Literature DB >> 23073840

Analysis of parametric histogram from dynamic contrast-enhanced MRI: application in evaluating brain tumor response to radiotherapy.

Shin-Lei Peng1, Chih-Feng Chen, Ho-Ling Liu, Chun-Chung Lui, Yu-Jie Huang, Tsung-Han Lee, Chiung-Chih Chang, Fu-Nien Wang.   

Abstract

Dynamic contrast-enhanced MRI (DCE MRI) has been used to study tumor response to treatment for many years. In this study, the modified full width at half-maximum (mFWHM), calculated from the wash-in slope histogram, is proposed as a parameter for the evaluation of changes in tumor heterogeneity which respond to radiotherapy. Twenty-five patients with brain tumors were evaluated and divided into the nonresponder group (n = 11) and the responder group (n = 14) according to the Response Evaluation Criteria in Solid Tumors (RECIST). All selected tumors were evaluated by mFWHM ratios of post- to pre-therapy (the ratio was defined as the therapeutic mFWHM ratio, TMR). The changes in kurtosis of the histograms and the averaged K(trans) within a tumor were also calculated for comparison. The receiver operating characteristic analysis and Kaplan-Meier curves were used to examine the diagnosis ability. The TMR values were significantly higher in nonresponders than in responders (p < 0.001). When compared with the other two parameters, the proposed method also demonstrated better sensitivity and specificity. When adopting the TMR for the estimation of prognosis after therapy, there was a significant difference between the population survival curves. In conclusion, the derived mFWHM reflects tumor heterogeneity, and the ability to depict patient survival probability from TMR corresponds well with that from RECIST. The results reveal that, in brain tumors, progression may be exhibited not only by tumor size, but also by tumor heterogeneity.
Copyright © 2012 John Wiley & Sons, Ltd.

Entities:  

Keywords:  brain tumor; dynamic contrast-enhanced (DCE) MRI; radiotherapy; treatment response; tumor heterogeneity; wash-in slope

Mesh:

Substances:

Year:  2012        PMID: 23073840     DOI: 10.1002/nbm.2882

Source DB:  PubMed          Journal:  NMR Biomed        ISSN: 0952-3480            Impact factor:   4.044


  20 in total

1.  Assessment of irradiated brain metastases using dynamic contrast-enhanced magnetic resonance imaging.

Authors:  Daniela B Almeida-Freitas; Marco C Pinho; Maria C G Otaduy; Henrique F Braga; Daniel Meira-Freitas; Claudia da Costa Leite
Journal:  Neuroradiology       Date:  2014-03-21       Impact factor: 2.804

2.  Histogram analysis of DCE-MRI for chemoradiotherapy response evaluation in locally advanced esophageal squamous cell carcinoma.

Authors:  Na-Na Sun; Xiao-Lin Ge; Xi-Sheng Liu; Lu-Lu Xu
Journal:  Radiol Med       Date:  2019-10-11       Impact factor: 3.469

3.  Dynamic contrast-enhanced magnetic resonance imaging for evaluating early response to radiosurgery in patients with vestibular schwannoma.

Authors:  Halil Özer; Merve Yazol; Nesrin Erdoğan; Ömer Hakan Emmez; Gökhan Kurt; Ali Yusuf Öner
Journal:  Jpn J Radiol       Date:  2022-01-17       Impact factor: 2.374

Review 4.  Invited review--neuroimaging response assessment criteria for brain tumors in veterinary patients.

Authors:  John H Rossmeisl; Paulo A Garcia; Gregory B Daniel; John Daniel Bourland; Waldemar Debinski; Nikolaos Dervisis; Shawna Klahn
Journal:  Vet Radiol Ultrasound       Date:  2013-11-13       Impact factor: 1.363

5.  Tumor heterogeneity estimation for radiomics in cancer.

Authors:  Ani Eloyan; Mun Sang Yue; Davit Khachatryan
Journal:  Stat Med       Date:  2020-09-23       Impact factor: 2.373

Review 6.  Applications and limitations of radiomics.

Authors:  Stephen S F Yip; Hugo J W L Aerts
Journal:  Phys Med Biol       Date:  2016-06-08       Impact factor: 3.609

Review 7.  DCE-MRI in hepatocellular carcinoma-clinical and therapeutic image biomarker.

Authors:  Bang-Bin Chen; Tiffany Ting-Fang Shih
Journal:  World J Gastroenterol       Date:  2014-03-28       Impact factor: 5.742

8.  Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network.

Authors:  Yannick Bliesener; Jay Acharya; Krishna S Nayak
Journal:  IEEE Trans Med Imaging       Date:  2019-11-26       Impact factor: 10.048

9.  Differentiation of progressive disease from pseudoprogression using MRI histogram analysis in patients with treated glioblastoma.

Authors:  Mustafa Yildirim; Murat Baykara
Journal:  Acta Neurol Belg       Date:  2021-02-08       Impact factor: 2.396

10.  Machine learning for the prediction of pathologic pneumatosis intestinalis.

Authors:  Kadie Clancy; Esmaeel Reza Dadashzadeh; Robert Handzel; Caroline Rieser; J B Moses; Lauren Rosenblum; Shandong Wu
Journal:  Surgery       Date:  2021-04-27       Impact factor: 4.348

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