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TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer

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Information Processing in Medical Imaging (IPMI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12729))

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Abstract

Characterization of breast parenchyma on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Current quantitative approaches, including radiomics and deep learning models, do not explicitly capture the complex and subtle parenchymal structures, such as fibroglandular tissue. In this paper, we propose a novel method to direct a neural network’s attention to a dedicated set of voxels surrounding biologically relevant tissue structures. By extracting multi-dimensional topological structures with high saliency, we build a topology-derived biomarker, TopoTxR. We demonstrate the efficacy of TopoTxR in predicting response to neoadjuvant chemotherapy in breast cancer. Our qualitative and quantitative results suggest differential topological behavior of breast tissue on treatment-naïve imaging, in patients who respond favorably to therapy versus those who do not.

This work was partially supported by grants NSF IIS-1909038, CCF-1855760, and NCI 1R01CA253368-01. This work used the Extreme Science and Engineering Discovery Environment (XSEDE) [27] Bridges-2 at the Pittsburgh Supercomputing Center through allocation TG-CIS210012, which is supported by NSF ACI-1548562.

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References

  1. Abdelhafiz, D., Yang, C., Ammar, R., Nabavi, S.: Deep CNN for mammography: advances, challenges and applications. BMC Bioinform. 20, 281(2019). https://doi.org/10.1186/s12859-019-2823-4

  2. Adams, H., et al.: Persistence images: a stable vector representation of persistent homology. J. Mach. 18, 1–35 (2017)

    Google Scholar 

  3. Braman, N., et al.: Association of peritumoral radiomics with tumor biology and pathologic response to preoperative targeted therapy for HER2 (ERBB2)–positive breast cancer. JAMA Netw. Open 2(4), e192561–e192561 (2019)

    Google Scholar 

  4. Braman, N.M., et al.: Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res. 19, 1–14 (2017)

    Google Scholar 

  5. Cain, E., Saha, A., Harowicz, M., Marks, J., Marcom, P., Mazurowski, M.: Multivariate ML models for prediction of pCR to NAC in BCa using MRI features: a study using an independent validation set. BCa Res. Treat. 173, 455–463 (2019)

    Google Scholar 

  6. Carriere, M., Cuturi, M., Oudot, S.: Sliced Wasserstein Kernel for persistence diagrams. In: ICML. JMLR. org (2017)

    Google Scholar 

  7. Clough, J., Byrne, N., Oksuz, I., Zimmer, V.A., Schnabel, J.A., King, A.: A topological loss function for deep-learning based image segmentation using persistent homology. In: TPAMI (2020)

    Google Scholar 

  8. Dey, T.K., Wang, J., Wang, Y.: Road network reconstruction from satellite images with machine learning supported by topological methods. In: Proceedings of the 27th ACM SIGSPATIAL (2019)

    Google Scholar 

  9. Duanmu, H.: Prediction of pCR to NAC in BCa using deep learning with integrative imaging, molecular and demographic data. In: MICCAI (2020)

    Google Scholar 

  10. Edelsbrunner, H., Harer, J.: Computational topology: an introduction. Am. Math. Soc. (2010)

    Google Scholar 

  11. Grimm, L.: Breast MRI radiogenomics: current status and research implications. J. Magn. Reson. Imaging 43, 1269–1278 (2015)

    Google Scholar 

  12. Ha, R.: Prior to initiation of chemotherapy, can we predict breast tumor response? Deep learning convolutional neural networks approach using a breast MRI tumor dataset. J. Digital Imaging (2018). https://doi.org/10.1007/s10278-018-0144-1

  13. Hofer, C., Kwitt, R., Niethammer, M., Uhl, A.: Deep learning with topological signatures. In: Advances in Neural Information Processing Systems (2017)

    Google Scholar 

  14. Hu, X., Li, F., Samaras, D., Chen, C.: Topology-preserving deep image segmentation. In: NeurIPS (2019)

    Google Scholar 

  15. Hu, X., Wang, Y., Fuxin, L., Samaras, D., Chen, C.: Topology-aware segmentation using discrete Morse theory. In: ICLR (2021)

