Artificial Intelligence for Indication of Invasive Assessment of Calcifications in Mammography Screening. / Künstliche Intelligenz zur Indikationsstellung einer invasiven Mikrokalkabklärung im Mammografie-Screening.
Rofo
; 195(1): 38-46, 2023 01.
Article
em En, De
| MEDLINE
| ID: mdl-36587613
ABSTRACT
PURPOSE:
Lesion-related evaluation of the diagnostic performance of an individual artificial intelligence (AI) system to assess mamographically detected and histologically proven calcifications. MATERIALS ANDMETHODS:
This retrospective study included 634 women of one screening unit (July 2012 - June 2018) who completed the invasive assessment of calcifications. For each leasion, the AI-system calculated a score between 0 and 98. Lesions scored >â0 were classified as AI-positive. The performance of the system was evaluated based on its positive predictive value of invasive assessment (PPV3), the false-negative rate and the true-negative rate.RESULTS:
The PPV3 increased across the categories (readers 4a 21.2â%, 4b 57.7â%, 5 100â%, overall 30.3â%; AI 4a 20.8â%, 4b 57.8â%, 5 100â%, overall 30.7â%). The AI system yielded a false-negative rate of 7.2â% (95â%-CI 4.3â% 11.4â%) and a true-negative rate of 9.1â% (95â%-CI 6.6â%; 11.9â%). These rates were highest in category 4a, 12.5â% and 10.4â% retrospectively. The lowest median AI score was observed for benign lesions (61, interquartile range (IQR) 45-74). Invasive cancers yielded the highest median AI score (81, IQR 64-86). Median AI scores for ductal carcinoma in situ were 74 (IQR 63-84) for low grade, 70 (IQR 52-79) for intermediate grade and 74 (IQR 66-83) for high grade.CONCLUSION:
At the lowest threshold, the AI system yielded calcification-related PPV3 values that increased across categories, similar as seen in human evaluation. The strongest loss in AI-based breast cancer detection was observed for invasively assessed calcifications with the lowest suspicion of malignancy, yet with a comparable decrease in the false-positive rate. An AI-score based stratification of malignant lesions could not be determined. KEY POINTS · The AI-based PPV3 for calcifications is comparable to human assessment.. · AI showed a lower detection performance of screen-positive and screen-negative lesions in category 4a.. · Histological subgroups could not be discriminated by AI scores.. CITATION FORMAT · Weigel S, Brehl AK, Heindel W etâal. Artificial Intelligence for Indication of Invasive Assessment of Calcifications in Mammography Screening. Fortschr Röntgenstr 2023; 195 38â-â46.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
/
Calcinose
/
Carcinoma Intraductal não Infiltrante
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Limite:
Female
/
Humans
Idioma:
De
/
En
Revista:
Rofo
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Alemanha