Purpose or Case Report: Medical Imaging has a crucial role in the diagnosis and management of pediatric cancer patients by providing information about tumor location and quantitative measures of tumor size and metabolic activity at baseline as well as during and after therapy. The standard imaging plan for staging and re-staging of pediatric malignancies includes a high-resolution MRI or CT scan of the local tumor and whole body staging for the detection of metastases on CT, MRI and/or PET scans. Children with lymphomas, sarcomas, germ cell tumors and a few other tumor types are referred to whole body 18F-FDG PET scanning, either coupled with CT or MRI. Artificial intelligence (AI) algorithms can facilitate staging and re-staging of cancers in children by providing 1) rapid detection and delineation of tumoral lesions, 2) automated measurements of tumoral size and metabolic activity, 3) relating tumor measurements to internal standard such as liver and blood pool, 4) assigning a score according to tumor-specific staging systems.

Detecting tumors on a whole-body scan is a challenging task, especially in children whose organs undergo changes in size and composition with increasing age. Moreover, the tumors in children can arise from almost anywhere in the body, from head to toe. With successful applications on object detection, AI methods are promising for automatic tumor detection from whole-body scans as well. In this tutorial, we will introduce a few popular AI methods for such purpose. These methods include U-Net, Vision Transformers (ViT), and the hybrid of the above methods such as O-Net Transformer or TransUNet.
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