A Random Forest-based Classifier for <i>MYCN</i> Status Prediction in Neuroblastoma using CT Images

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Pereira, T (Author) Other; Silva, F (Author) Other; Claro, P (Author) Other; Carvalho, DC (Author) Other; Dias, SC (Author) Other; Torrao, H (Author) Other; Oliveira, HP (Author) FCUP

Conference proceedings International

Title: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2022, Glasgow, Scotland, United Kingdom, July 11-15, 2022

Pages: 3854-3857

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00X-BGP

DOI: 10.1109/embc48229.2022.9871349

Abstract (EN): Neuroblastoma (NB) is the most common extracranial solid tumor in childhood. Genomic amplification of MYCN is associated with poor outcomes and is detected in 16% of all NB cases. CT scans and MRI are the imaging techniques recommended for diagnosis and disease staging. The assessment of imaging features such as tumor volume, shape, and local extension represent relevant prognostic information. Radiogenomics have shown powerful results in the assessment of the genotype based on imaging findings automatically extracted from medical images. In this work, random forest was used to classify the MYCN amplification using radiomic features extracted from CT slices in a population of 46 NB patients. The learning model showed an area under the curve (AUC) of 0.85 ± 0.13, suggesting that radiomic-based methodologies might be helpful in the extraction of information that is not accessible by human naked eyes but could aid the clinicians on the diagnosis and treatment plan definition. Clinical relevance - This approach represents a random forest-based model to predict the MYCN amplification in NB patients that could give a faster, earlier, and repeatable analysis of the tumor along the time.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4