Open Journal Systems

Dynamic Comparison of the Two Models to Enhance the Application Value of MRI in Cervical Cancer Case Classification

Xuechao Liang (The People's Hospital, Lianshan District, Huludao City, Liaoning Province,China)
Yan Jiang (Fleming Research Center of Life Sciences,Singapore)

Abstract


Objective. To explore the application of dynamic contrast-enhanced magnetic  resonance imaging (DCE-MRI) with Extended Tofts Linear(ETL) model and extended tofts (ET) model in pathological grading of cervical cancer. Method. Retrospective analysis was performed on 31 cases of patients admitted to Shaoxing People's Hospital from January 2016 to October 2019 who were pathologically confirmed to have cervical cancer. They underwent preoperative multistage pelvic DCE-MRI examination. Extended Tofts Linear and Extended Tofts hemodynamic model of cervical cancer were calculated separately, and the quantitative perfusion parameters (including transport capacity constant (Ktrans), the rate constant (Kep), blood vessels, extracellular clearance volume fraction (Ve), blood vessels, and clearance volume fraction (Vp)). According to the postoperative pathologic stage can be divided into low, medium and high differentiate cervical cancer group, the difference contrast of three groups of cervical cancer perfusion parameter were analyzed. The parameters with statistical significance were screened out, and the ROC curves for the differentiation of cervical cancer with different degrees were drawn. Result. The diagnostic efficacy of the quantitative parameters obtained by the two models in the pathological grading of cervical cancer was compared. The Ktrans values of the Exte Nded Tofts Linear model (highly differentiated (0.518±0.180) min-1, poorly differentiated (1.032±0.408) min-1) and the Extended Tofts model (highly differentiated (0.525±0.283) min-1, poorly differentiated (1.487±0.991) min-1) were significantly different between the highly differentiated and poorly differentiated groups (P < 0.05). There were no significant differences in Kep, Ve and Vp between high and low differentiated cervical cancer groups (all P > 0.05). There were no significant differences in Ktrans, Kep, Ve and Vp between the two models (all P > 0.05). The AUC of Ktrans in the Exte Nded Tofts Linear model was greater than that of Ktrans in the Extended Tofts model for the identification of highly and poorly differentiated cervical cancers. Joint diagnosis: Logistic regression analysis was used to calculate the joint diagnostic probability values of Ktrans of the Extended Tofts Linear and Extended Tofts models, and the results showed that the AUC of the two models was greater than that of the single model. Conclusion. The Ktrans obtained by the Extended Tofts Linear model and the Extended Tofts DCE-MRI model had certain value for the pathological grading of high and low differentiated cervical cancer, and the overall efficacy of the former was better than the latter. Meanwhile, the differential value of the two models combined for the pathological grading of cervical cancer was better than that of the single model perfusion parameter, and the differential diagnosis sensitivity and specificity of the two models combined with Ktrans were the highest.


Keywords


Cervical cancer; Pathology; Dynamic contrast- enhanced magnetic resonance imaging; Model

Full Text:

PDF

References


Rebecca L, Siegel, Kimberly D, et al. Cancer s ta tis tic[J]. CA Can-

cer J Clin, 2018, 68:7- 30. DOI:10.3322/caac.21442.

Lee EY, Hui ES, Chan KK, et al. Relationship between intravoxel incoherent motion diffusion- weighted MRI and dynamic con- tras t- enhanced MRI in tis sue perfusion of cervical cancers [J]. Journal of Magnetic Resonance Imaging, 2015, 42 (2):454- 459. DOI:10.1002/jmri.24808.

Andersen EK, Hole KH, Lund KV, et al. Pharmacokinetic parame- ters derived from dynamic contrast enhanced MRI of cervical cancers predict chemoradiotherapy outcome[J]. Radiotherapy and Oncology, 2013, 107(1):117- 122. DOI:10.1016/j.radonc.2012.11. 007.

Park JJ, Kim CK, Park SY, et al. Assessment of early response to concurrent chemoradiotherapy in cervical cancer: value of diffu- s ion- weighted and dynamic contrast- enhanced MR imaging [J]. Magnetic Resonance Imaging, 2014, 32(8):993- 1000. DOI:10.1016/ j.mri.2014.05.009.

Wang H, Su Z, Xiao X, et al. Dynamic Contrast- enhanced MR Imaging in Renal Cell Carcinoma: Reproducibility of Histogram Analysis on Pharmacokinetic Parameters [J ]. Scientific Reports , 2016, 6:29146. DOI:10.1038/srep 29146.

