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

  • Xin Li
  • Genggeng Qin
  • Qiang He
  • Lei Sun
  • Hui Zeng
  • Zilong He
  • Weiguo Chen
  • Xin ZhenEmail author
  • Linghong ZhouEmail author
Breast

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.

Keywords

Breast Mammography Deep learning Neural network (computer) Classification 

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

Notes

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 information

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).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Professor Linghong Zhou.

Conflict of interest

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.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• experimental

• performed at one institution

Supplementary material

330_2019_6457_MOESM1_ESM.docx (325 kb)
ESM 1 (DOCX 325 kb)

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.School of Biomedical EngineeringSouthern Medical UniversityGuangzhouChina
  2. 2.Department of RadiologyNanfang Hospital, Southern Medical UniversityGuangzhouChina

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