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Cancer Imaging
Published in: Cancer Imaging 1/2024

Open Access 01-12-2024 | Pancreatic Cancer | Research article

Value of diffusion kurtosis MR imaging and conventional diffusion weighed imaging for evaluating response to first-line chemotherapy in unresectable pancreatic cancer

Authors: Zehua Zhang, Yuqin Zhang, Feixiang Hu, Tiansong Xie, Wei Liu, Huijing Xiang, Xiangxiang Li, Lei Chen, Zhengrong Zhou

Published in: Cancer Imaging | Issue 1/2024

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Abstract

Objective

To investigate the diagnostic value of diffusion kurtosis magnetic resonance imaging (DKI) and conventional diffusion-weighted imaging (DWI) for evaluating the response to first-line chemotherapy in unresectable pancreatic cancer.

Materials and methods

We retrospectively analyzed 21 patients with clinically and pathologically confirmed unresected pancreatic cancer who received palliative chemotherapy. Three-tesla MRI examinations containing DWI sequences with b values of 0, 100, 700, 1400, and 2100 s/mm2 were performed before and after chemotherapy. Parameters included the apparent diffusion coefficient (ADC), mean diffusion coefficient (MD), and mean diffusional kurtosis (MK). The performances of the DWI and DKI parameters in distinguishing the response to chemotherapy were evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Overall survival (OS) was calculated from the date of first treatment to the date of death or the latest follow-up date.

Results

The ADCchange and MDchange were significantly higher in the responding group (PR group) than in the nonresponding group (non-PR group) (ADCchange: 0.21 ± 0.05 vs. 0.11 ± 0.09, P = 0.02; MDchange: 0.37 ± 0.24 vs. 0.10 ± 0.12, P = 0.002). No statistical significance was shown when comparing ADCpre, ADCpost, MKpre, MKpost, MKchange, MDpre, and MDpost between the PR and non-PR groups. The ROC curve analysis indicated that MDchange (AUC = 0.898, cutoff value = 0.7143) performed better than ADCchange (AUC = 0.806, cutoff value = 0.1369) in predicting the response to chemotherapy.

