A Voxel-wise imaging analysis method for early evaluation of tumor treatment response_Journal of Biomedical Engineering

Authors：

CHENLin ^1,2 , XIAWei ¹ ,  GAOXin ¹

1. Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China;
2. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;

Corresponding author：

GAOXin, Email: xingaosam@yahoo.com

Keywords：

tumor treatment response early evaluation; voxel-wise image analysis method; dynamic contrast enhanced magnetic resonance imaging; imaging biomarker

DOI：

10.7507/1001-5515.20160090

Video：

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To solve the problem that the method based on tumor morphology or overall average parameters of tumor cannot conduct the early evaluation of tumor treatment response, we proposed a voxel-wise method. The voxel-wise method uses the method combining rigid and elastic registration algorithm to align the tumor area before and after treatment on the images which are acquired by the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). We calculated voxel-wise volume transport constant (K^trans) using pharmacokinetic model, and designed a threshold d to get the volume fraction of voxels which K^trans increased significantly (F₊), K^trans decreased significantly (F_-) or had no significant change (F₀). Linear regression analysis was performed to get the correlation between volume fractions and pathological tumor cell necrosis rate (TCNR). We then determined the ability of volume fractions to evaluate treatment response at early stage by receiver operating characteristic (ROC) curve analysis. We performed experiments on 10 patients with soft tissue sarcomas. The results indicated that F_- had significant negative correlation with TCNR (R²=0.832 8, P=0.0002), F₀ has significant positively correlation with TCNR (R²=0.788 4, P=0.0006). In addition, F_-(AUC=0.905,P=0.053), F₀ (AUC=0.857,P=0.087) had a good ability in early tumor treatment response evaluation. Therefore, F_- and F₀ can be used as effective imaging biomarkers for early evaluation of tumor treatment.

Citation： CHENLin, XIAWei, GAOXin. A Voxel-wise imaging analysis method for early evaluation of tumor treatment response. Journal of Biomedical Engineering, 2016, 33(3): 538-544. doi: 10.7507/1001-5515.20160090 Copy

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1. 国家卫生和计划生育委员会.2013中国卫生和计划生育统计年鉴[M].北京:中国协和医科大学出版社,2013.
2. EISENHAUER E A, THERASSE P, BOGAERTS J, et al. New response evaluation criteria in solid tumours:Revised RECIST guideline (version 1.1)[J]. Eur J Cancer, 2009, 45(2):228-247.
3. 李亚明.功能影像与肿瘤化疗疗效评价[J].中国实用内科杂志,2012,4(4):243-244.
4. 程志祥,王科明.肿瘤疗效评价研究进展[J].中国肿瘤,2009,18(7):548-553.
5. AKISIK M F, SANDRASEGARAN K, BU Guixue, et al. Pancreatic cancer:utility of dynamic contrast-enhanced MR imaging in assessment of antiangiogenic therapy[J]. Radiology, 2010, 256(2):441-449.
6. RØDAL J, RUSTEN E, SØVIK Å, et al. Functional imaging to monitor vascular and metabolic response in canine head and neck tumors during fractionated radiotherapy[J]. Acta Oncol, 2013, 52(7):1293-1299.
7. YOUNG H, BAUM R, CREMERIUS U, et al. Measurement of clinical and subclinical tumour response using[¹⁸F]-fluorodeoxyglucose and positron emission tomography:Review and 1999 EORTC recommendations[J]. Eur J Cancer, 1999, 35(13):1773-1782.
8. MEYER J M, PERLEWITZ K S, HAYDEN J B, et al. Phase Ⅰ trial of preoperative chemoradiation plus sorafenib for high-risk extremity soft tissue sarcomas with dynamic contrast-enhanced MRI correlates[J]. Clin Cancer Res, 2013, 19(24):6902-6911.
9. TROITZSCH D, HOFFMANN J, DAMMANN F, et al. Registration using three-dimensional laser surface scanning for navigation in oral and craniomaxillofacial surgery[J]. Zentralbl Chir, 2003, 128(7):551-556.
10. RUECKERT D, ALJABAR P. Nonrigid registration of medical images:theory, methods, and applications[J]. IEEE Signal Processing Magazine, 2010, 27(4):113-119.
11. GORBUNOVA V, SPORRING J, LO P, et al. Mass preserving image registration for lung CT[J]. Med Image Anal, 2012, 16(4):786-795.
12. MATTES D, HAYNOR D R, VESSELLE H, et al. PET-CT image registration in the chest using free-form deformations[J]. IEEE Trans Med Imaging, 2003, 22(1):120-128.
13. ZHU C Y, BYRD R H, LU P H, et al. Algorithm 778:L-BFGS-B:Fortran subroutines for large-scale bound-constrained optimization[J]. ACM Transactions on Mathematical Software, 1997, 23(4):550-560.
14. CHOI Y, LEE S. Injectivity conditions of 2D and 3D uniform cubic B-spline functions[J]. Graphical Models, 2000, 62(6):411-427.
15. 邓振生,张娜,孟莉,等.利用T_1加权DCE-MRI技术对脑胶质瘤分级[J].中国生物医学工程学报,2010,29(1):16-21.
16. TOFTS P S, BRIX G, BUCKLEY D L, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer:standardized quantities and symbols[J]. J Magn Reson Imaging, 1999, 10(3):223-232.
17. GALBÁN C J, CHENEVERT T L, MEYER C R, et al. The parametric response map is an imaging biomarker for early cancer treatment outcome[J]. Nat Med, 2009, 15(5):572-576.
18. TSIEN C, GALBÁN C J, CHENEVERT T L, et al. Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma[J]. J Clin Oncol, 2010, 28(13):2293-2299.
19. 刘润幸.使用SPSS作多变量观察值的ROC曲线分析[J].中国公共卫生,2003,19(9):1151-1152.
20. CAO Yue, TSIEN C I, NAGESH V, et al. Clinical investigation survival prediction in high-grade gliomas by MRI perfusion before and during early stage of RT[J]. Int J Radiat Oncol Biol Phys, 2006, 64(3):876-885.
21. CHAWLA S, KIM S, DOUGHERTY L, et al. Pretreatment diffusion-weighted and dynamic contrast-enhanced MRI for prediction of local treatment response in squamous cell carcinomas of the head and neck[J]. American Journal of Roentgenology, 2013, 200(1):35-43.

Journal of Biomedical Engineering

A Voxel-wise imaging analysis method for early evaluation of tumor treatment response

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