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Uterine tumors resembling ovarian sex cord tumors (UTROSCT) are tumors of unclear histogenesis. The authors analyzed the ultrastructural features of 13 UTROSCT and correlated the findings with their immunohistochemical profile. Features included cells with frequent organoid, nested or cord-like arrangement (8), lumen formation (2; one of which showed surface microvilli), nuclei with irregular indentations (8), intermediate filaments (13), prominent paranuclear aggregates (5), cell junctions (9), desmosome-like junctions (2), tonofilaments (2), basal lamina (1), and cytoplasmic lipid droplets (7; prominent in 3). No dense bodies, subplasmalemmal densities or pinocytotic vesicles were seen. Ultrastructural epithelial differentiation was present in 2 tumors (positive for keratin or epithelial membrane antigen). Prominent lipid droplets correlated with sex cord markers positivity in 2 tumors. Ultrastructural features of smooth muscle differentiation were lacking and abundant paranuclear filaments did not correlate with myoid markers. UTROSCT are polyphenotypic neoplasms ultrastructurally with focal epithelial and variable sex cord-like differentiation. These findings suggest that UTROSCT may result from divergent differentiation in endometrial stromal tumors or represent a distinct group of uterine tumors with sex cord-like differentiation that are closer in histogenesis to ovarian sex cord stromal tumors.

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This study reported undiagnosed, uninvestigated, and untreated osteoporosis in the majority of fragility fracture patients seen by the Rush FLS in the first 12 months of operation.

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Glioblastoma is a highly infiltrative neoplasm with a high propensity of recurrence. The location of recurrence usually cannot be anticipated and depends on various factors, including the surgical resection margins. Currently, radiation planning utilizes the hyperintense signal from T2‐FLAIR MRI and is delivered to a limited area defined by standardized guidelines. To this end, noninvasive early prediction and delineation of recurrence can aid in tailored targeted therapy, which may potentially delay the relapse, consequently improving overall survival. In this work, we hypothesize that radiomics‐based phenotypic quantifiers may support the detection of recurrence before it is visualized on multimodal MRI. We employ retrospective longitudinal data from 29 subjects with a varying number of time points (three to 13) that includes glioblastoma recurrence. Voxelwise textural and intensity features are computed from multimodal MRI (T1‐contrast enhanced [T1CE], FLAIR, and apparent diffusion coefficient), primarily to gain insights into longitudinal radiomic changes from preoperative MRI to recurrence and subsequently to predict the region of relapse from 143 ± 42 days before recurrence using machine learning. T1CE MRI first‐order and gray‐level co‐occurrence matrix features are crucial in detecting local recurrence, while multimodal gray‐level difference matrix and first‐order features are highly predictive of the distant relapse, with a voxelwise test accuracy of 80.1% for distant recurrence and 71.4% for local recurrence. In summary, our work exemplifies a step forward in predicting glioblastoma recurrence using radiomics‐based phenotypic changes that may potentially serve as MR‐based biomarkers for customized therapeutic intervention.

A 63-year-old woman initially presented with progressive left foot pain for 3 months, not responding to conservative management. MRI of the left foot showed a suspicious lesion in calcaneus. An open biopsy was consistent with metastatic lung adenocarcinoma. Tc-MDP total-body bone scintigraphy was ordered for possible other bony lesions, and only left calcaneus lesion was identified on bone scan. CT scan of the chest revealed a soft tissue mass in the superior aspect of the right lower lobe. Staging FDG PET/CT showed hypermetabolic right lung mass and left calcaneus lesion. She received chemotherapy and local radiation to the left calcaneus metastatic lesion.

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