Literature DB >> 32075610

Diffusion weighted magnetic resonance imaging (DW-MRI) as a non-invasive, tissue cellularity marker to monitor cancer treatment response.

Frederikke Petrine Fliedner1,2, Trine Bjørnbo Engel3, Henrik H El-Ali4, Anders Elias Hansen1,2,3, Andreas Kjaer5,6.   

Abstract

BACKGROUND: Diffusion weighted magnetic resonance imaging (DW-MRI) holds great potential for monitoring treatment response in cancer patients shortly after initiation of radiotherapy. It is hypothesized that a decrease in cellular density of irradiated cancerous tissue will lead to an increase in quantitative apparent diffusion coefficient (ADC) values. DW-MRI can therefore serve as a non-invasive marker of cell death and apoptosis in response to treatment. In the present study, we aimed to investigate the applicability of DW-MRI in preclinical models to monitor radiation-induced treatment response. In addition, we compared DW-MRI with ex vivo measures of cell density, cell death and apoptosis.
METHODS: DW-MRI was tested in two different syngeneic mouse models, a colorectal cancer (CT26) and a breast cancer (4 T1). ADC values were compared with quantitative determinations of apoptosis and cell death by flow cytometry. Furthermore, ADC-values were also compared to histological measurement of cell density on tumor sections.
RESULTS: We found a significant correlation between ADC-values and apoptotic state in the CT26 model (P = 0.0031). A strong correlation between the two measurements of ADC-value and apoptotic state was found in both models, which were also present when comparing ADC-values to cell densities.
CONCLUSIONS: Our findings demonstrate that DW-MRI can be used for non-invasive monitoring of radiation-induced changes in cell state during cancer therapy. ADC values reflect ex vivo cell density and correlates well with apoptotic state, and can hereby be described as a marker for the cell state after therapy and used as a non-invasive response marker.

Entities:  

Keywords:  ADC-value; Cancer treatment response; Cellular density; Diffusion weighted MRI; Preclinical

Year:  2020        PMID: 32075610     DOI: 10.1186/s12885-020-6617-x

Source DB:  PubMed          Journal:  BMC Cancer        ISSN: 1471-2407            Impact factor:   4.430


  9 in total

Review 1.  Diffusion-weighted MRI of the liver: challenges and some solutions for the quantification of apparent diffusion coefficient and intravoxel incoherent motion.

Authors:  Yi Xiang J Wang; Hua Huang; Cun-Jing Zheng; Ben-Heng Xiao; Olivier Chevallier; Wei Wang
Journal:  Am J Nucl Med Mol Imaging       Date:  2021-04-15

2.  Defining the Magnetic Resonance Features of Renal Lesions and Their Response to Everolimus in a Transgenic Mouse Model of Tuberous Sclerosis Complex.

Authors:  Shubhangi Agarwal; Emilie Decavel-Bueff; Yung-Hua Wang; Hecong Qin; Romelyn Delos Santos; Michael J Evans; Renuka Sriram
Journal:  Front Oncol       Date:  2022-06-23       Impact factor: 5.738

3.  Deep learning features encode interpretable morphologies within histological images.

Authors:  Ali Foroughi Pour; Brian S White; Jonghanne Park; Todd B Sheridan; Jeffrey H Chuang
Journal:  Sci Rep       Date:  2022-06-08       Impact factor: 4.996

4.  Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI.

Authors:  Ming Ni; Xiaoming Zhou; Jingwei Liu; Haiyang Yu; Yuanxiang Gao; Xuexi Zhang; Zhiming Li
Journal:  BMC Cancer       Date:  2020-11-09       Impact factor: 4.430

5.  Predicting pathologic response to neoadjuvant chemotherapy in patients with locally advanced breast cancer using multiparametric MRI.

Authors:  Nannan Lu; Jie Dong; Xin Fang; Lufang Wang; Wei Jia; Qiong Zhou; Lingyu Wang; Jie Wei; Yueyin Pan; Xinghua Han
Journal:  BMC Med Imaging       Date:  2021-10-23       Impact factor: 1.930

6.  Targeting Glioblastoma via Selective Alteration of Mitochondrial Redox State.

Authors:  Akira Sumiyoshi; Sayaka Shibata; Zhivko Zhelev; Thomas Miller; Dessislava Lazarova; Ichio Aoki; Takayuki Obata; Tatsuya Higashi; Rumiana Bakalova
Journal:  Cancers (Basel)       Date:  2022-01-19       Impact factor: 6.639

7.  Diagnostic value of apparent diffusion coefficient in predicting pathological T stage in patients with thymic epithelial tumor.

Authors:  Chao-Chun Chang; Chia-Ying Lin; Li-Ting Huang; Ming-Tsung Chuang; Ying-Hung Lu; Wei-Li Huang; Ying-Yuan Chen; Wu-Wei Lai; Yau-Lin Tseng; Yi-Ting Yen
Journal:  Cancer Imaging       Date:  2022-10-05       Impact factor: 5.605

8.  Relationships and Qualitative Evaluation Between Diffusion-Weighted Imaging and Pathologic Findings of Resected Lung Cancers.

Authors:  Katsuo Usuda; Shun Iwai; Aika Yamagata; Atsushi Sekimura; Nozomu Motono; Munetaka Matoba; Mariko Doai; Sohsuke Yamada; Yoshimichi Ueda; Keiya Hirata; Hidetaka Uramoto
Journal:  Cancers (Basel)       Date:  2020-05-08       Impact factor: 6.639

Review 9.  Emerging methods for prostate cancer imaging: evaluating cancer structure and metabolic alterations more clearly.

Authors:  Adam Retter; Fiona Gong; Tom Syer; Saurabh Singh; Sola Adeleke; Shonit Punwani
Journal:  Mol Oncol       Date:  2021-08-30       Impact factor: 6.603

  9 in total

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