Analyzing breast tumor heterogeneity to predict the response to chemotherapy using 3D MR images registration

ABSTRACT

Breast tumor structure is extremely heterogeneous. This heterogeneity changes spatially during chemotherapy treatment. This was correlated with how the tumor reacts to Neoadjuvant chemotherapy (treatment presiding surgery). A significant number of studies adopting Magnetic Resonance Imaging (MRI) have looked into the quantification of intratumor heterogeneity of breast cancer. Nevertheless, a limited number of them are interested chiefly in evaluating breast cancer heterogeneity as index of response to treatment. In this paper, we present a new approach that compares breast tumor heterogeneity degree, using acquired images before and after chemotherapy. The purpose of our study is to help radiologists to predict the effectiveness of Neoadjuvant chemotherapy as soon as possible. Indeed, many patients with breast cancer have a non-responding tumor type to chemotherapy. However, these patients still requiring avoidable chemotherapies during a long time, which causes several undesirable effects. We evaluate our study using a data set constructed by Dynamic Contrast Enhanced (DCE-MRI) and Diffusion Weighed sequences of MRI (DW-MRI), consisting of 64 adult patients. Our approach consists to apply a volumetric registration between images acquired before and after chemotherapy to quantify induced changes on the tumor by the first chemotherapy session. The first step of our approach is to segment the volume of interest (VOI). Then, a coherent volumetric registration will provide to make a voxel by voxel comparison of breast tumor volume. Therefore, The breast tumor response rate to the first chemotherapy session was obtained, by comparing each voxel intensity in DCE-MR and DW-MRI sequences before and after the chemotherapy. This approach will not only provide the breast tumor response degree to chemotherapy, but also monitoring tumor regions that have responded, not responded and tumor regions that have recognized disease progression during chemotherapy session. This will allow radiologists and oncologists to decide if the patient will continue to require chemotherapy, or applying other alternative solutions, without wasting time in unnecessary chemotherapy sessions.