Duan C, Kallehauge JF, Bretthorst GL, et al. Are Complex DCE- MRI models supported by clinical data?[J]. Magnetic Resonance in Medicine, 2017,77(3):1329- 1339. DOI:10.1002/mrm.26189.

Yankeelov TE, Cron GO, Addison CL, et al. Comparison of a Ref- erence Region Model With Direct Measurement of an AIF in the Analysis of DCE- MRI Data [J ]. Magnetic Resonance in Medicine, 2007, 57(2):353- 361. DOI:10.1002/mrm.21131.

Li Li, Zhao ZH, Yang JF, et al. The value of dynamic contrast-enhanced MRI quantitative perfusion histogram parameters in the diagnosis of rich uterine fibroids [J]. The Chinese journal of radiology, 2018, 52 (11) : 852-857. The DOI: 10.3760 / cma. J.i SSN. 1005-1201.2018.11.008.

Zheng J, Zhao Z H, Yang J F, et al. Application of dynamic enhanced quantitative perfusion parameters in the pathological classification of uterine fibroids [J]. Chin J Med, 2017, 97(15): 1155-1159. DOI: 10.3760/ cma.j.issn.0376-2491.2017.15.009.

Ctrdenas- Redriguez J, Howison CM, Pagel MD. A linear algo- rithm of the reference region model for DCE- MRI is robust and relaxes requirements for temporal resolution[J]. Magn Reson Imaging, 2013, 31(4):497- 507. DOI:10.1016/j.mri.2012.10.008.

Ewing JR, Bagher- Ebadian H. Model selection in measures of vascular parameters using dynamic contrast enhanced MRI: Experimental and clinical applications [J]. NMR Biomed,2013,26 (8):1028- 1041. DOI:10.1002/nbm.2996.

Sourbron SP, Buckley DL. On the scope and interpretation of the Tofts models for DCE- MRI[J ]. Magn Reson Med , 2011, 66 (3): 735- 745. DOI:10.1002/mrm.22861.

Jialong-Wei, Niu Lei, Ma Wen-Shuai, et al. Extended Tofts Linear model of hemodynamics in quantitative analysis of cerebral glioma permeability by DCE-MRI [J]. Magnetic resonance imaging, 2015, 8 (6): 571-574. DOI:10.3969/ j.issn.1674-8034.2015.08.003. DOI:10.3969/ j.issn.1674-8034.2015.08.003

Meng H, Qin H Y. Research progress of MR imaging in cervical cancer [J]. Journal of Clinical Radiology, 2017, 36(6): 906-909. DOI:CNKI:SUN: lcfs.0.2017-06-039.

Gordon Y, Partovi S, Müller- Eschner M, et al. Dynamic con- tras t- enhanced magnetic resonance imaging: fundamentals and application to the evaluation of the peripheral perfusion[J]. Cardiovasc Diagn Ther, 2014, 4(2):147- 164. DOI:10.3978/j.issn. 2223- 3652.2014.03.01.

Wang Haiyi, Ye Huyi, Ma Lin. Quantitative MR dynamic

contrast-enhanced imaging mechanism and its application

value in oncology [J]. The Chinese journal of radiology,

, 48 (3) : 261-264. The DOI: 10.3760 / cma. J.i SSN.

-1201.2014.03.028.

Jae- Hun Kim, Chan Kyo Kim. Dynamic contrast- enhanced 3- T MR imaging in cervical cancer before and

after concurrent chemoradiotherapy[J]. Eur Radiol, 2012,

(11):2533- 2539. DOI: 10.1007/s 00330- 012- 2504- 4.

Jesper FK, Kari T. Tracer kinetic model selection for dynamic contrast- enhanced magnetic resonance imaging of

locally ad- vanced cervical cancer[J]. Acta Oncol, 2014,

(8):1064- 1072. DOI:10.3109/0284186X.2014.937879.

Tofts PS. Modeling tracer kinetics in dynamic Gd- DTPA

MR im- aging[J]. J Magn Reson Imaging, 1997, 7(1):91-

DOI:10.1002/ jmri.1880070113

Ewing JR, Bagher- Ebadian H. Model selection in measures of vascular parameters using dynamic contrastenhanced MRI: experimental and clinical applications[J].

NMR Biomed, 2013, 26 (8):1028- 1041. DOI:10.1002/

nbm.2996.



DOI: http://dx.doi.org/10.30564/amor.v6i2.267

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Xuechao Liang

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.