Conclusion

The ADCchange and MDchange demonstrated strong potential for evaluating the response to chemotherapy in unresectable pancreatic cancer. The MDchange showed higher specificity in the classification of PR and non-PR than the ADCchange. Other parameters, including ADCpre, ADCpost, MKpre, MKpost, MKchange, MDpre, and MDpost, are not suitable for response evaluation. The combined model SUMchange demonstrated superior performance compared to the individual DWI and DKI models. Further experiments are needed to evaluate the potential of DWI and DKI parameters in predicting the prognosis of patients with unresectable pancreatic cancer.
Literature
1.
2.
Brynolfsson P, et al. Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters. Sci Rep. 2017;7(1):4041.ADSCrossRefPubMedPubMedCentral
3.
Wu EX, Cheung MM. MR diffusion kurtosis imaging for neural tissue characterization. NMR Biomed. 2010;23(7):836–48.CrossRefPubMed
4.
5.
Jensen JH, et al. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med. 2005;53(6):1432–40.CrossRefPubMed
6.
Poot DH, et al. Optimal experimental design for diffusion kurtosis imaging. IEEE Trans Med Imaging. 2010;29(3):819–29.CrossRefPubMed
7.
Hui ES, et al. Towards better MR characterization of neural tissues using directional diffusion kurtosis analysis. NeuroImage. 2008;42(1):122–34.CrossRefPubMed
8.
Huang Y, et al. MRI quantification of non-gaussian water diffusion in normal human kidney: a diffusional kurtosis imaging study. NMR Biomed. 2015;28(2):154–61.CrossRefPubMed
9.
Sun K, et al. Breast Cancer: Diffusion Kurtosis MR Imaging-Diagnostic accuracy and correlation with clinical-pathologic factors. Radiology. 2015;277(1):46–55.CrossRefPubMed
10.
Suo S, et al. Non-gaussian water diffusion kurtosis imaging of prostate cancer. Magn Reson Imaging. 2014;32(5):421–7.CrossRefPubMed
11.
Nogueira L, et al. Application of the diffusion kurtosis model for the study of breast lesions. Eur Radiol. 2014;24(6):1197–203.CrossRefPubMed
12.
Rosenkrantz AB, et al. Assessment of hepatocellular carcinoma using apparent diffusion coefficient and diffusion kurtosis indices: preliminary experience in fresh liver explants. Magn Reson Imaging. 2012;30(10):1534–40.CrossRefPubMed
13.
Granata V, et al. Diffusion kurtosis imaging and conventional diffusion weighted imaging to assess electrochemotherapy response in locally advanced pancreatic cancer. Radiol Oncol. 2019;53(1):15–24.CrossRefPubMedPubMedCentral
14.
Ke MJ, Ji LD, Li YX. Bioinformatics analysis combined with experiments to explore potential prognostic factors for pancreatic cancer. Cancer Cell Int. 2020;20:382.CrossRefPubMedPubMedCentral
15.
Zhou L et al. YAP Inhibition by Nuciferine via AMPK-Mediated downregulation of HMGCR sensitizes pancreatic Cancer cells to Gemcitabine. Biomolecules, 2019. 9(10).
16.
Tempero MA, Adenocarcinoma P, et al. Version 2.2017, NCCN Clinical Practice guidelines in Oncology. J Natl Compr Canc Netw. 2017;15(8):1028–61.CrossRefPubMed
17.
Von Hoff DD, et al. Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine. N Engl J Med. 2013;369(18):1691–703.CrossRef
18.
Conroy T, et al. FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer. N Engl J Med. 2011;364(19):1817–25.CrossRefPubMed
19.
Therasse P, et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst. 2000;92(3):205–16.CrossRefPubMed
20.
Le Bihan D, et al. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology. 1986;161(2):401–7.CrossRefPubMed
21.
Le Bihan D, et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988;168(2):497–505.CrossRefPubMed
22.
Nishiofuku H, et al. Increased tumour ADC value during chemotherapy predicts improved survival in unresectable pancreatic cancer. Eur Radiol. 2016;26(6):1835–42.CrossRefPubMed
23.
Mayer P et al. Diffusion Kurtosis Imaging-A Superior Approach to assess tumor-stroma ratio in pancreatic ductal adenocarcinoma. Cancers (Basel), 2020. 12(6).
24.
Granata V, et al. Magnetic resonance imaging in the assessment of pancreatic cancer with quantitative parameter extraction by means of dynamic contrast-enhanced magnetic resonance imaging, diffusion kurtosis imaging and intravoxel incoherent motion diffusion-weighted imaging. Th Adv Gastroenterol. 2020;13:1756284819885052.
25.
Chen BB, et al. Multiparametric PET/MR imaging biomarkers are associated with overall survival in patients with pancreatic cancer. Eur J Nucl Med Mol Imaging. 2018;45(7):1205–17.CrossRefPubMed
26.
Niwa T, et al. Advanced pancreatic cancer: the use of the apparent diffusion coefficient to predict response to chemotherapy. Br J Radiol. 2009;82(973):28–34.CrossRefPubMed
27.
Fujima N, et al. Prediction of the treatment outcome using intravoxel incoherent motion and diffusional kurtosis imaging in nasal or sinonasal squamous cell carcinoma patients. Eur Radiol. 2017;27(3):956–65.CrossRefPubMed
28.
Zhao DW, et al. Comparison of the pre-treatment functional MRI metrics’ efficacy in predicting Locoregionally advanced nasopharyngeal carcinoma response to induction chemotherapy. Cancer Imaging. 2021;21(1):59.CrossRefPubMedPubMedCentral
29.
Wang P, et al. A study on diffusion and kurtosis features of cervical cancer based on non-gaussian diffusion weighted model. Magn Reson Imaging. 2018;47:60–6.CrossRefPubMed
30.
Wu R, et al. Assessment of chemotherapy response in non-hodgkin lymphoma involving the neck utilizing diffusion kurtosis imaging: a preliminary study. Diagn Interv Radiol. 2017;23(3):245–9.CrossRefPubMedPubMedCentral
Metadata
Title
Value of diffusion kurtosis MR imaging and conventional diffusion weighed imaging for evaluating response to first-line chemotherapy in unresectable pancreatic cancer
Authors
Zehua Zhang
Yuqin Zhang
Feixiang Hu
Tiansong Xie
Wei Liu
Huijing Xiang
Xiangxiang Li
Lei Chen
Zhengrong Zhou
Publication date
01-12-2024
Publisher
BioMed Central
Published in
Cancer Imaging / Issue 1/2024
Electronic ISSN: 1470-7330
DOI
https://doi.org/10.1186/s40644-024-00674-y

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