Work

Ther-19. Machine Learning Approach to Tumor Diagnosis Using Small Datasets: Proof of Principle Using Pediatric Adamantinomatous Craniopharyngioma

Year: 2019

Type: article

Abstract: Advances in Artificial Intelligence (AI) have rapidly become well known through the mainstream media, with myriad applications. Most commonly, AI has been leveraged for problems that possess vast amounts of sample data, as this enables AI models to better generalize patterns within the data. We hypothesized that novel computational methods could allow AI technologies to be applied to topics with more limited amounts of data, such as pediatric brain tumors. Craniopharyngioma, which has a frequency of approximately 2 in 1,000,000 children per year, requires very different treatment algorithms than other mass lesions that commonly present in the same anatomical region. As such, a prioriconfidence regarding the diagnosis of ACP would offer a distinct advantage to healthcare providers, patients, and caregivers. This makes ACP an idea model to test the application of novel AI methods. Through our collected multi-institutional dataset of CT and MRI comprising approximately 100 ACP patients controlled against 100 patients diagnosed with other suprasellar lesions, we demonstrate that state-of-the-art networks can correctly identify ACP with accuracy levels exceeding 88%, which we demonstrate to be comparable or better than current diagnostic techniques. Using concepts such as data scaling, synthetic data expansion, image augmentation, transfer-learning, and fine-tuning, this preliminary work demonstrates the reliable application of AI models towards topics with restricted volumes of data.

Source: Neuro-Oncology

DOI: 10.1093/neuonc/noz036.225