Diagnostics
Fusion of Deep Learning Radiomics and Clinical Features for Individualized Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma
By Jianhua Mu, Yi Luo, Han Liu and 7 others.
Abstract
Background
Stage IIB conventional osteosarcoma exhibits highly heterogeneous responses to neoadjuvant chemotherapy, necessitating reliable, noninvasive pre-treatment evaluation systems.
Methods
This retrospective study of 198 patients utilized pre-treatment T2-weighted magnetic resonance imaging (MRI) and baseline clinical data. Twelve classical convolutional neural network architectures were benchmarked to identify the optimal imaging backbone. This selected radiomics score was then integrated with core clinical variables using logistic regression to construct a multimodal model. Gradient-weighted Class Activation Mapping++ (Grad-CAM++) and SHapley Additive exPlanations (SHAP) algorithms provided image- and feature-level interpretability.