Literature DB >> 20052727

Ultra-early predictive assay for treatment failure using functional magnetic resonance imaging and clinical prognostic parameters in cervical cancer.

Nina A Mayr1, William T C Yuh, David Jajoura, Jian Z Wang, Simon S Lo, Joseph F Montebello, Kyle Porter, Dongqing Zhang, D Scott McMeekin, John M Buatti.   

Abstract

BACKGROUND: The authors prospectively evaluated magnetic resonance imaging (MRI) parameters quantifying heterogeneous perfusion pattern and residual tumor volume early during treatment in cervical cancer, and compared their predictive power for primary tumor recurrence and cancer death with the standard clinical prognostic factors. A novel approach of augmenting the predictive power of clinical prognostic factors with MRI parameters was assessed.
METHODS: Sixty-two cervical cancer patients underwent dynamic contrast-enhanced (DCE) MRI before and during early radiation/chemotherapy (2-2.5 weeks into treatment). Heterogeneous tumor perfusion was analyzed by signal intensity (SI) of each tumor voxel. Poorly perfused tumor regions were quantified as lower 10th percentile of SI (SI[10%]). DCE-MRI and 3-dimensional (3D) tumor volumetry MRI parameters were assessed as predictors of recurrence and cancer death (median follow-up, 4.1 years). Their discriminating capacity was compared with clinical prognostic factors (stage, lymph node status, histology) using sensitivity/specificity and Cox regression analysis.
RESULTS: SI(10%) and 3D volume 2-2.5 weeks into therapy independently predicted disease recurrence (hazard ratio [HR], 2.6; 95% confidence interval [95% CI], 1.0-6.5 [P = .04] and HR, 1.9; 95% CI, 1.1-3.5 [P = .03], respectively) and death (HR, 1.9; 95% CI, 1.0-3.5 [P = .03] and HR, 1.9; 95% CI, 1.2-2.9 [P = .01], respectively), and were superior to clinical prognostic factors. The addition of MRI parameters to clinical prognostic factors increased sensitivity and specificity of clinical prognostic factors from 71% and 51%, respectively, to 100% and 71%, respectively, for predicting recurrence, and from 79% and 54%, respectively, to 93% and 60%, respectively, for predicting death.
CONCLUSIONS: MRI parameters reflecting heterogeneous tumor perfusion and subtle tumor volume change early during radiation/chemotherapy are independent and better predictors of tumor recurrence and death than clinical prognostic factors. The combination of clinical prognostic factors and MRI parameters further improves early prediction of treatment failure and may enable a window of opportunity to alter treatment strategy.

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Year:  2010        PMID: 20052727      PMCID: PMC4362726          DOI: 10.1002/cncr.24822

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


  31 in total

1.  Tumor size evaluated by pelvic examination compared with 3-D quantitative analysis in the prediction of outcome for cervical cancer.

Authors:  N A Mayr; W T Yuh; J Zheng; J C Ehrhardt; J I Sorosky; V A Magnotta; R E Pelsang; D H Hussey
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Review 2.  MR microcirculation assessment in cervical cancer: correlations with histomorphological tumor markers and clinical outcome.

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Review 5.  Barriers to drug delivery in solid tumors.

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6.  Tumor diameter and volume assessed by magnetic resonance imaging in the prediction of outcome for invasive cervical cancer.

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Journal:  Gynecol Oncol       Date:  2001-09       Impact factor: 5.482

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8.  Tumor perfusion studies using fast magnetic resonance imaging technique in advanced cervical cancer: a new noninvasive predictive assay.

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Journal:  Int J Radiat Oncol Biol Phys       Date:  1996-10-01       Impact factor: 7.038

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  19 in total

1.  Multi-parametric MRI in cervical cancer: early prediction of response to concurrent chemoradiotherapy in combination with clinical prognostic factors.

Authors:  Wei Yang; Jin Wei Qiang; Hai Ping Tian; Bing Chen; Ai Jun Wang; Jian Guo Zhao
Journal:  Eur Radiol       Date:  2017-08-04       Impact factor: 5.315

Review 2.  Applications of imaging technology in radiation research.

Authors:  MingDe Lin; Edward F Jackson
Journal:  Radiat Res       Date:  2012-03-08       Impact factor: 2.841

Review 3.  Image-guided radiotherapy: from current concept to future perspectives.

Authors:  David A Jaffray
Journal:  Nat Rev Clin Oncol       Date:  2012-11-20       Impact factor: 66.675

4.  Characterizing tumor heterogeneity with functional imaging and quantifying high-risk tumor volume for early prediction of treatment outcome: cervical cancer as a model.

Authors:  Nina A Mayr; Zhibin Huang; Jian Z Wang; Simon S Lo; Joline M Fan; John C Grecula; Steffen Sammet; Christina L Sammet; Guang Jia; Jun Zhang; Michael V Knopp; William T C Yuh
Journal:  Int J Radiat Oncol Biol Phys       Date:  2011-12-28       Impact factor: 7.038

Review 5.  Imaging tumor hypoxia to advance radiation oncology.

Authors:  Chen-Ting Lee; Mary-Keara Boss; Mark W Dewhirst
Journal:  Antioxid Redox Signal       Date:  2014-03-24       Impact factor: 8.401

6.  Impact of perfusion map analysis on early survival prediction accuracy in glioma patients.

Authors:  Benjamin Lemasson; Thomas L Chenevert; Theodore S Lawrence; Christina Tsien; Pia C Sundgren; Charles R Meyer; Larry Junck; Jennifer Boes; Stefanie Galbán; Timothy D Johnson; Alnawaz Rehemtulla; Brian D Ross; Craig J Galbán
Journal:  Transl Oncol       Date:  2013-12-01       Impact factor: 4.243

7.  Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic response.

Authors:  R Venkatasubramanian; R B Arenas; M A Henson; N S Forbes
Journal:  Br J Cancer       Date:  2010-07-13       Impact factor: 7.640

8.  Validation of optimal DCE-MRI perfusion threshold to classify at-risk tumor imaging voxels in heterogeneous cervical cancer for outcome prediction.

Authors:  Zhibin Huang; Kevin A Yuh; Simon S Lo; John C Grecula; Steffen Sammet; Christina L Sammet; Guang Jia; Michael V Knopp; Qiang Wu; Norman J Beauchamp; William T C Yuh; Roy Wang; Nina A Mayr
Journal:  Magn Reson Imaging       Date:  2014-08-29       Impact factor: 2.546

9.  Imaging across the life span: innovations in imaging and therapy for gynecologic cancer.

Authors:  Meng Xu-Welliver; William T C Yuh; Julia R Fielding; Katarzyna J Macura; Zhibin Huang; Ahmet S Ayan; Floor J Backes; Guang Jia; Mariam Moshiri; Jun Zhang; Nina A Mayr
Journal:  Radiographics       Date:  2014 Jul-Aug       Impact factor: 5.333

10.  Are complex DCE-MRI models supported by clinical data?

Authors:  Chong Duan; Jesper F Kallehauge; G Larry Bretthorst; Kari Tanderup; Joseph J H Ackerman; Joel R Garbow
Journal:  Magn Reson Med       Date:  2016-03-04       Impact factor: 4.668

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