Abstract
Recently, deep learning models have shown the potential to predict breast cancer risk and enable targeted screening strategies, but current models do not consider the change in the breast over time. In this paper, we present a new method, PRIME+, for breast cancer risk prediction that leverages prior mammograms using a transformer decoder, outperforming a state-of-the-art risk prediction method that only uses mammograms from a single time point. We validate our approach on a dataset with 16,113 exams and further demonstrate that it effectively captures patterns of changes from prior mammograms, such as changes in breast density, resulting in improved short-term and long-term breast cancer risk prediction. Experimental results show that our model achieves a statistically significant improvement in performance over the state-of-the-art based model, with a C-index increase from 0.68 to 0.73 (p < 0.05) on held-out test sets.
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References
Bakker, M.F., et al.: Supplemental MRI screening for women with extremely dense breast tissue. N. Engl. J. Med. 381(22), 2091–2102 (2019)
Boyd, N.F.: Mammographic density and risk of breast cancer. Am. Soc. Clin. Oncol. Educ. Book 33(1), e57–e62 (2013)
Brentnall, A.R., Cuzick, J.: Risk models for breast cancer and their validation. Stat. Sci. Rev. J. Inst. Math. Stat. 35(1), 14 (2020)
Brentnall, A.R., 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. 17, 1–10 (2015)
Dadsetan, S., Arefan, D., Berg, W.A., Zuley, M.L., Sumkin, J.H., Wu, S.: Deep learning of longitudinal mammogram examinations for breast cancer risk prediction. Pattern Recogn. 132, 108919 (2022)
DeLong, E.R., DeLong, D.M., Clarke-Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 837–845 (1988)
Duffy, S.W., et al.: Effect of mammographic screening from age 40 years on breast cancer mortality (UK age trial): final results of a randomised, controlled trial. Lancet Oncol. 21(9), 1165–1172 (2020)
Eriksson, M., et al.: A risk model for digital breast tomosynthesis to predict breast cancer and guide clinical care. Sci. Transl. Med. 14(644), eabn3971 (2022)
Gastounioti, A., Desai, S., Ahluwalia, V.S., Conant, E.F., Kontos, D.: Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review. Breast Cancer Res. 24(1), 1–12 (2022)
Hakama, M., Coleman, M.P., Alexe, D.M., Auvinen, A.: Cancer screening: evidence and practice in Europe 2008. Eur. J. Cancer 44(10), 1404–1413 (2008)
Hayward, J.H., et al.: Improving screening mammography outcomes through comparison with multiple prior mammograms. AJR Am. J. Roentgenol. 207(4), 918 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hussein, H., et al.: Supplemental breast cancer screening in women with dense breasts and negative mammography: a systematic review and meta-analysis. Radiology 306(3), e221785 (2023)
National Cancer Institute: Breast cancer risk assessment tool (2011). https://www.cancer.gov/bcrisktool/. Accessed 13 Aug 2017
World Cancer Research Fund International: Breast cancer statistics. https://www.wcrf.org/cancer-trends/breast-cancer-statistics/
Kamarudin, A.N., Cox, T., Kolamunnage-Dona, R.: Time-dependent ROC curve analysis in medical research: current methods and applications. BMC Med. Res. Methodol. 17(1), 1–19 (2017)
Kang, L., Chen, W., Petrick, N.A., Gallas, B.D.: Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach. Stat. Med. 34(4), 685–703 (2015)
Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y.: DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18(1), 1–12 (2018)
Lee, C.I., Chen, L.E., Elmore, J.G.: Risk-based breast cancer screening: implications of breast density. Med. Clin. 101(4), 725–741 (2017)
Liu, Y., Azizpour, H., Strand, F., Smith, K.: Decoupling inherent risk and early cancer signs in image-based breast cancer risk models. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 230–240. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_23
Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)
Ontario, H.Q., et al.: Screening mammography for women aged 40 to 49 years at average risk for breast cancer: an evidence-based analysis. Ont. Health Technol. Assess. Ser. 7(1), 1–32 (2007)
Paci, E.: Summary of the evidence of breast cancer service screening outcomes in Europe and first estimate of the benefit and harm balance sheet. J. Med. Screen. 19(1_suppl), 5–13 (2012)
Park, J., et al.: Screening mammogram classification with prior exams. arXiv preprint arXiv:1907.13057 (2019)
Roelofs, A.A., et al.: Importance of comparison of current and prior mammograms in breast cancer screening. Radiology 242(1), 70–77 (2007)
Sumkin, J.H., et al.: Optimal reference mammography: a comparison of mammograms obtained 1 and 2 years before the present examination. Am. J. Roentgenol. 180(2), 343–346 (2003)
Tyrer, J., Duffy, S.W., Cuzick, J.: A breast cancer prediction model incorporating familial and personal risk factors. Stat. Med. 23(7), 1111–1130 (2004)
Uno, H., Cai, T., Pencina, M.J., D’Agostino, R.B., Wei, L.J.: On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat. Med. 30(10), 1105–1117 (2011)
Varela, C., Karssemeijer, N., Hendriks, J.H., Holland, R.: Use of prior mammograms in the classification of benign and malignant masses. Eur. J. Radiol. 56(2), 248–255 (2005)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Veronesi, U., Boyle, P., Goldhirsch, A., Orecchia, R., Viale, G.: Breast cancer. Lancet 365, 1727–1741 (2005)
Yala, A., et al.: Multi-institutional validation of a mammography-based breast cancer risk model. J. Clin. Oncol. 40(16), 1732–1740 (2022)
Yala, A., et al.: Toward robust mammography-based models for breast cancer risk. Sci. Transl. Med. 13(578), eaba4373 (2021)
Yeoh, H.H., et al.: RADIFUSION: a multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement. arXiv preprint arXiv:2304.00257 (2023)
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Lee, H., Kim, J., Park, E., Kim, M., Kim, T., Kooi, T. (2023). Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14224. Springer, Cham. https://doi.org/10.1007/978-3-031-43904-9_38
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