A new approach to evaluate drug treatment response of ovarian cancer patients based on deformable image registration

IEEE Transactions on Medical Imaging

Volumn 35, Issue 1, 2016, Pages 316-325

  Tan, Maxine ^a   Li, Zheng ^a   Qiu, Yuchen ^a   McMeekin, Scott D ^b   Thai, Theresa C ^b   Ding, Kai ^b   Moore, Kathleen N ^b   Liu, Hong ^a   Zheng, Bin ^a  

^a University of Oklahoma   (United States)

^b UNIVERSITY OF OKLAHOMA   (United States)

Author keywords

Computed tomography (CT); Computer aided diagnosis (CAD); Deformable image registration; Metastatic tumors; Ovarian cancer; Response evaluation criteria in solid tumors (RECIST); Tumor volume and necrosis tracking

Indexed keywords

CHEMOTHERAPY; COMPUTER AIDED DIAGNOSIS; DEFORMATION; DIAGNOSIS; DISEASES; DRUG THERAPY; IMAGE REGISTRATION; INTERPOLATION; PATIENT TREATMENT; STATISTICAL TESTS; TOMOGRAPHY; TUMORS;

COMPUTER AIDED DIAGNOSIS(CAD); DEFORMABLE IMAGE REGISTRATION; METASTATIC TUMORS; OVARIAN CANCERS; SOLID TUMORS; TUMOR VOLUMES;

COMPUTERIZED TOMOGRAPHY;

ANTINEOPLASTIC AGENT; IOPAMIDOL;

ARTICLE; CANCER CHEMOTHERAPY; CANCER PATIENT; CANCER STAGING; CLINICAL ARTICLE; CLINICAL PRACTICE; COMPUTED TOMOGRAPHY SCANNER; COMPUTER ASSISTED TOMOGRAPHY; CONTROLLED STUDY; DEFORMABLE IMAGE REGISTRATION; HUMAN; LIVER METASTASIS; OVARY CANCER; PREDICTION; RADIOLOGICAL PROCEDURES; RETROSPECTIVE STUDY; TREATMENT RESPONSE; TUMOR VOLUME; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; FEMALE; NECROSIS; OVARIAN NEOPLASMS; PATHOLOGY; PROCEDURES; X-RAY COMPUTED TOMOGRAPHY;

ANTINEOPLASTIC AGENTS; FEMALE; HUMANS; NECROSIS; OVARIAN NEOPLASMS; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84959364855     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2015.2473823     Document Type: Article

References (29)

