Congress:
ECR24
Poster Number:
C-21633
Type:
EPOS Radiologist (scientific)
DOI:
10.26044/ecr2024/C-21633
Authorblock:
C. Salvatore1, M. Interlenghi1, E. Schiavon1, A. Lad1, D. Fazzini1, M. Alì1, S. Papa1, F. Sardanelli2, I. Castiglioni3; 1Milan/IT, 2San Donato Milanese, Milan/IT, 3Milano/IT
Disclosures:
Christian Salvatore:
Shareholder: DeepTrace Technologies S.R.L., Milan, Italy CEO: DeepTrace Technologies S.R.L., Milan, Italy
Matteo Interlenghi:
Shareholder: DeepTrace Technologies S.R.L., Milan, Italy Employee: DeepTrace Technologies S.R.L., Milan, Italy
Elia Schiavon:
Employee: DeepTrace Technologies S.R.L., Milan, Italy
Akash Lad:
Nothing to disclose
Deborah Fazzini:
Nothing to disclose
Marco Alì:
Other: Bracco Imaging
Sergio Papa:
Nothing to disclose
Francesco Sardanelli:
Nothing to disclose
Isabella Castiglioni:
Shareholder: DeepTrace Technologies S.R.L., Milan, Italy
Keywords:
Breast, Mammography, Computer Applications-General, Cancer
A total of 567 patients was processed by the Trace4BDensity™ tool. For each patient, a report was automatically provided to the PACS research node and made available at a patient level.
It must be noted that the classification results were inaccurate in presence of image artifacts associated to a breast incorrect positioning, which occurred in 25 of 567 patients (4.4%).
Overall, the breast density classification proposed by the model was dense in 55% of the patients, and nondense in 45% of the patients, with a concordance frequency with expert human readers of 90% and a substantial reliability (Cohen k of 0.657). Category D (extremely dense) was assigned to 17% of the patients (92 of 542 patients, aged 47±6 years).