Artificial Intelligence for Breast Cancer Detection on Mammography: Factors Related to Cancer Detection.
Acad Radiol
; 31(6): 2239-2247, 2024 Jun.
Article
em En
| MEDLINE
| ID: mdl-38216413
ABSTRACT
RATIONALE AND OBJECTIVES:
Little is known about the factors affecting the Artificial Intelligence (AI) software performance on mammography for breast cancer detection. This study was to identify factors associated with abnormality scores assigned by the AI software. MATERIALS ANDMETHODS:
A retrospective database search was conducted to identify consecutive asymptomatic women who underwent breast cancer surgery between April 2016 and December 2019. A commercially available AI software (Lunit INSIGHT, MMG, Ver. 1.1.4.0) was used for preoperative mammography to assign individual abnormality scores to the lesions and score of 10 or higher was considered as positive detection by AI software. Radiologists without knowledge of the AI results retrospectively assessed the mammographic density and classified mammographic findings into positive and negative finding. General linear model (GLM) analysis was used to identify the clinical, pathological, and mammographic findings related to the abnormality scores, obtaining coefficient ß values that represent the mean difference per unit or comparison with the reference value. Additionally, the reasons for non-detection by the AI software were investigated.RESULTS:
Among the 1001 index cancers (830 invasive cancers and 171 ductal carcinoma in situs) in 1001 patients, 717 (72%) were correctly detected by AI, while the remaining 284 (28%) were not detected. Multivariable GLM analysis showed that abnormal mammography findings (ß = 77.0 for mass, ß = 73.1 for calcification only, ß = 49.4 for architectural distortion, and ß = 47.6 for asymmetry compared to negative; all Ps < 0.001), invasive tumor size (ß = 4.3 per 1 cm, P < 0.001), and human epidermal growth receptor type 2 (HER2) positivity (ß = 9.2 compared to hormone receptor positive, HER2 negative, P = 0.004) were associated with higher mean abnormality score. AI failed to detect small asymmetries in extremely dense breasts, subcentimeter-sized or isodense lesions, and faint amorphous calcifications.CONCLUSION:
Cancers with positive abnormal mammographic findings on retrospective review, large invasive size, HER2 positivity had high AI abnormality scores. Understanding the patterns of AI software performance is crucial for effectively integrating AI into clinical practice.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
/
Inteligência Artificial
/
Mamografia
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Adult
/
Aged
/
Female
/
Humans
/
Middle aged
Idioma:
En
Revista:
Acad Radiol
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de publicação:
Estados Unidos