Language
English
Publication Date
8-6-2025
Journal
Cancers
DOI
10.3390/cancers17152586
PMID
40805281
PMCID
PMC12345888
PubMedCentral® Posted Date
8-6-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Background/Objectives: Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents; the survival rate is as low as 24%. Accurate prediction of clinical outcomes remains a challenge due to tumor heterogeneity and the complexity of pediatric cases. This study aims to improve predictions of progressive disease, therapy response, relapse, and survival in pediatric OS using MRI-based radiomics and machine learning methods. \
Methods: Pre-treatment contrast-enhanced coronal T1-weighted MR scans were collected from 63 pediatric OS patients, with an additional nine external cases used for validation. Three strategies were considered for target region segmentation (whole-tumor, tumor sampling, and bone/soft tissue) and used for MRI-based radiomics. These were then combined with clinical features to predict OS clinical outcomes.
Results: The mean age of OS patients was 11.8 ± 3.5 years. Most tumors were located in the femur (65%). Osteoblastic subtype was the most common histological classification (79%). The majority of OS patients (79%) did not have evidence of metastasis at diagnosis. Progressive disease occurred in 27% of patients, 59% of patients showed adequate therapy response, 25% experienced relapse after therapy, and 30% died from OS. Classification models based on bone/soft tissue segmentation generally performed the best, with certain clinical features improving performance, especially for therapy response and mortality. The top performing classifier in each outcome achieved 0.94-1.0 validation ROC AUC and 0.63-1.0 testing ROC AUC, while those without radiomic features (RFs) generally performed suboptimally.
Conclusions: This study demonstrates the strong predictive capabilities of MRI-based radiomics and multi-region segmentations for predicting clinical outcomes in pediatric OS.
Keywords
MRI, Osteosarcoma, classification, machine learning, pediatric, radiomics
Published Open-Access
yes
Recommended Citation
Ngan, Esther; Mullikin, Dolores; Theruvath, Ashok J; et al., "MRI-Based Radiomics for Outcome Stratification in Pediatric Osteosarcoma" (2025). Faculty, Staff and Students Publications. 6436.
https://digitalcommons.library.tmc.edu/baylor_docs/6436