Purpose: To develop a 4D MRI method for assessing respiration‐induced abdominal organ motion in children receiving radiation therapy. Methods: A 4D MRI using internal image‐based respiratory surrogate has been developed and implemented on a clinical scanner (1.5T Siemens Avanto). Ten patients (younger group: N=6, 2–5 years, anesthetized; older group: N=4, 11–15 years) with neuroblastoma, Wilm's tumor rhabdomyosarcoma, or desmoplastic small round cell tumor received free breathing 4D MRI scans for treatment planning. Coronal image slices of the entire abdomen were retrospectively constructed in 10 respiratory phases. A B‐spline deformable registration (Metz et al. 2011) was performed on 4D datasets to automatically derive motion trajectories of selected anatomical landmarks, including the dome and the center of the liver, and the superior edges of kidneys and spleen. The extents of the motion in three dimensions (anteroposterior, AP; mediolateral, ML; superoinferior, SI) and the correlations between organ motion trajectories were quantified. Results: The 4D MRI scans were successfully performed in <20 minutes for all patients without the use of any external device. Organ motion extents were larger in adolescents (kidneys: 3–13 mm SI, liver and spleen: 6–18 mm SI) than in younger children (kidneys:<3mm in all directions; liver and spleen: 1–8 mm SI, 1–5 mm ML and AP). The magnitude of respiratory motion in some adolescents may warrant special motion management. Motion trajectories were not synchronized across selected anatomical landmarks, particularly in the ML and AP directions, indicating inter‐ and intra‐organ variations of the respiratory‐induced motion. Conclusion: The developed 4D MRI acquisition and motion analysis methods provide a non‐ionizing, non‐invasive approach to automatically measure the organ motion trajectory in the pediatric abdomen. It is useful for defining ITV and PRV, monitoring changes in target motion patterns during the treatment course, and studying interplay effects in proton scanning.

Purpose/Objective(s): Pediatric sarcomas, accounting for approximately 15-20% of pediatric cancers, provide a unique diagnostic challenge. There is considerable morphologic overlap between entities, increasing the importance of molecular studies in the diagnosis, treatment, and identification of therapeutic targets. While these tumors can often occur in similar locations in the bone, the optimal treatment strategies are quite different between tumor histologies, and consist of multimodal therapies including unique chemotherapy regimens, surgery and/or radiation therapy. We developed and validated a genome-wide DNA methylation based classifier to differentiate between osteosarcoma, Ewing's sarcoma, and synovial sarcoma. Materials/Methods: DNA methylation status of 482,421 CpG sites in 10 Ewing's sarcoma, 11 synovial sarcoma, and 15 osteosarcoma samples were determined using the Illumina HumanMethylation450 array. Unsupervised hierarchical clustering was implemented with Euclidean measure for distance matrix and complete agglomeration method for clustering. We developed a classifier from the 400 most differentially methylated CpG sites within the training set of 36 sarcoma samples using the randomForest package in R. This classifier was validated with data drawn from The Cancer Genome Atlas (TCGA) synovial sarcoma data set, TARGET Osteosarcoma data set, and a recently published series of Ewing's sarcoma tumors. Results: Methylation profiling revealed three distinct patterns, each enriched with a single sarcoma subtype. Within the validation cohorts, all samples from TCGA were accurately classified as synovial sarcoma (10/ 10, sensitivity and specificity 100%), all but one sample from TARGET Osteosarcoma were classified as osteosarcoma (85/86, sensitivity 98%, specificity 100%), and 14/15 Ewing's sarcoma samples classified correctly (sensitivity 93%, specificity 100%). The single misclassified osteosarcoma sample was determined to be a misdiagnosed Ewing's sarcoma based on RNA-Seq data demonstrating high EWRS1 and ETV1 expression. The misclassified Ewing's sarcoma sample may represent a true misclassification. An additional clinical sample from a patient who had previous radiation in the area where a new tumor developed was initially misdiagnosed as synovial sarcoma on histopathology and later accurately recognized as osteosarcoma by the methylation classifier and confirmatory additional immunohistochemical staining. Conclusion: Osteosarcoma, synovial sarcoma, and Ewing's sarcoma have distinct epigenetic profiles. Our validated methylation-based classifier can be used to provide a definitive diagnosis when histological and standard techniques are inconclusive.