    Google Scholar 

  16. King, V., Brooks, J.D., Bernstein, J.L., Reiner, A.S., Pike, M.C., Morris, E.A.: Background paren. enhancement at breast MRI and BCa risk. Radiology 260, 50–60 (2011)

    Google Scholar 

  17. Kusano, G., Hiraoka, Y., Fukumizu, K.: Persistence weighted Gaussian Kernel for topological data analysis. In: ICML (2016)

    Google Scholar 

  18. Lee, H., Kang, H., Chung, M.K., Kim, B.-N., Lee, D.S.: Persistent brain network homology from the perspective of dendrogram. IEEE Trans. Med. Imaging 31, 2267–2277 (2012)

    Google Scholar 

  19. Liu, M.Z., Mutasa, S., Chang, P., Siddique, M., Jambawalikar, S., Ha, R.: A novel CNN algorithm for pathological complete response prediction using an i-SPY TRIAL breast MRI database. Magn. Reson. Imaging 73, 148–151 (2020)

    Google Scholar 

  20. Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik 29(2), 102–127 (2019)

    Article  Google Scholar 

  21. Mani, S.: Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy. JAMIA 20, 688–695 (2013)

    Google Scholar 

  22. Massey Jr, F.J.: The Kolmogorov-Smirnov test for goodness of fit. J. Am. Stat. Assoc. 46, 68–78 (1951)

    Google Scholar 

  23. Milosavljević, N., Morozov, D., Skraba, P.: Zigzag persistent homology in matrix multiplication time. In: SoCG, pp. 216–225 (2011)

    Google Scholar 

  24. Newitt, D., Hylton, N.: Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials. Cancer Imaging Arch. (2016)

    Google Scholar 

  25. Qu, Y., Zhu, H., Cao, K., Li, X., Ye, M., Sun, Y.: Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (dl) method. Thoracic Cancer (2020)

    Google Scholar 

  26. Reininghaus, J., Huber, S., Bauer, U., Kwitt, R.: A stable multi-scale Kernel for topological machine learning. In: CVPR (2015)

    Google Scholar 

  27. Towns, J., et al.: XSEDE: accelerating scientific discovery. Comput. Sci. Eng. 16(5), 62–74 (2014)

    Article  Google Scholar 

  28. Van Griethuysen, J.J., et al.: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77, e104–e107 (2017)

    Google Scholar 

  29. Wang, F., Liu, H., Samaras, D., Chen, C.: TopoGAN: a topology-aware generative adversarial network. In: ECCV, vol. 2 (2020)

    Google Scholar 

  30. Wu, P., et al.: Optimal topological cycles and their application in cardiac trabeculae restoration. In: IPMI (2017)

    Google Scholar 

  31. Zhang, X., Wu, P., Yuan, C., Wang, Y., Metaxas, D.N., Chen, C.: Heuristic search for homology localization problem and its application in cardiac trabeculae reconstruction. In: IJCAI, pp. 1312–1318 (2019)

    Google Scholar 

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Authors and Affiliations

  1. Stony Brook University, Stony Brook, NY, 11794, USA

    Fan Wang, Saarthak Kapse, Steven Liu, Prateek Prasanna & Chao Chen

Authors
  1. Fan Wang
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  2. Saarthak Kapse
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  3. Steven Liu
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  4. Prateek Prasanna
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  5. Chao Chen
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Corresponding author

Correspondence to Fan Wang .

Editor information

Editors and Affiliations

  1. Technical University of Denmark, Kgs Lyngby, Denmark

    Aasa Feragen

  2. University of Copenhagen, Copenhagen, Denmark

    Stefan Sommer

  3. King's College London, London, UK

    Julia Schnabel

  4. University of Copenhagen, Copenhagen, Denmark

    Mads Nielsen

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Wang, F., Kapse, S., Liu, S., Prasanna, P., Chen, C. (2021). TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds) Information Processing in Medical Imaging. IPMI 2021. Lecture Notes in Computer Science(), vol 12729. Springer, Cham. https://doi.org/10.1007/978-3-030-78191-0_30

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  • DOI: https://doi.org/10.1007/978-3-030-78191-0_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78190-3

  • Online ISBN: 978-3-030-78191-0

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