In:
Pediatric Radiology, Springer Science and Business Media LLC, Vol. 53, No. 3 ( 2022-11-18), p. 438-449
Abstract:
Cross-sectional imaging-based morphological characteristics of pediatric rhabdomyosarcoma have failed to predict outcomes. Objective To evaluate the feasibility and possible value of generating tumor sub-volumes using voxel-wise analysis of metabolic and functional data from positron emission tomography/magnetic resonance imaging (PET/MR) or PET/computed tomography (CT) and MRI in rhabdomyosarcoma. Materials and methods Thirty-four examinations in 17 patients who received PET/MRI or PET/CT plus MRI were analyzed. The volume of interest included total tumor volume before and after therapy. Apparent diffusion coefficients (ADC) and standard uptake values (SUV) were determined voxel-wise. Voxels were assigned to three different groups based on ADC and SUV: “viable tumor tissue,” “intermediate tissue” or “possible necrosis.” In a second approach, data were grouped into three clusters using the Gaussian mixture model. The ratio of these clusters to total tumor volume and changes due to chemotherapy were correlated with clinical and histopathological data. Results After chemotherapy, the proportion of voxels in the different groups changed significantly. A significant reduction of the proportion of voxels assigned to cluster 1 was found, from a mean of 36.4% to 2.5% ( P 〈 0.001). There was a significant increase in the proportion of voxels in cluster 3 following chemotherapy from 24.8% to 81.6% ( P = 0.02). The proportion of voxels in cluster 2 differed depending on the presence or absence of tumor recurrence, falling from 48% to 10% post-chemotherapy in the group with no tumor recurrence ( P 〈 0.05) and from 29% to 23% ( P 〉 0.05) in the group with tumor recurrence. Conclusion Voxel-wise evaluation of multimodal data in rhabdomyosarcoma is feasible. Our initial results suggest that the different distribution of sub-volumes before and after therapy may have prognostic significance.
Type of Medium:
Online Resource
ISSN:
1432-1998
DOI:
10.1007/s00247-022-05540-2
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2022
detail.hit.zdb_id:
1463007-2
