Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features.

ABSTRACT:

Purpose

To predict bone marrow metastasis in neuroblastoma using contrast-enhanced computed tomography (CECT) radiomics features and explainable machine learning.

Methods

This cohort study retrospectively included a total of 345 neuroblastoma patients who underwent testing for bone marrow metastatic status. Tumor lesions on CECT images were delineated by two radiologists, and 1409 radiomics features were extracted. Correlation analysis, Least Absolute Shrinkage and Selection Operator regression, and one-way analysis of variance were used to identify radiomics features associated with bone marrow metastasis. A predictive model for bone marrow metastasis was then developed using the support vector machine algorithm based on the selected radiomics features. The performance of t

SUBMITTER: Wang H 

PROVIDER: S-EPMC11500234 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature