ReviewAbdominal radiology (New York)2026
Great peritoneal pretenders: imaging mimickers of peritoneal carcinomatosis.
Review in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
3 authors.
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Abstract
Peritoneal pathology is commonly encountered in clinical practice yet remains diagnostically challenging due to substantial overlap in imaging appearances. Findings such as ascites, peritoneal thickening, and nodularity are often attributed to peritoneal carcinomatosis (PC), a metastatic condition associated with a worse prognosis than metastatic disease without peritoneal involvement. Accurate diagnosis is therefore essential for staging, treatment planning, and prognostication. However, a variety of benign and malignant conditions can produce similar findings. These "peritoneal pretenders" include inflammatory, infectious, traumatic, iatrogenic, and non-carcinomatous malignant processes. This imaging similarity can lead to diagnostic uncertainty, unnecessary investigations, or delays in appropriate management. Understanding peritoneal anatomy, fluid dynamics, and predictable patterns of disease spread is critical for accurate localization and interpretation. This review presents a practical pattern-based multimodality imaging approach to peritoneal pathology, integrating clinical context, disease acuity, imaging morphology and distribution, and targeted advanced imaging to distinguish PC from its benign and malignant mimics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.