Primary and Secondary Breast Lymphoma: Focus on Epidemiology and Imaging Features.
Riccardo PicassoAlberto TagliaficoMassimo CalabreseCarlo MartinoliFederico PistoiaAnna RossiFederico ZaottiniLorenzo DerchiPublished in: Pathology oncology research : POR (2019)
Aim of this study was to select all the cases of Primary (PBL) and Secondary (SBL) Breast Lymphoma from our breast unit since 01/01/2000, to obtain up-to-date data on the prevalence of this rare pathology and to analyze imaging features, with a special focus on CT. All pathological reports of breast biopsies performed from 01/01/2000 to 01/01/2019 were at first screened. Among them, we performed two different researches, looking for key words suggesting either a diagnosis of lymphoma or any other malignant disease. Using the Wiseman criteria, we identify PBL and SBL. All imaging features of PBL and SBL were analyzed. Prevalence of lymphoma amongst suspicious breast masses and amongst all breast malignancies were calculated. Out of 42,505 histopathology reports from mammary nodule biopsies, we found 19,354 malignancies. We were able to identify 11 patients affected by PBL (0,03% of suspicious breast lesions, 0.06% of breast malignancies), and 23 cases of SBL (0,05% of suspicious breast lesions, 0,12% of breast malignancies). Most common isotype in PBL was DLBC lymphoma, whereas in SBL that resulted Follicular lymphoma. In PBL group, we were able to retrieve images 7 CT or CT-PET study performed at diagnosis 7 US, 1 mammography and and 1 MR. In SBL group, we analyzed 14 CT/CT-PET examinations, 11 US studies and 3 mammography. PBL and SBL are rarer than considered until now. There is no definite imaging characteristic able to distinguish between these two pathologies and among them and breast cancer.
Keyphrases
- contrast enhanced
- computed tomography
- image quality
- diffuse large b cell lymphoma
- high resolution
- positron emission tomography
- magnetic resonance imaging
- risk factors
- dual energy
- magnetic resonance
- chronic kidney disease
- end stage renal disease
- emergency department
- pet ct
- machine learning
- electronic health record
- young adults
- ultrasound guided
- fine needle aspiration
- patient reported