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Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification

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.

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Abbreviations

ACC:

Accuracy

AUC:

Area under the ROC curve

CADe:

Computer-aided detection

CADx:

Computer-aided diagnosis

CC:

Craniocaudal

DBT:

Digital breast tomosynthesis

DCNN:

Deep convolutional neural network

DTL:

Double transfer learning

FFDM:

Full-field digital mammography

MIX:

Mixture of DBT&FFDM

ML:

Mediolateral

MLO:

Mediolateral oblique

PACS:

Picture archiving and communication system

PPV:

Positive predictive value

RNN:

Recurrent neural network

ROC:

Receiver operating characteristic

ROI:

Region of interest

SEN:

Sensitivity

SPE:

Specificity

STL:

Single transfer learning

TL:

Transfer learning

VGG:

Visual geometry group

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Acknowledgements

We gratefully acknowledge all the members of Department of Radiology, Nanfang Hospital, for continuous assistance. In particular, we would like to thank Dr. Weiguo Chen for his advice during the project.

Funding

This study has received funding by the National Natural Science Foundation of China (81874216 and 81728016), the National Key Research and Development Program of China (2017YFC0112900).

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Correspondence to Xin Zhen or Linghong Zhou.

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The scientific guarantor of this publication is Professor Linghong Zhou.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

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Li, X., Qin, G., He, Q. et al. Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification. Eur Radiol 30, 778–788 (2020). https://doi.org/10.1007/s00330-019-06457-5

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  • DOI: https://doi.org/10.1007/s00330-019-06457-5

Keywords

  • Breast
  • Mammography
  • Deep learning
  • Neural network (computer)
  • Classification