References

1. Ahmed, A., Gibbs, P., Pickles, M., and Turnbull, L. Texture analysis in assessment and prediction of chemotherapy response in breast cancer. Journal of Magnetic Resonance Imaging 38, 1 (2013), 89--101.
2. Bedard, P. L., Hansen, A. R., Ratain, M. J., and Siu, L. L. Tumour heterogeneity in the clinic. Nature 501, 7467 (2013), 355--364.
3. Boes, J. L., Hoff, B. A., Hylton, N., Pickles, M. D., Turnbull, L. W., Schott, A. F., Rehemtulla, A., Chamberlain, R., Lemasson, B., Chenevert, T. L., et al. Image registration for quantitative parametric response mapping of cancer treatment response. Translational oncology 7, 1 (2014), 101--110.
4. Bookstein, F. L. Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Transactions on pattern analysis and machine intelligence 11, 6 (1989), 567--585.
5. Carter, J. S., Koopmeiners, J. S., Kuehn-Hajder, J. E., Metzger, G. J., Lakkadi, N., Downs, L. S., and Bolan, P. J. Quantitative multiparametric mri of ovarian cancer. Journal of Magnetic Resonance Imaging 38, 6 (2013), 1501--1509.
6. Cho, G. Y., Moy, L., Kim, S. G., Baete, S. H., Moccaldi, M., Babb, J. S., Sodickson, D. K., and Sigmund, E. E. Evaluation of breast cancer using intravoxel incoherent motion (ivim) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors. European radiology 26, 8 (2016), 2547--2558.
7. Cho, N., Im, S.-A., Park, I.-A., Lee, K.-H., Li, M., Han, W., Noh, D.-Y., and Moon, W. K. Breast cancer: early prediction of response to neoadjuvant chemotherapy using parametric response maps for mr imaging. Radiology 272, 2 (2014), 385--396.
8. Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. The cancer imaging archive (tcia): maintaining and operating a public information repository. Journal of digital imaging 26, 6 (2013), 1045--1057.
9. Eisenhauer, E., Therasse, P., Bogaerts, J., Schwartz, L., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., et al. New response evaluation criteria in solid tumours: revised recist guideline (version 1.1). European journal of cancer 45, 2 (2009), 228--247.
10. Elmore, J. G., Longton, G. M., Carney, P. A., Geller, B. M., Onega, T., Tosteson, A. N., Nelson, H. D., Pepe, M. S., Allison, K. H., Schnitt, S. J., et al. Diagnostic concordance among pathologists interpreting breast biopsy specimens. Jama 313, 11 (2015), 1122--1132.
11. Fox, M. J., Gibbs, P., and Pickles, M. D. Minkowski functionals: An mri texture analysis tool for determination of the aggressiveness of breast cancer. Journal of Magnetic Resonance Imaging (2015).
12. Galbán, C. J., Chenevert, T. L., Meyer, C. R., Tsien, C., Lawrence, T. S., Hamstra, D. A., Junck, L., Sundgren, P. C., Johnson, T. D., Galbán, S., et al. Prospective analysis of parametric response map-derived mri biomarkers: identification of early and distinct glioma response patterns not predicted by standard radiographic assessment. Clinical Cancer Research 17, 14 (2011), 4751--4760.
13. Galbán, C. J., Chenevert, T. L., Meyer, C. R., Tsien, C., Lawrence, T. S., Hamstra, D. A., Junck, L., Sundgren, P. C., Johnson, T. D., Ross, D. J., et al. The parametric response map is an imaging biomarker for early cancer treatment outcome. Nature medicine 15, 5 (2009), 572--576.
14. Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl j Med 2012, 366 (2012), 883--892.
15. Geyer, F. C., Weigelt, B., Natrajan, R., Lambros, M. B., de Biase, D., Vatcheva, R., Savage, K., Mackay, A., Ashworth, A., and Reis-Filho, J. S. Molecular analysis reveals a genetic basis for the phenotypic diversity of metaplastic breast carcinomas. The Journal of pathology 220, 5 (2010), 562--573.
16. Golden, D. I., Lipson, J. A., Telli, M. L., Ford, J. M., and Rubin, D. L. Dynamic contrast-enhanced mri-based biomarkers of therapeutic response in triple-negative breast cancer. Journal of the American Medical Informatics Association 20, 6 (2013), 1059--1066.
17. Hamstra, D. A., Galbán, C. J., Meyer, C. R., Johnson, T. D., Sundgren, P. C., Tsien, C., Lawrence, T. S., Junck, L., Ross, D. J., Rehemtulla, A., et al. Functional diffusion map as an early imaging biomarker for high-grade glioma: correlation with conventional radiologic response and overall survival. Journal of clinical oncology 26, 20 (2008), 3387--3394.
18. Haralick, R. M., Shanmugam, K., et al. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics 3, 6 (1973), 610--621.
19. Hayes, C., Padhani, A. R., and Leach, M. O. Assessing changes in tumour vascular function using dynamic contrast-enhanced magnetic resonance imaging. NMR in Biomedicine 15, 2 (2002), 154--163.
20. Issa, B., Buckley, D. L., and Turnbull, L. W. Heterogeneity analysis of gd-dtpa uptake: improvement in breast lesion differentiation. Journal of computer assisted tomography 23, 4 (1999), 615--621.
21. Johansen, R., Jensen, L. R., Rydland, J., Goa, P. E., Kvistad, K. A., Bathen, T. F., Axelson, D. E., Lundgren, S., and Gribbestad, I. S. Predicting survival and early clinical response to primary chemotherapy for patients with locally advanced breast cancer using dce-mri. Journal of Magnetic Resonance Imaging 29, 6 (2009), 1300--1307.
