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Germ cell tumors of childhood are tumors arising from germline cells in gonadal or extragonadal locations. Extragonadal germ cell tumors are characteristically located in the midline, arising intracranially or in the mediastinum, retroperitoneum, or pelvis. These tumors are generally easily diagnosed due to typical sites of origin, characteristic imaging findings, and laboratory markers. However, germ cell tumors can be associated with unusual clinical syndromes or imaging features that can perplex the radiologist. This review will illustrate atypical imaging/clinical manifestations and complications of abdominal germ cell tumors in childhood. These features include unusual primary tumors such as multifocal primaries; local complications such as ovarian torsion or ruptured dermoid; atypical presentations of metastatic disease associated with burned-out primary tumor, growing teratoma syndrome, and gliomatosis peritonei; endocrine manifestations such as precocious puberty and hyperthyroidism; and antibody mediated paraneoplastic syndrome such as anti-N-methyl-D-aspartate-receptor antibody-mediated encephalitis. This review aims to illustrate unusual imaging features associated with the primary tumor, metastatic disease, or distant complications of abdominal germ cell tumors of childhood. [ABSTRACT FROM AUTHOR]

Tumor segmentation in oncological PET is challenging, a major reason being the partial-volume effects (PVEs) that arise due to low system resolution and finite voxel size. The latter results in tissue-fraction effects (TFEs), i.e. voxels contain a mixture of tissue classes. Conventional segmentation methods are typically designed to assign each image voxel as belonging to a certain tissue class. Thus, these methods are inherently limited in modeling TFEs. To address the challenge of accounting for PVEs, and in particular, TFEs, we propose a Bayesian approach to tissue-fraction estimation for oncological PET segmentation. Specifically, this Bayesian approach estimates the posterior mean of the fractional volume that the tumor occupies within each image voxel. The proposed method, implemented using a deep-learning-based technique, was first evaluated using clinically realistic 2D simulation studies with known ground truth, in the context of segmenting the primary tumor in PET images of patients with lung cancer. The evaluation studies demonstrated that the method accurately estimated the tumor-fraction areas and significantly outperformed widely used conventional PET segmentation methods, including a U-net-based method, on the task of segmenting the tumor. In addition, the proposed method was relatively insensitive to PVEs and yielded reliable tumor segmentation for different clinical-scanner configurations. The method was then evaluated using clinical images of patients with stage IIB/III non-small cell lung cancer from ACRIN 6668/RTOG 0235 multi-center clinical trial. Here, the results showed that the proposed method significantly outperformed all other considered methods and yielded accurate tumor segmentation on patient images with Dice similarity coefficient (DSC) of 0.82 (95% CI: 0.78, 0.86). In particular, the method accurately segmented relatively small tumors, yielding a high DSC of 0.77 for the smallest segmented cross-section of 1.30 cm2. Overall, this study demonstrates the efficacy of the proposed method to accurately segment tumors in PET images. [ABSTRACT FROM AUTHOR]