Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification.
Eur Radiol
; 30(2): 778-788, 2020 Feb.
Article
em En
| MEDLINE
| ID: mdl-31691121
ABSTRACT
OBJECTIVE:
To evaluate the impact of utilizing digital breast tomosynthesis (DBT) or/and full-field digital mammography (FFDM), and different transfer learning strategies on deep convolutional neural network (DCNN)-based mass classification for breast cancer.METHODS:
We retrospectively collected 441 patients with both DBT and FFDM on which regions of interest (ROIs) covering the malignant, benign and normal tissues were extracted for DCNN training and validation. Experiments were conducted for tasks in distinguishing malignant/benign/normal (1) classification capabilities of DBT vs FFDM and the role of transfer learning were validated on 2D-DCNN; (2) different strategies of combining DBT and FFDM and the associated impacts on classification were explored; (3) 2D-DCNN and 3D-DCNN trained from scratch with volumetric DBT were compared.RESULTS:
2D-DCNN with transfer learning outperformed that without for DBT in distinguishing malignant (ΔAUC = 0.059 ± 0.009, p < 0.001), benign (ΔAUC = 0.095 ± 0.010, p < 0.001) and normal tissue (ΔAUC = 0.042 ± 0.004, p < 0.001) (paired samples t test). 2D-DCNN trained on DBT (with transfer learning) achieved higher accuracy than those on FFDM (malignant ΔAUC = 0.014 ± 0.014, p = 0.037; benign ΔAUC = 0.031 ± 0.006, p < 0.001; normal ΔAUC = 0.017 ± 0.004, p < 0.001) (independent samples t test). The 2D-DCNN employing both DBT and FFDM for training achieved better performances in benign (FFDM ΔAUC = 0.010 ± 0.008, p < 0.001; DBT ΔAUC = 0.009 ± 0.005, p < 0.001) and normal (FFDM ΔAUC = 0.005 ± 0.003, p < 0.001; DBT ΔAUC = 0.002 ± 0.002, p < 0.001) (related samples Friedman test). The 3D-DCNN and 2D-DCNN trained from scratch with DBT only produced moderate classification.CONCLUSIONS:
Transfer learning facilitates mass classification for both DBT and FFDM, and DBT outperforms FFDM when equipped with transfer learning. Integrating DBT and FFDM in DCNN training enhances mass classification accuracy for breast cancer. KEY POINTS ⢠Transfer learning facilitates mass classification for both DBT and FFDM, and the DBT-based DCNN outperforms the FFDM-based DCNN when equipped with transfer learning. ⢠Integrating DBT and FFDM in DCNN training enhances breast mass classification accuracy. ⢠3D-DCNN/2D-DCNN trained from scratch with volumetric DBT but without transfer learning only produce moderate mass classification result.Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
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Mamografia
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Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
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Evaluation_studies
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Observational_studies
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Risk_factors_studies
Limite:
Adult
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Aged
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Aged80
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Female
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Humans
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Middle aged
Idioma:
En
Revista:
Eur Radiol
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
China