22. Just, N. Improving tumour heterogeneity mri assessment with histograms. British journal of cancer 111, 12 (2014), 2205--2213.
23. Kim, J.-H., Ko, E. S., Lim, Y., Lee, K. S., Han, B.-K., Ko, E. Y., Hahn, S. Y., and Nam, S. J. Breast cancer heterogeneity: Mr imaging texture analysis and survival outcomes. Radiology (2016), 160261.
24. Li, X., Dawant, B. M., Welch, E. B., Chakravarthy, A. B., Freehardt, D., Mayer, I., Kelley, M., Meszoely, I., Gore, J. C., and Yankeelov, T. E. A nonrigid registration algorithm for longitudinal breast mr images and the analysis of breast tumor response. Magnetic resonance imaging 27, 9 (2009), 1258--1270.
25. Martelotto, L. G., Ng, C. K., Piscuoglio, S., Weigelt, B., and Reis-Filho, J. S. Breast cancer intra-tumor heterogeneity. Breast Cancer Research 16, 3 (2014), 3401.
26. Michoux, N., Van den Broeck, S., Lacoste, L., Fellah, L., Galant, C., Berlière, M., and Leconte, I. Texture analysis on mr images helps predicting non-response to nac in breast cancer. BMC cancer 15, 1 (2015), 574.
27. Moffat, B. A., Chenevert, T. L., Lawrence, T. S., Meyer, C. R., Johnson, T. D., Dong, Q., Tsien, C., Mukherji, S., Quint, D. J., Gebarski, S. S., et al. Functional diffusion map: a noninvasive mri biomarker for early stratification of clinical brain tumor response. Proceedings of the National Academy of Sciences of the United States of America 102, 15 (2005), 5524--5529.
28. Mukherjee, G., Chatterjee, A., and Tudu, B. Study on the potential of combined glcm features towards medicinal plant classification. In Control, Instrumentation, Energy & Communication (CIEC), 2016 2nd International Conference on (2016), IEEE, pp. 98--102.
29. Navin, N., Kendall, J., Troge, J., Andrews, P., Rodgers, L., McIndoo, J., Cook, K., Stepansky, A., Levy, D., Esposito, D., et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 7341 (2011), 90--94.
30. Padhani, A. R., Hayes, C., Assersohn, L., Powles, T., Makris, A., Suckling, J., Leach, M. O., and Husband, J. E. Prediction of clinicopathologic response of breast cancer to primary chemotherapy at contrast-enhanced mr imaging: initial clinical results 1. Radiology 239, 2 (2006), 361--374.
31. Parikh, J., Selmi, M., Charles-Edwards, G., Glendenning, J., Ganeshan, B., Verma, H., Mansi, J., Harries, M., Tutt, A., and Goh, V. Changes in primary breast cancer heterogeneity may augment midtreatment mr imaging assessment of response to neoadjuvant chemotherapy. Radiology 272, 1 (2014), 100--112.
32. Rose, C. J., Mills, S. J., O'Connor, J. P., Buonaccorsi, G. A., Roberts, C., Watson, Y., Cheung, S., Zhao, S., Whitcher, B., Jackson, A., et al. Quantifying spatial heterogeneity in dynamic contrast-enhanced mri parameter maps. Magnetic Resonance in Medicine 62, 2 (2009), 488--499.
33. Sezgin, M., et al. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic imaging 13, 1 (2004), 146--168.
34. Shah, S. P., Morin, R. D., Khattra, J., Prentice, L., Pugh, T., Burleigh, A., Delaney, A., Gelmon, K., Guliany, R., Senz, J., et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature 461, 7265 (2009), 809--813.
35. Sharp, G., Li, R., Wolfgang, J., Chen, G., Peroni, M., Spadea, M., Mori, S., Zhang, J., Shackleford, J., and Kandasamy, N. Plastimatch - an open source software suite for radiotherapy image processing. In Proceedings of the XVIâĂŹth International Conference on the use of Computers in Radiotherapy (ICCR), Amsterdam, Netherlands (2010).
36. Teruel, J. R., Heldahl, M. G., Goa, P. E., Pickles, M., Lundgren, S., Bathen, T. F., and Gibbs, P. Dynamic contrast-enhanced mri texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. NMR in Biomedicine 27, 8 (2014), 887--896.
37. Therasse, P., Mauriac, L., Welnicka-Jaskiewicz, M., Bruning, P., Cufer, T., Bonnefoi, H., Tomiak, E., Pritchard, K., Hamilton, A., and Piccart, M. Final results of a randomized phase iii trial comparing cyclophosphamide, epirubicin, and fluorouracil with a dose-intensified epirubicin and cyclophosphamide+ filgrastim as neoadjuvant treatment in locally advanced breast cancer: an eortc-ncic-sakk multicenter study. Journal of clinical oncology 21, 5 (2003), 843--850.
38. Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., and Gee, J. C. N4itk: improved n3 bias correction. IEEE transactions on medical imaging 29, 6 (2010), 1310--1320.
39. Varma, D. R. Managing dicom images: Tips and tricks for the radiologist. The Indian journal of radiology & imaging 22, 1 (2012), 4.
40. Whelan, P. F., and Molloy, D. Machine vision algorithms in Java: techniques and implementation. Springer Science & Business Media, 2012.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

        • Image segmentation

        • Video segmentation

  • Computer graphics

    • Image manipulation