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Worldwide, breast cancer accounts for 16%
Worldwide, breast cancer accounts for 16%
Studies have shown that CESM is superior to FFDM in both sensitivity and specificity in detecting primary breast cancer in dense breasts , and CESM has similar sensitivity and specificity compared to breast magnetic resonance imaging (MRI.
An international study has shown that deep learning models can be successfully used for primary breast cancer screening in FF.
Recently, a study published in the European Radiology journal evaluated the ability of a deep learning model to describe breast malignant lesions on CESM, providing technical support for the rapid and accurate diagnosis
This retrospective single-center study included biopsy-proven invasive breast cancer, each with enhancement on CE.
This retrospective single-center study included biopsy-proven invasive breast cancer, each with enhancement on CE.
In total, 447 pathologically confirmed invasive breast cancers detected on CESM were included , involving 389 patients, 389 including 2460 images for analysi.
Image-by-image diagnostic performance of a deep learning system on DES (a) and LE images (b) followed by simultaneous diagnosis on all images (c) Image -by-image diagnosis of a deep learning system on DES (a) and LE images (b) performance, then diagnose all images simultaneously (c)
The results of this study demonstrate that deep learning analysis of CESM for histological analysis of breast tumors, especially estrogen receptor status and triple-negative receptor status, can quickly provide accurate prognostic and predictive information for the clin.
The results of this study demonstrate that deep learning analysis of CESM for histological analysis of breast tumors, especially estrogen receptor status and triple-negative receptor status, can quickly provide accurate prognostic and predictive information for the clin.
Original source:
Caroline Dominique, Françoise Callonnec, Anca Berghian, et .
Caroline Dominique, Françoise Callonnec, Anca Berghian, et .
Deep learning analysis of contrast-enhanced spectral mammography to determine histoprognostic factors of malignant breast tumour.
DOI : Deep learning analysis of contrast-enhanced spectral mammography to determine histoprognostic factors of malignant breast tumours 11007 /s00330-022-08538- 11007/s00330-022-08538- Leave a message here
