Peritoneal loose bodies and the differentiation of fatty abdominal and pelvic lesions
| Autor: | Y. Yurko, Christine Schammel, D.P. Schammel, A. M. Devane, J. Mayfield |
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| Rok vydání: | 2020 |
| Předmět: |
lcsh:Medical physics. Medical radiology. Nuclear medicine
Diagnostic Imaging medicine.medical_specialty lcsh:R895-920 Diagnostic laparoscopy Diagnostic dilemma Malignancy 030218 nuclear medicine & medical imaging Prostatic nodule 03 medical and health sciences 0302 clinical medicine Left lower quadrant medicine Radiology Nuclear Medicine and imaging Pelvis business.industry Sigmoid colon Hylanized fat medicine.disease medicine.anatomical_structure Radiologic differentiation Radiology Signal intensity business 030217 neurology & neurosurgery Peritoneal loose bodies |
| Zdroj: | Radiology Case Reports Radiology Case Reports, Vol 15, Iss 9, Pp 1506-1511 (2020) |
| ISSN: | 1930-0433 |
| DOI: | 10.1016/j.radcr.2020.06.040 |
| Popis: | Peritoneal loose bodies (PLBs) have been sparingly documented within the surgical and radiologic literature, with 38 cases reported to date. A 67-year-old male presented to urology for the management of an asymmetric prostatic nodule. Imaging incidentally identified a well-circumscribed mass of low T2 signal intensity with a small fatty core in the left lower quadrant close to the sigmoid colon; malignancy was in the differential. The mass grew slightly over the next year. A diagnostic laparoscopy retrieved a free floating 4 × 4 cm benign mass from the pelvis, identified as necrotic fat with areas of dystrophic calcifications. PLBs are often a diagnostic dilemma without surgical intervention. Here we present a diagnostic algorithm based on a comprehensive literature review and our case to help better identify unknown abdominal and pelvic fatty masses and to avoid surgery strictly for diagnosis, especially for patients that are not ideal surgical candidates. Using this algorithm, the mass in the patient presented here could have been accurately characterized without invasive diagnostic measures. |
| Databáze: | OpenAIRE |
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