Comparison of Visual and Quantra Software Mammographic Density Assessment According to BI-RADS® in 2D and 3D Images
Mammographic density (MD) assessment is subject to inter- and intra-observer variability. An automated method, such as Quantra software, could be a useful tool for an objective and reproducible MD assessment. Our purpose was to evaluate the performance of Quantra software in assessing MD, according to BI-RADS<sup>®</sup> Atlas Fifth Edition recommendations, verifying the degree of agreement with the gold standard, given by the consensus of two breast radiologists. A total of 5009 screening examinations were evaluated by two radiologists and analysed by Quantra software to assess MD. The agreement between the three assigned values was expressed as intraclass correlation coefficients (ICCs). The agreement between the software and the two readers (R1 and R2) was moderate with ICC values of 0.725 and 0.713, respectively. A better agreement was demonstrated between the software's assessment and the average score of the values assigned by the two radiologists, with an index of 0.793, which reflects a good correlation. Quantra software appears a promising tool in supporting radiologists in the MD assessment and could be part of a personalised screening protocol soon. However, some fine-tuning is needed to improve its accuracy, reduce its tendency to overestimate, and ensure it excludes high-density structures from its assessment.
Authors
- Paolo Belli (ORCID: https://orcid.org/0000-0001-7979-2466)
- Oscar Tommasini
- Claudia Rossati
- Rossella Rella (ORCID: https://orcid.org/0000-0002-1337-4381)
- Francesca Fornasa
- Giovanna Romanucci (ORCID: https://orcid.org/0000-0003-2132-0217)
- Stefano Tambalo (ORCID: https://orcid.org/0000-0003-2562-1324)
- Marco Conti (ORCID: https://orcid.org/0000-0001-8260-4753)
- Andrea Quaglia
- Francesca Morciano (ORCID: https://orcid.org/0009-0002-7308-8392)
- Andreea Georgiana Trisca
- Cristina Marcazzan
- Andrea Spagnolo
Institutions
- Università Cattolica del Sacro Cuore (IT)
- University of Verona (IT)
- University of Trento (IT)
- University of Ferrara (IT)
- Agostino Gemelli University Polyclinic (IT)
- ASL Roma (IT)
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
Publication Details
- Journal
- Journal of Imaging
- Published
- 2024-09-23
- DOI
- https://doi.org/10.3390/jimaging10090238
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.35