Literature DB >> 31710955

Granular cell tumor of the breast: a multidisciplinary challenge.

Francesco Meani1, Simona Di Lascio2, Wiebke Wandschneider3, Giacomo Montagna3, Valerio Vitale4, Sabine Zehbe5, Yves Harder6, Sandra Leoni Parvex7, Paolo Spina7, Claudia Canonica3, Daniele Generali8, Olivia Pagani9.   

Abstract

Granular cell tumors are rare soft tissue tumors; they are almost never malignant, but can mimic a carcinoma clinically, radiologically and microscopically. The finding of a suspicious lump often entails subsequent diagnostic procedures that can pose significant anxiety on patients before reaching a challenging differential diagnosis. The physical and psychological burden is even more significant when such findings occur during the follow up of a previous oncologic condition. Sometimes the fear for a potential local or distant recurrence can be responsible for a misdiagnosis and lead to patient overtreatment.
Copyright © 2019. Published by Elsevier B.V.

Entities:  

Keywords:  Benign tumor; Breast; Breast cancer; Breast mass; Carcinoma; Differential diagnosis; Granular cell tumor; Overtreatment; Scirrhous tumor

Mesh:

Year:  2019        PMID: 31710955     DOI: 10.1016/j.critrevonc.2019.102828

Source DB:  PubMed          Journal:  Crit Rev Oncol Hematol        ISSN: 1040-8428            Impact factor:   6.312


  4 in total

1.  Abrikossoff Tumor Clinically Mimicking Carcinoma in Accessory Axillary Breast Tissue.

Authors:  Lyronne Olivier; Vijay Naraynsingh; Dale Hassranah; Christopher Cassim
Journal:  Cureus       Date:  2022-01-30

2.  Granular Cell Tumor: A Mimicker of Breast Carcinoma.

Authors:  Frederik Bosmans; Sofie Dekeyzer; Filip Vanhoenacker
Journal:  J Belg Soc Radiol       Date:  2021-04-05       Impact factor: 1.894

3.  Contrast-enhanced ultrasound of granular cell tumor in breast: A case report with review of the literature.

Authors:  Huanyu Wang; Duo Feng; Tianhui Zou; Yao Liu; Xiaoqin Wu; Jiawei Zou; Rong Huang
Journal:  Front Oncol       Date:  2022-08-23       Impact factor: 5.738

4.  A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses.

Authors:  Matteo Interlenghi; Christian Salvatore; Veronica Magni; Gabriele Caldara; Elia Schiavon; Andrea Cozzi; Simone Schiaffino; Luca Alessandro Carbonaro; Isabella Castiglioni; Francesco Sardanelli
Journal:  Diagnostics (Basel)       Date:  2022-01-13
  4 in total

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