Imaging Characteristics and Diagnostic Implications of Breast Amyloidoma

Breast amyloidoma is an extremely rare and potentially underdiagnosed entity with underreported diagnostic implications. Breast amyloidomas typically present as painless masses with varying imaging characteristics ranging from nonspecific asymmetries to mammary carcinoma mimics. Calcifications are visualized in many described cases in the literature with a characteristic histopathologic appearance following Congo red staining under polarized light. While breast amyloidomas are benign lesions, there are proposed associations with systemic autoimmune and hematolymphoid disorders in the literature. This suggests the need for standardized management parameters following diagnosis, as the treatment of localized amyloidosis differs greatly from that of its systemic counterpart. To date, no consensus guidelines for follow-up or management of breast amyloidomas exist, owing to its rarity and dearth of cases in the literature. Due to the necessity of employing specific staining to establish the diagnosis, underdiagnosis and occasionally misdiagnosis may contribute to its apparent rarity. In this radiologic-pathologic correlation, a unique case of breast amyloidoma is presented to highlight the imaging characteristics, underlying histopathology, and proposed clinical management, with the goal of improving understanding of this rare entity.

Authors

Institutions

Publication Details

Journal
Case Reports in Radiology
Published
2025-01-01
DOI
https://doi.org/10.1155/crra/8480457
Citations
1
Primary Topic
Amyloidosis: Diagnosis, Treatment, Outcomes
Type
article
Field-Weighted Citation Impact
0.81
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Imaging Characteristics and Diagnostic Implications of Breast Amyloidoma

Tamarya L. Hoyt, Michael F. Chan
1 citations
Case Reports in Radiology
Amyloidosis: Diagnosis, Treatment, Outcomes
0.81
article

Imaging Characteristics and Diagnostic Implications of Breast Amyloidoma

Tamarya L. Hoyt, Michael F. Chan
article en
1 citations

Abstract

Breast amyloidoma is an extremely rare and potentially underdiagnosed entity with underreported diagnostic implications. Breast amyloidomas typically present as painless masses with varying imaging characteristics ranging from nonspecific asymmetries to mammary carcinoma mimics. Calcifications are visualized in many described cases in the literature with a characteristic histopathologic appearance following Congo red staining under polarized light. While breast amyloidomas are benign lesions, there are proposed associations with systemic autoimmune and hematolymphoid disorders in the literature. This suggests the need for standardized management parameters following diagnosis, as the treatment of localized amyloidosis differs greatly from that of its systemic counterpart. To date, no consensus guidelines for follow-up or management of breast amyloidomas exist, owing to its rarity and dearth of cases in the literature. Due to the necessity of employing specific staining to establish the diagnosis, underdiagnosis and occasionally misdiagnosis may contribute to its apparent rarity. In this radiologic-pathologic correlation, a unique case of breast amyloidoma is presented to highlight the imaging characteristics, underlying histopathology, and proposed clinical management, with the goal of improving understanding of this rare entity.

Case Reports in RadiologyVol. 2025(1)
Vanderbilt University Medical Center (US)
Good health and well-being
Openalex Percentile: Top 29%
Amyloidosis: Diagnosis, Treatment, Outcomes
0.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.