Literature DB >> 26336119

A New Approach to Evaluate Drug Treatment Response of Ovarian Cancer Patients Based on Deformable Image Registration.

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

Abstract

Although Response Evaluation Criteria in Solid Tumors (RECIST) is the current clinical guideline to assess size change of solid tumors after therapeutic treatment, it has a relatively lower association to the clinical outcome of progression free survival (PFS) of the patients. In this paper, we presented a new approach to assess responses of ovarian cancer patients to new chemotherapy drugs in clinical trials. We first developed and applied a multi-resolution B-spline based deformable image registration method to register two sets of computed tomography (CT) image data acquired pre- and post-treatment. The B-spline difference maps generated from the co-registered CT images highlight the regions related to the volumetric growth or shrinkage of the metastatic tumors, and density changes related to variation of necrosis inside the solid tumors. Using a testing dataset involving 19 ovarian cancer patients, we compared patients' response to the treatment using the new image registration method and RECIST guideline. The results demonstrated that using the image registration method yielded higher association with the six-month PFS outcomes of the patients than using RECIST. The image registration results also provided a solid foundation of developing new computerized quantitative image feature analysis schemes in the future studies.

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Year:  2015        PMID: 26336119      PMCID: PMC5161344          DOI: 10.1109/TMI.2015.2473823

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  21 in total

1.  Nonrigid registration using free-form deformations: application to breast MR images.

Authors:  D Rueckert; L I Sonoda; C Hayes; D L Hill; M O Leach; D J Hawkes
Journal:  IEEE Trans Med Imaging       Date:  1999-08       Impact factor: 10.048

2.  PET-CT image registration in the chest using free-form deformations.

Authors:  David Mattes; David R Haynor; Hubert Vesselle; Thomas K Lewellen; William Eubank
Journal:  IEEE Trans Med Imaging       Date:  2003-01       Impact factor: 10.048

Review 3.  The value of progression-free survival to patients with advanced-stage cancer.

Authors:  Lesley J Fallowfield; Anne Fleissig
Journal:  Nat Rev Clin Oncol       Date:  2011-10-18       Impact factor: 66.675

4.  Fast free-form deformation using graphics processing units.

Authors:  Marc Modat; Gerard R Ridgway; Zeike A Taylor; Manja Lehmann; Josephine Barnes; David J Hawkes; Nick C Fox; Sébastien Ourselin
Journal:  Comput Methods Programs Biomed       Date:  2009-10-08       Impact factor: 5.428

5.  Tumor cavitation in stage I non-small cell lung cancer: epidermal growth factor receptor expression and prediction of poor outcome.

Authors:  Amir Onn; Du Hwan Choe; Roy S Herbst; Arlene M Correa; Reginald F Munden; Mylene T Truong; Ara A Vaporciyan; Takeshi Isobe; Michael Z Gilcrease; Edith M Marom
Journal:  Radiology       Date:  2005-10       Impact factor: 11.105

6.  Detection and characterization of tumor changes in 18F-FDG PET patient monitoring using parametric imaging.

Authors:  Hatem Necib; Camilo Garcia; Antoine Wagner; Bruno Vanderlinden; Patrick Emonts; Alain Hendlisz; Patrick Flamen; Irène Buvat
Journal:  J Nucl Med       Date:  2011-03       Impact factor: 10.057

7.  A model for predicting surgical outcome in patients with advanced ovarian carcinoma using computed tomography.

Authors:  R E Bristow; L R Duska; N C Lambrou; E K Fishman; M J O'Neill; E L Trimble; F J Montz
Journal:  Cancer       Date:  2000-10-01       Impact factor: 6.860

8.  Computed tomography assessment of response to therapy: tumor volume change measurement, truth data, and error.

Authors:  Michael F McNitt-Gray; Luc M Bidaut; Samuel G Armato; Charles R Meyer; Marios A Gavrielides; Charles Fenimore; Geoffrey McLennan; Nicholas Petrick; Binsheng Zhao; Anthony P Reeves; Reinhard Beichel; Hyun-Jung Grace Kim; Lisa Kinnard
Journal:  Transl Oncol       Date:  2009-12       Impact factor: 4.243

Review 9.  The anaplastic lymphoma kinase in the pathogenesis of cancer.

Authors:  Roberto Chiarle; Claudia Voena; Chiara Ambrogio; Roberto Piva; Giorgio Inghirami
Journal:  Nat Rev Cancer       Date:  2008-01       Impact factor: 60.716

Review 10.  CA 125, PET alone, PET-CT, CT and MRI in diagnosing recurrent ovarian carcinoma: a systematic review and meta-analysis.

Authors:  Ping Gu; Ling-Ling Pan; Shu-Qi Wu; Li Sun; Gang Huang
Journal:  Eur J Radiol       Date:  2008-04-18       Impact factor: 3.528

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  6 in total

1.  Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer.

Authors:  Sadegh Riyahi; Wookjin Choi; Chia-Ju Liu; Hualiang Zhong; Abraham J Wu; James G Mechalakos; Wei Lu
Journal:  Phys Med Biol       Date:  2018-07-19       Impact factor: 3.609

2.  Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy.

Authors:  Gopichandh Danala; Theresa Thai; Camille C Gunderson; Katherine M Moxley; Kathleen Moore; Robert S Mannel; Hong Liu; Bin Zheng; Yuchen Qiu
Journal:  Acad Radiol       Date:  2017-05-26       Impact factor: 3.173

3.  4D-CT deformable image registration using multiscale unsupervised deep learning.

Authors:  Yang Lei; Yabo Fu; Tonghe Wang; Yingzi Liu; Pretesh Patel; Walter J Curran; Tian Liu; Xiaofeng Yang
Journal:  Phys Med Biol       Date:  2020-04-20       Impact factor: 3.609

4.  Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker.

Authors:  Abolfazl Zargari; Yue Du; Morteza Heidari; Theresa C Thai; Camille C Gunderson; Kathleen Moore; Robert S Mannel; Hong Liu; Bin Zheng; Yuchen Qiu
Journal:  Phys Med Biol       Date:  2018-08-06       Impact factor: 3.609

5.  Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome.

Authors:  Yunzhi Wang; Yuchen Qiu; Theresa Thai; Kathleen Moore; Hong Liu; Bin Zheng
Journal:  BMC Med Imaging       Date:  2016-08-31       Impact factor: 1.930

6.  Developing global image feature analysis models to predict cancer risk and prognosis.

Authors:  Bin Zheng; Yuchen Qiu; Faranak Aghaei; Seyedehnafiseh Mirniaharikandehei; Morteza Heidari; Gopichandh Danala
Journal:  Vis Comput Ind Biomed Art       Date:  2019-11-19
  6 in total

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