Advertisement

Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response

  • Sadegh Riyahi
  • Wookjin Choi
  • Chia-Ju Liu
  • Saad Nadeem
  • Shan Tan
  • Hualiang Zhong
  • Wengen Chen
  • Abraham J. Wu
  • James G. Mechalakos
  • Joseph O. Deasy
  • Wei Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11076)

Abstract

Quantification of local metabolic tumor volume (MTV) changes after Chemo-radiotherapy would allow accurate tumor response evaluation. Currently, local MTV changes in esophageal (soft-tissue) cancer are measured by registering follow-up PET to baseline PET using the same transformation obtained by deformable registration of follow-up CT to baseline CT. Such approach is suboptimal because PET and CT capture fundamentally different properties (metabolic vs. anatomy) of a tumor. In this work we combined PET and CT images into a single blended PET-CT image and registered follow-up blended PET-CT image to baseline blended PET-CT image. B-spline regularized diffeomorphic registration was used to characterize the large MTV shrinkage. Jacobian of the resulting transformation was computed to measure the local MTV changes. Radiomic features (intensity and texture) were then extracted from the Jacobian map to predict pathologic tumor response. Local MTV changes calculated using blended PET-CT registration achieved the highest correlation with ground truth segmentation (R = 0.88) compared to PET-PET (R = 0.80) and CT-CT (R = 0.67) registrations. Moreover, using blended PET-CT registration, the multivariate prediction model achieved the highest accuracy with only one Jacobian co-occurrence texture feature (accuracy = 82.3%). This novel framework can replace the conventional approach that applies CT-CT transformation to the PET data for longitudinal evaluation of tumor response.

References

  1. 1.
    Ashburner, J.: A fast diffeomorphic image registration algorithm. NeuroImage 38(1), 95–113 (2007)CrossRefGoogle Scholar
  2. 2.
    Choi, W., et al.: Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Med. Phys. 45(4), 1537–1549 (2018)CrossRefGoogle Scholar
  3. 3.
    Fuentes, D., et al.: Morphometry-based measurements of the structural response to whole-brain radiation. Int. J. Comput. Assist. Radiol. Surg. 10(4), 393–401 (2015)CrossRefGoogle Scholar
  4. 4.
    Jin, S., Li, D., Wang, H., Yin, Y.: Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients. J. Appl. Clin. Med. Phys. 14(1), 50–61 (2013)CrossRefGoogle Scholar
  5. 5.
    Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)CrossRefGoogle Scholar
  6. 6.
    Krebs, J., et al.: Robust non-rigid registration through agent-based action learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 344–352. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66182-7_40CrossRefGoogle Scholar
  7. 7.
    Staring, M., Klein, S., Pluim, J.P.: A rigidity penalty term for nonrigid registration. Med. Phys. 34(11), 4098–4108 (2007)CrossRefGoogle Scholar
  8. 8.
    Riyahi, S.: Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer. Phys. Med. Biol. 63(14), 145020 (2018)CrossRefGoogle Scholar
  9. 9.
    Tan, S., Li, L., Choi, W., Kang, M.K., D’Souza, D., Lu, W.: Adaptive region-growing with maximum curvature strategy for tumor segmentation in 18F-FDG PET. Phys. Med. Biol. 62(13), 5383 (2017)CrossRefGoogle Scholar
  10. 10.
    Tustison, N., Avants, B.: Explicit B-spline regularization in diffeomorphic image registration. Front. Neuroinformatics 7(39), 1–13 (2013)Google Scholar
  11. 11.
    van Velden, F.H.P., Nissen, I.A., Hayes, W., Velasquez, L.M., Hoekstra, O.S., et al.: Effects of reusing baseline volumes of interest by applying (non-)rigid image registration on positron emission tomography response assessments. PloS one 9(1), e87167 (2014)CrossRefGoogle Scholar
  12. 12.
    Westerterp, M., et al.: Esophageal cancer: CT, endoscopic US, and FDG PET for assessment of response to neoadjuvant therapy-systematic review. Radiology 236(3), 841–851 (2005)CrossRefGoogle Scholar
  13. 13.
    Yip, S.S.F., et al.: Relationship between the temporal changes in positron-emission-tomography-imaging-based textural features and pathologic response and survival in esophageal cancer patients. Front. Oncol. 6, 72 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sadegh Riyahi
    • 1
  • Wookjin Choi
    • 1
  • Chia-Ju Liu
    • 1
  • Saad Nadeem
    • 1
  • Shan Tan
    • 2
  • Hualiang Zhong
    • 3
  • Wengen Chen
    • 4
  • Abraham J. Wu
    • 1
  • James G. Mechalakos
    • 1
  • Joseph O. Deasy
    • 1
  • Wei Lu
    • 1
  1. 1.Memorial Sloan Kettering Cancer CenterNew YorkUSA
  2. 2.Huazhong University of Science and TechnologyWuhanChina
  3. 3.Henry Ford HospitalDetroitUSA
  4. 4.University of Maryland School of MedicineBaltimoreUSA

Personalised recommendations