Deep learning-based multimodal fusion of imaging, pathology, and CTCs for early diagnosis of pediatric distal femur osteosarcoma

This study proposes a novel deep learning (DL)-based multimodal diagnostic framework that integrates magnetic resonance imaging (MRI), computed tomography (CT), histopathological slides, and circulating tumor cells (CTCs) data for early and accurate diagnosis of distal femur osteosarcoma (OS) in pediatric patients. Public datasets including The Cancer Imaging Archive (TCIA), The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO) provided imaging and genomic data. Preprocessing involved denoising, normalization, slice alignment, and color standardization using Fiji/ImageJ. Pathological features were extracted via transfer learning using pretrained convolutional neural networks (CNNs) like VGG16 and ResNet50. CTCs were detected and classified using flow cytometry, Hough transform, and support vector machine (SVM) algorithms. A multimodal DL architecture was constructed by fusing image, pathology, and CTC feature vectors, and performance was evaluated through cross-validation. The model achieved an accuracy of 92.5%, sensitivity of 88.7%, specificity of 94.3%, and AUC of 0.96 on an independent test set. Incorporating CTC data notably improved performance in metastasis assessment and diagnosis where imaging was inconclusive. The proposed DL-based multimodal model significantly enhances the early diagnostic capacity for pediatric distal femur OS. Its robustness, diagnostic accuracy, and potential for clinical translation make it a promising tool for personalized treatment strategies.