Mammographic breast density (BD) is commonly visually assessed using the Breast Imaging Reporting and Data System (BI-RADS) four-category scale. To overcome inter- and intraobserver variability of visual assessment, the authors retrospectively developed and externally validated a software for BD classification based on convolutional neural networks from mammograms obtained between 2017 and 2020. The tool was trained using the majority BD category determined by seven board-certified radiologists who independently visually assessed 760 mediolateral oblique (MLO) images in 380 women (mean age, 57 years +/- 6 [SD]) from center 1; this process mimicked training from a consensus of several human readers. External validation of the model was performed by the three radiologists whose BD assessment was closest to the majority (consensus) of the initial seven on a dataset of 384 MLO images in 197 women (mean age, 56 years +/- 13) obtained from center 2. The model achieved an accuracy of 89.3% in distinguishing BI-RADS a or b (nondense breasts) versus c or d (dense breasts) categories, with an agreement of 90.4% (178 of 197 mammograms) and a reliability of 0.807 (Cohen k) compared with the mode of the three readers. This study demonstrates accuracy and reliability of a fully automated software for BD classification.

Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus / V. Magni, M. Interlenghi, A. Cozzi, M. Ali', C. Salvatore, A.A. Azzena, D. Capra, S. Carriero, G. DELLA PEPA, D. Fazzini, G. Granata, C.B. Monti, G. Muscogiuri, G. Pellegrino, S. Schiaffino, I. Castiglioni, S. Papa, F. Sardanelli. - In: RADIOLOGY. ARTIFICIAL INTELLIGENCE. - ISSN 2638-6100. - 4:2(2022), pp. 1-5. [10.1148/ryai.210199]

Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus

V. Magni
Co-primo
;
A. Cozzi;M. Ali';A.A. Azzena;D. Capra;S. Carriero;G. DELLA PEPA;G. Granata;C.B. Monti;G. Muscogiuri;G. Pellegrino;F. Sardanelli
Ultimo
2022

Abstract

Mammographic breast density (BD) is commonly visually assessed using the Breast Imaging Reporting and Data System (BI-RADS) four-category scale. To overcome inter- and intraobserver variability of visual assessment, the authors retrospectively developed and externally validated a software for BD classification based on convolutional neural networks from mammograms obtained between 2017 and 2020. The tool was trained using the majority BD category determined by seven board-certified radiologists who independently visually assessed 760 mediolateral oblique (MLO) images in 380 women (mean age, 57 years +/- 6 [SD]) from center 1; this process mimicked training from a consensus of several human readers. External validation of the model was performed by the three radiologists whose BD assessment was closest to the majority (consensus) of the initial seven on a dataset of 384 MLO images in 197 women (mean age, 56 years +/- 13) obtained from center 2. The model achieved an accuracy of 89.3% in distinguishing BI-RADS a or b (nondense breasts) versus c or d (dense breasts) categories, with an agreement of 90.4% (178 of 197 mammograms) and a reliability of 0.807 (Cohen k) compared with the mode of the three readers. This study demonstrates accuracy and reliability of a fully automated software for BD classification.
Breast; Convolutional Neural Network (CNN); Deep Learning Algorithms; Machine Learning Algorithms; Mammography;
Settore MED/36 - Diagnostica per Immagini e Radioterapia
16-mar-2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/920442
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