Pediatric Tumor

Pediatric tumors represent a significant challenge in oncology, with high mortality rates for certain cancers like high-grade gliomas. Current research focuses on improving early diagnosis and treatment planning through advanced imaging analysis, employing deep learning models such as convolutional neural networks (CNNs) and U-Net architectures, often incorporating ensemble methods and longitudinal data analysis for improved accuracy in segmentation and quantification of tumor characteristics from MRI and PET/CT scans. These advancements in automated image analysis are crucial for facilitating multi-institutional clinical trials, enabling more precise diagnosis, and ultimately improving patient outcomes.