1. What are the key statistics about ovarian cancer? Am. Cancer Soc., 2014 [Online]. Available: Http://www.cancer.org/cancer/ovariancancer/detailedguide/ovarian-cancer-key-statistics
2. G. A. Omura et al., "Long-term follow-up and prognostic factor analysis in advanced ovarian carcinoma: The Gynecologic Oncology Group experience," J. Clin. Oncol., vol. 9, pp. 1138-1150, Jul. 1991.
3. R. Chiarle, C. Voena, C. Ambrogio, R. Piva, and G. Inghirami, "The anaplastic lymphoma kinase in the pathogenesis of cancer," Nat. Rev. Cancer, vol. 8, pp. 11-23, 2008.
4. C. M. Goetsch, "Genetic tumor profiling and genetically targeted cancer therapy," Semin. Oncol. Nurs., vol. 27, pp. 34-44, Feb. 2011.
5. J. J. Folk, M. Botsford, and A. G. Musa, "Monitoring cancer antigen 125 levels in induction chemotherapy for epithelial ovarian carcinoma and predicting outcome of second-look procedure," Gynecol. Oncol., vol. 57, pp. 178-182, May 1995.
6. P. Gu, L. L. Pan, S. Q. Wu, L. Sun, and G. Huang, "CA 125, PET alone, PET-CT, CT and MRI in diagnosing recurrent ovarian carcinoma: A systematic review and meta-analysis," Eur. J. Radiol., vol. 71, pp. 164-174, Jul. 2009.
7. R. E. Bristow et al., "A model for predicting surgical outcome in patients with advanced ovarian carcinoma using computed tomography," Cancer, vol. 89, pp. 1532-1540, Oct. 2000.
8. E. A. Eisenhauer et al., "New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)," Eur. J. Cancer, vol. 45, pp. 228-247, Jan. 2009.
9. M. R. Sharma, M. L. Maitland, and M. J. Ratain, "RECIST: No longer the sharpest tool in the oncology clinical trials toolbox-point," Cancer Res., vol. 72, pp. 5145-9, Oct. 2012.
10. A. Onn et al., "Tumor cavitation in stage I non-small cell lung cancer: Epidermal growth factor receptor expression and prediction of poor outcome," Radiology, vol. 237, pp. 342-347, Oct. 2005.
11. E. Segal et al., "Decoding global gene expression programs in liver cancer by noninvasive imaging," Nature Biotechnol., vol. 25, pp. 675-680, May 2007.
12. L. R. Folio, A. Sandouk, J. Huang, J. M. Solomon, and A. B. Apolo, "Consistency and efficiency of CT analysis of metastatic disease: Semiautomated lesion management application within a PACS," Am. J. Roentgenol., vol. 201, pp. 618-625, Sep. 2013.
13. L. R. Folio, M. M. Choi, J. M. Solomon, and N. P. Schaub, "Automated registration, segmentation, and measurement of metastatic melanoma tumors in serial CT scans," Acad. Radiol., vol. 20, pp. 604-613, May 2013.
14. H. Necib et al., "Detection and characterization of tumor changes in F-18-FDG PET patient monitoring using parametric imaging," J. Nucl. Med., vol. 52, pp. 354-361, Mar. 2011.
15. L. J. Fallowfield and A. Fleissig, "The value of progression-free survival to patients with advanced-stage cancer," Nat. Rev. Clin. Oncol., vol. 9, pp. 41-47, Jan. 2012.
16. D. Mattes, D. R. Haynor, H. Vesselle, T. K. Lewellen, and W. Eubank, "PET-CT image registration in the chest using free-form deformations," IEEE Trans. Med. Imag., vol. 22, no. 1, pp. 120-128, Jan. 2003.
17. D. Mattes, D. R. Haynor, H. Vesselle, T. K. Lewellyn, and W. Eubank, "Nonrigid multimodality image registration," in SPIE Med. Imag. 2001, Image Process., San Diego, CA, 2001, pp. 1609-1620.
18. D. Rueckert et al., "Nonrigid registration using free-form deformations: Application to breast MR images," IEEE Trans. Med. Imag., vol. 18, no. 8, pp. 712-721, Aug. 1999.
19. H. J. Johnson, M. McCormick, and L. Ibanez, The ITK Software Guide 4.5 ed., 2013.
20. A. Sotiras, C. Davatzikos, and N. Paragios, "Deformable medical image registration: A survey," IEEE Trans. Med. Imag., vol. 32, no. 7, pp. 1153-1190, Jul. 2013.
21. C. Zhu, R. H. Byrd, P. Lu, and J. Nocedal, "Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization," ACM Trans. Math. Softw., vol. 23, pp. 550-560, 1997.
22. R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu, "A limited memory algorithm for bound constrained optimization," SIAM J. Sci. Comput., vol. 16, pp. 1190-1208, 1995.
23. P. Thevenaz and M. A. Unser, "Spline pyramids for intermodal image registration using mutual information," in SPIE Wavelet Appl. Signal Image Process. V, San Diego, CA, 1997, pp. 236-247.
24. M. F. McNitt-Gray et al., "Computed tomography assessment of response to therapy: Tumor volume change measurement, truth data, and error," Translat. Oncol., vol. 2, pp. 216-222, Dec. 2009.
25. J. Hou, M. Guerrero, M. Suntharalingam, and W. D. D'Souza, "Response assessment in locally advanced head and neck cancer based on RECIST and volume measurements using cone beam CT images," Technol. Cancer Res. Treat., vol. 14, pp. 19-27, Feb. 2015.
26. R. Varadhan, G. Karangelis, K. Krishnan, and S. Hui, "A framework for deformable image registration validation in radiotherapy clinical applications," J. Appl. Clin. Med. Phys., vol. 14, p. 4066, 2013.
27. D. Rueckert and P. Aljabar, "Nonrigid registration of medical images: Theory, methods, and applications [applications corner]," IEEE Signal Process. Mag., vol. 27, no. 4, pp. 113-119, Jul. 2010.
28. M. Modat et al., "Fast free-form deformation using graphics processing units," Comput. Methods Programs Biomed., vol. 98, pp. 278-284, 2010.
29. D. Forsberg, A. Eklund, M. Andersson, and H. Knutsson, "Phase-based non-rigid 3D image registration: From minutes to seconds using CUDA," presented at the Joint MICCAI Workshop High Performance Distrib. Comput. Med. Imag., Toronto, ON, Canada, 2011.