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.
Similar content being viewed by others
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
References
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424
Munoz D, Near AM, van Ravesteyn NT et al (2014) Effects of screening and systemic adjuvant therapy on ER-specific US breast cancer mortality. J Natl Cancer Inst 106:dju289
Youlden DR, Cramb SM, Dunn NA, Muller JM, Pyke CM, Baade PD (2012) The descriptive epidemiology of female breast cancer: an international comparison of screening, incidence, survival and mortality. Cancer Epidemiol 36:237–248
Berry DA, Cronin KA, Plevritis SK et al (2005) Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med 353:1784–1792
Althuis MD, Dozier JM, Anderson WF, Devesa SS, Brinton LA (2005) Global trends in breast cancer incidence and mortality 1973-1997. Int J Epidemiol 34:405–412
Tagliafico A, Houssami N, Calabrese M (2016) Digital breast tomosynthesis: a practical approach, 1st edn. Springer International Publishing, New York City, New York
Niklason LT, Christian BT, Niklason LE et al (1997) Digital tomosynthesis in breast imaging. Radiology 205:399–406
Lång K, Andersson I, Rosso A, Tingberg A, Timberg P, Zackrisson S (2016) Performance of one-view breast tomosynthesis as a stand-alone breast cancer screening modality: results from the Malmo breast Tomosynthesis screening trial, a population-based study. Eur Radiol 26:184–190
Friedewald SM, Rafferty EA, Rose SL et al (2014) Breast cancer screening using tomosynthesis in combination with digital mammography. JAMA 311:2499–2507
Durand MA, Haas BM, Yao X et al (2015) Early clinical experience with digital breast tomosynthesis for screening mammography. Radiology 274:85–92
McCarthy AM, Kontos D, Synnestvedt M et al (2014) Screening outcomes following implementation of digital breast tomosynthesis in a general-population screening program. J Natl Cancer Inst 106:dju316
Lourenco AP, Barry-Brooks M, Baird GL, Tuttle A, Mainiero MB (2015) Changes in recall type and patient treatment following implementation of screening digital breast tomosynthesis. Radiology 274:337–342
Skaane P, Bandos AI, Gullien R et al (2013) Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program. Radiology 267:47–56
Skaane P, Bandos AI, Gullien R et al (2013) Prospective trial comparing full-field digital mammography (FFDM) versus combined FFDM and tomosynthesis in a population-based screening programme using independent double reading with arbitration. Eur Radiol 23:2061–2071
Ciatto S, Houssami N, Bernardi D et al (2013) Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study. Lancet Oncol 14:583–589
Haas BM, Kalra V, Geisel J, Raghu M, Durand M, Philpotts LE (2013) Comparison of tomosynthesis plus digital mammography and digital mammography alone for breast cancer screening. Radiology 269:694–700
Mall S, Noakes J, Kossoff M et al (2018) Can digital breast tomosynthesis perform better than standard digital mammography work-up in breast cancer assessment clinic? Eur Radiol 28:5182–5194
Dang PA, Freer PE, Humphrey KL, Halpern EF, Rafferty EA (2014) Addition of tomosynthesis to conventional digital mammography: effect on image interpretation time of screening examinations. Radiology 270:49–56
Bernardi D, Ciatto S, Pellegrini M et al (2012) Application of breast tomosynthesis in screening: incremental effect on mammography acquisition and reading time. Br J Radiol 85:e1174–e1178
Palma G, Bloch I, Muller S (2014) Detection of masses and architectural distortions in digital breast tomosynthesis images using fuzzy and a contrario approaches. Pattern Recogn 47:2467–2480
Wei J, Chan HP, Sahiner B et al (2011) Computer-aided detection of breast masses in digital breast tomosynthesis (DBT): improvement of false positive reduction by optimization of object segmentation. In: SPIE medical imaging 2011, Lake Buena Vista, Florida, United States, 796311:1–6
Chan HP, Wei J, Sahiner B et al (2005) Computer-aided detection system for breast masses on digital tomosynthesis mammograms: preliminary experience. Radiology 237:1075–1080
Kim ST, Kim DH, Ro YM (2014) Breast mass detection using slice conspicuity in 3D reconstructed digital breast volumes. Phys Med Biol 59:5003–5023
Kim DH, Kim ST, Ro YM (2015) Improving mass detection using combined feature representations from projection views and reconstructed volume of DBT and boosting based classification with feature selection. Phys Med Biol 60:8809–8832
Kim DH, Kim ST, Baddar WJ, Ro YM (2015) Feature extraction from bilateral dissimilarity in digital breast tomosynthesis reconstructed volume. In: 2015 IEEE international conference on image processing (ICIP), Quebec City, Quebec, Canada, 4521–4524
