Computer-Aided Approach for BI-RADS Breast Density
Classification: Multicentric Retrospective Study

C 2024

Computer-Aided Approach for BI-RADS Breast Density Classification: Multicentric Retrospective Study

KVAK, Daniel, Marek BIROŠ, Robert HRUBÝ a Eva JANŮ

Základní údaje

Originální název

Computer-Aided Approach for BI-RADS Breast Density Classification: Multicentric Retrospective Study

Autoři

KVAK, Daniel, Marek BIROŠ, Robert HRUBÝ a Eva JANŮ

Vydání

Cham, Germany, Breast Cancer Pathophysiology: An Interdisciplinary Approach, od s. 311-322, 2024

Nakladatel

Springer, Cham

Další údaje

Jazyk

angličtina

Typ výsledku

Kapitola resp. kapitoly v odborné knize

Obor

30204 Oncology

Stát vydavatele

Německo

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Organizační jednotka

Lékařská fakulta

ISBN

978-3-031-65834-1

DOI

http://dx.doi.org/10.1007/16833_2024_284

Klíčová slova anglicky

BI-RADS, Breast density, Computer-aided diagnosis, Deep learning, Full-field digital mammography, Medical image processing

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 7. 9. 2024 08:42, Mgr. Daniel Kvak

Anotace

V originále

Assessing mammographic breast density, a crucial risk determinant for breast cancer, is typically conducted by radiologists through a visual examination of mammography images using the Breast Imaging and Reporting Data System (BI-RADS) breast density classification. However, significant interobserver variability among radiologists leads to inconsistency and potential inaccuracy in breast density assessments and consequent risk predictions. To address this, we analyzed 3835 Full-Field Digital Mammography (FFDM) studies from three mammographic centers. A team of 10 radiologists with experience in breast imaging ranging from 2 to 27 years evaluated these studies, establishing a ground truth for 2127 cases. We utilized 1122 (BI-RADS A: 356, BI-RADS B: 356, BI-RADS C: 356, BI-RADS D: 54) of the studies for training and 122 (BI-RADS A: 39, BI-RADS B: 39, BI-RADS C: 39, BI-RADS D: 5) for testing our Deep-Learning-based Automatic Detection (DLAD) algorithm. The proposed DLAD demonstrated an overall high accuracy (0.853), with balanced accuracy (BA) scores of 0.899 for BI-RADS Category A, 0.838 for Category B, 0.900 for Category C, and 0.900 for Category D. Our findings suggest that the proposed DLAD model can serve as a substantial support in the evaluation process, introducing an additional layer of analysis.

Návaznosti

MUNI/A/1551/2023, interní kód MU

Název: Rozvoj vědeckých aspektů simulačního vzdělávání na LF MU I.

Investor: Masarykova univerzita, Rozvoj vědeckých aspektů simulačního vzdělávání na LF MU I.

Citovat

KVAK, Daniel, Marek BIROŠ, Robert HRUBÝ a Eva JANŮ. Computer-Aided Approach for BI-RADS Breast Density Classification: Multicentric Retrospective Study. In Rezaei, N. Breast Cancer Pathophysiology: An Interdisciplinary Approach. Cham, Germany: Springer, Cham, 2024, s. 311-322. ISBN 978-3-031-65834-1. Dostupné z: https://dx.doi.org/10.1007/16833_2024_284.

@inbook{2429938,
   author = {Kvak, Daniel and Biroš, Marek and Hrubý, Robert and Janů, Eva},
   address = {Cham, Germany},
   booktitle = {Breast Cancer Pathophysiology: An Interdisciplinary Approach},
   doi = {http://dx.doi.org/10.1007/16833_2024_284},
   editor = {Rezaei, N.},
   keywords = {BI-RADS, Breast density, Computer-aided diagnosis, Deep learning, Full-field digital mammography, Medical image processing},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Cham, Germany},
   isbn = {978-3-031-65834-1},
   pages = {311-322},
   publisher = {Springer, Cham},
   title = {Computer-Aided Approach for BI-RADS Breast Density Classification: Multicentric Retrospective Study},
   year = {2024}
}

TY  - CHAP
ID  - 2429938
AU  - Kvak, Daniel - Biroš, Marek - Hrubý, Robert - Janů, Eva
PY  - 2024
TI  - Computer-Aided Approach for BI-RADS Breast Density Classification: Multicentric Retrospective Study
PB  - Springer, Cham
CY  - Cham, Germany
SN  - 9783031658341
KW  - BI-RADS, Breast density, Computer-aided diagnosis, Deep learning, Full-field digital mammography, Medical image processing
N2  - Assessing mammographic breast density, a crucial risk determinant for breast cancer, is typically conducted by radiologists through a visual examination of mammography images using the Breast Imaging and Reporting Data System (BI-RADS) breast density classification. However, significant interobserver variability among radiologists leads to inconsistency and potential inaccuracy in breast density assessments and consequent risk predictions. To address this, we analyzed 3835 Full-Field Digital Mammography (FFDM) studies from three mammographic centers. A team of 10 radiologists with experience in breast imaging ranging from 2 to 27 years evaluated these studies, establishing a ground truth for 2127 cases. We utilized 1122 (BI-RADS A: 356, BI-RADS B: 356, BI-RADS C: 356, BI-RADS D: 54) of the studies for training and 122 (BI-RADS A: 39, BI-RADS B: 39, BI-RADS C: 39, BI-RADS D: 5) for testing our Deep-Learning-based Automatic Detection (DLAD) algorithm. The proposed DLAD demonstrated an overall high accuracy (0.853), with balanced accuracy (BA) scores of 0.899 for BI-RADS Category A, 0.838 for Category B, 0.900 for Category C, and 0.900 for Category D. Our findings suggest that the proposed DLAD model can serve as a substantial support in the evaluation process, introducing an additional layer of analysis.
ER  -

KVAK, Daniel, Marek BIROŠ, Robert HRUBÝ a Eva JANŮ. Computer-Aided Approach for BI-RADS Breast Density Classification: Multicentric Retrospective Study. In Rezaei, N. \textit{Breast Cancer Pathophysiology: An Interdisciplinary Approach}. Cham, Germany: Springer, Cham, 2024, s.~311-322. ISBN~978-3-031-65834-1. Dostupné z: https://dx.doi.org/10.1007/16833\_{}2024\_{}284.

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