Vollständiger Abstract
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OBJECTIVE: To evaluate the clinical value of breast MRI in radiation-associated angiosarcoma of the breast (RAASB) for assessing disease extent at diagnosis and after neoadjuvant therapy compared with clinical examination and surgical pathology. METHODS: This IRB-approved retrospective study included 25 patients with breast MRIs between1995 to 2020. MRI estimated disease extent was compared to clinical exam and surgical pathology. MRI findings were evaluated for additional disease and impact on clinical management. Agreement between MRI and clinical assessment of disease was evaluated using Cohen's Kappa statistic. RESULTS: Baseline MRI demonstrated moderate agreement with clinical disease extent (Cohen's κ = 0.44, 95% CI 0.14-0.75). 13/24 (54%) of MRIs reflected clinical exam, 4/24 (17%) MRIs underestimated disease, and 5/24 (21%) MRIs overestimated disease. Baseline MRI changed surgical management in 4/24 (17%). In 8 patients that proceeded directly to surgery, MRI reflected pathology in 6/8 (75%). In 10 patients with post-neoadjuvant MRI, 4/10 (40%) pathology and MRI extent were similar, 5/10 (50%) MRI overestimated disease, and 1/10 (10%) MRI underestimated disease. CONCLUSION: MRI has clinical value in assessing RAASB with the potential for significantly altering surgical management. In this small cohort, post-neoadjuvant MRI demonstrated limited additional clinical value. Larger studies are needed to define the optimal role and timing of post-neoadjuvant MRI. ADVANCES IN KNOWLEDGE: Baseline MRI detected additional disease not identified on initial clinical exam in 5/24 (21%) and changed surgical management in 4/24 (17%). Although baseline MRI demonstrated moderate agreement with clinical examination, post-neoadjuvant MRI frequently overestimated residual disease when compared with final pathology.
Abstract: PubMed · Datensatz
Bibliografischer Nachweis
Publikationsdaten
- Autor:innen
- Daniel Bell
- Quelle
- Radiopaedia.org
- Publikation
- 2024-01-01
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Zitierfähiger Nachweis
Daniel Bell (2024). BJR|Artificial Intelligence. Radiopaedia.org. https://doi.org/10.1093/bjr/tqag191