Chan HP, Wu YT, Sahiner B et al (2010) Characterization of masses in digital breast tomosynthesis: comparison of machine learning in projection views and reconstructed slices. Med Phys 37:3576–3586
Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117
Samala RK, Chan HP, Hadjiiski LM, Helvie MA, Wei J, Cha KH (2016) Mass detection in digital breast tomosynthesis: deep convolutional neural network with transfer learning from mammography. Med Phys 43:6654–6666
Fotin SV, Yin Y, Haldankar H, Hoffmeister JW, Periaswamy S (2016) Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches. In: SPIE medical imaging 2016, San Diego, California, United States, 97850X:1–6
Kim DH, Kim ST, Ro YM (2016) Latent feature representation with 3-D multi-view deep convolutional neural network for bilateral analysis in digital breast tomosynthesis. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP), Shanghai, China, 927–931
Kim DH, Kim ST, Chang JM, Ro YM (2017) Latent feature representation with depth directional long-term recurrent learning for breast masses in digital breast tomosynthesis. Phys Med Biol 62:1009–1031
Samala RK, Chan HP, Hadjiiski LM, Helvie MA, Richter CD, Cha KH (2018) Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis. Phys Med Biol 63:095005
Mendel K, Li H, Sheth D, Giger M (2018) Transfer learning from convolutional neural networks for computer-aided diagnosis: a comparison of digital breast Tomosynthesis and full-field digital mammography. Acad Radiol. https://doi.org/10.1016/j.acra.2018.06.019
Samala RK, Chan H, Hadjiiski L, Helvie MA, Richter CD, Cha KH (2019) Breast cancer diagnosis in digital breast tomosynthesis: effects of training sample size on multi-stage transfer learning using deep neural nets. IEEE Trans Med Imaging 38:686–696
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: arXiv e-prints. Available via https://arxiv.org/abs/1409.1556v6. Accessed 10 Apr 2015
Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2014) Learning spatiotemporal features with 3D convolutional networks. In: arXiv e-prints. Available via https://arxiv.org/abs/1412.0767v4. Accessed 7 Oct 2015
Perez L, Wang J (2017) The effectiveness of data augmentation in image classification using deep learning. In: arXiv e-prints. Available via https://arxiv.org/abs/1712.04621v1. Accessed 13 Dec 2017
Fitzpatrick JM, Sonka M (2000) Handbook of medical imaging: volume 2. Medical image processing and analysis. SPIE, Bellingham, Washington
Seeram E (2010) Digital radiography: an introduction, 1st edn. Delmar Learning, Clifton Park, New York
Gonzalez RC, Woods RE (2017) Digital image processing, 4th edn. Pearson, Hoboken
Wu T, Moore RH, Rafferty EA, Kopans DB (2004) A comparison of reconstruction algorithms for breast tomosynthesis. Med Phys 31:2636–2647
Reiser I, Bian J, Nishikawa RM, Sidky EY, Pan X (2009) Comparison of reconstruction algorithms for digital breast tomosynthesis. In: arXiv e-prints. Available via https://arxiv.org/abs/0908.2610v1. Accessed 01 Aug 2009
D’Orsi CJ, Sickles EA, Mendelson EB, Morris EA (2013) ACR BI-RADS® atlas: breast imaging reporting and data system, 5th edn. American College of Radiology, Reston
Lehman CD, Arao RF, Sprague BL et al (2016) National Performance Benchmarks for modern screening digital mammography: update from the breast Cancer surveillance consortium. Radiology 283:49–58
Sprague BL, Arao RF, Miglioretti DL et al (2017) National Performance Benchmarks for modern diagnostic digital mammography: update from the breast Cancer surveillance consortium. Radiology 283:59–69
Seo BK, Pisano ED, Kuzmiak CM et al (2006) The positive predictive value for diagnosis of breast Cancer: full-field digital mammography versus film-screen mammography in the diagnostic mammographic population. Acad Radiol 13:1229–1235
Liberman L, Abramson AF, Squires FB, Glassman JR, Morris EA, Dershaw DD (1998) The breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categories. AJR Am J Roentgenol 171:35–40
Zou XN (2017) Epidemic trend, screening, and early detection and treatment of cancer in Chinese population. Cancer Biol Med 14:50–59
Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK (2018) Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med 15:e1002683
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).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
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
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(DOCX 325 kb)
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00330-019-06457-5