Deep learning model to assess cancer risk on the basis of a breast MR image alone

American Journal of Roentgenology

Volumn 213, Issue 1, 2019, Pages 227-233

  Portnoi, Tally ^a   Yala, Adam ^a   Schuster, Tal ^a   Barzilay, Regina ^a   Dontchos, Brian ^b,c   Lamb, Leslie ^b,c   Lehman, Constance ^b,c  

^a USA   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c MASSACHUSETTS GENERAL HOSPITAL   (United States)

Author keywords

Artificial intelligence; Breast imaging; Deep learning; Risk assessment

Indexed keywords

ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; BREAST CANCER; CANCER DIAGNOSIS; CANCER GROWTH; CANCER RISK; CANCER SCREENING; CLINICAL ASSESSMENT; CLINICAL FEATURE; COHORT ANALYSIS; CONTROLLED STUDY; DEEP LEARNING; DISEASE DURATION; FALSE POSITIVE RESULT; FEMALE; FOLLOW UP; HUMAN; IMAGE ANALYSIS; IMAGE ENHANCEMENT; LEARNING CURVE; MAJOR CLINICAL STUDY; MIDDLE AGED; NUCLEAR MAGNETIC RESONANCE IMAGING; PATIENT CARE; PRIORITY JOURNAL; RETROSPECTIVE STUDY; RISK ASSESSMENT; VERY ELDERLY;

EID: 85068644430     PISSN: 0361803X     EISSN: 15463141     Source Type: Journal    
DOI: 10.2214/AJR.18.20813     Document Type: Article

References (27)

1. Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 1989; 81:1879–1886
2. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 2004; 23:1111–1130
3. Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K. Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med 2008; 148:337–347
4. Claus EB, Risch N, Thompson WD. The calculation of breast cancer risk for women with a first degree family history of ovarian cancer. Breast Cancer Res Treat 1993; 28:115–120
5. Amir E, Freedman OC, Seruga B, Evans DG. Assessing women at high risk of breast cancer: a review of risk assessment models. J Natl Cancer Inst 2010; 102:680–691
6. Wang C, Brentnall AR, Cuzick J, Harkness EF, Evans DG, Astley S. Exploring the prediction performance for breast cancer risk based on volumetric mammographic density at different thresholds. Breast Cancer Res 2018; 20:49
7. Dontchos BN, Rahbar H, Partridge SC, et al. Are qualitative assessments of background parenchymal enhancement, amount of fibroglandular tissue on MR images, and mammographic density associated with breast cancer risk? Radiology 2015; 276:371–380
8. Tworoger SS, Zhang X, Eliassen AH, et al. Inclusion of endogenous hormone levels in risk prediction models of postmenopausal breast cancer. J Clin Oncol 2014; 32:3111–3117
9. Brentnall AR, Evans DG, Cuzick J. Distribution of breast cancer risk from SNPs and classical risk factors in women of routine screening age in the UK. Br J Cancer 2014; 110:827–828
10. Taylor JM, Ankerst DP, Andridge RR. Validation of biomarker-based risk prediction models. Clin Cancer Res 2008; 14:5977–5983
11. Wolfe JN. A study of breast parenchyma by mammography in the normal woman and those with benign and malignant disease. Radiology 1967; 89:201–205
12. Brentnall AR, Harkness EF, Astley SM, et al. Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort. Breast Cancer Res 2015; 17:147
13. Barlow WE, White E, Ballard-Barbash R, et al. Prospective breast cancer risk prediction model for women undergoing screening mammography. J Natl Cancer Inst 2006; 98:1204–1214
14. King V, Brooks JD, Bernstein JL, Reiner AS, Pike MC, Morris EA. Background parenchymal enhancement at breast MR imaging and breast cancer risk. Radiology 2011; 260:50–60
15. Sprague BL, Conant EF, Onega T, et al; PROSPR Consortium. Variation in mammographic breast density assessments among radiologists in clinical practice: a multicenter observational study. Ann Intern Med 2016; 165:457–464
16. Ciatto S, Houssami N, Apruzzese A, et al. Categorizing breast mammographic density: intra- and interobserver reproducibility of BI-RADS density categories. Breast 2005; 14:269–275
17. Kanas G, Morimoto L, Mowat F, O’Malley C, Fryzek J, Nordyke R. Use of electronic medical records in oncology outcomes research. Clinico-econ Outcomes Res 2010; 2:1–14
18. Zheng A, Casari A. Feature engineering for machine learning: principles and techniques for data scientists. Sebastopol, CA: O’Reilly Media, 2018
19. Sickles EA, D’Orsi CJ, Bassett LW, et al. ACR BI-RADS Mammography, 5th ed. In: D’Orsi CJ, Sickles EA, Mendelson EB, et al. ACR BI-RADS Atlas, Breast Imaging Reporting and Data System. Reston, VA: American College of Radiology, 2013
20. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Tuytelaars T. Li FF, Bajcsy R, eds. Proceedings of the Institute of Electrical and Electronics Engineers Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2016:770–778
21. Huh M, Agrawal P, Efros AA. What makes ImageNet good for transfer learning? arXiv website. arxiv.org/abs/1608.08614. Revised December 10, 2016. Accessed February 20, 2019
22. Russakovsky O, Deng J, Su H, et al. ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis 2015; 115:211–252
23. Hu J, Shen L, Sun G. Squeeze-and-excitation networks. arXiv website. arxiv.org/abs/1709.01507. Revised October 25, 2016. Accessed February 20, 2019
24. Thompson IM, Ankerst DP, Chi C, et al. Assessing prostate cancer risk: results from the Prostate Cancer Prevention Trial. J Natl Cancer Inst 2006; 98:529–534
25. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence. San Francisco, CA: Morgan Kaufmann, 1995; 2:1137–1145)
26. Zhou B, Khosla A, Lapedriza A, Torralba A, Oli-va A. Places: an image database for deep scene understanding. arXiv website. arxiv.org/abs/1610.02055. Revised October 6, 2016. Accessed February 20, 2019
27. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014; 15:1929–1958