data. CNNs have been shown to successfully identify images without a priori information of features. Inductive Transfer is a machine learning concept that is concerned with storing knowledge gained from solving a problem and applying that knowledge to a different problem. Using Inductive Transfer to overcome relatively low class populations, we leveraged the knowledge learned from several CNN models, including the Google® Inception-v4 architecture. CNNs were used to classify ACP out of a preoperative medical image dataset that also included other pathologies present in ACP differential diagnoses, such as pituitary adenoma, optic tract glioma, etc. Even baseline performance rivalled that of human performance by clinical experts. Our results suggest that CNNs can be utilized improve the reliability of pre-operative diagnosis of ACP, thereby improving clinical care.
Source: Neuro-Oncology
Authors: Eric Prince, Ros Whelan, Susan Staulcup, Kenneth Jones, Michael Yang, Lindsey Hoffman, Michael Handler, Nicholas Stence, Todd Hankinson
Institutions: Children's Hospital Colorado, University of Colorado Denver
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2809030147