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Correction: Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models

April 2018
PLOS ONE 13(4):e0196262

DOI:10.1371/journal.pone.0196262

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CC BY 4.0

Authors:

Georgios C. Manikis

Foundation for Research and Technology - Hellas

Kostas Marias

Foundation for Research and Technology - Hellas

Katerina Nikiforaki

Foundation for Research and Technology - Hellas

Show all 8 authorsHide

[This corrects the article DOI: 10.1371/journal.pone.0184197.].

Available via license: CC BY 4.0

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CORRECTION

Correction: Diffusion weighted imaging in

patients with rectal cancer: Comparison

between Gaussian and non-Gaussian models

Georgios C. Manikis, Kostas Marias, Doenja M. J. Lambregts, Katerina Nikiforaki, Miriam

M. van Heeswijk, Frans C. H. Bakers, Regina G. H. Beets-Tan, Nikolaos Papanikolaou

One affiliation for the second author is not indicated. Kostas Marias is also affiliated with:

Department of Informatics Engineering, Technological Educational Institute of Crete, Herak-

lion, Greece.

Reference

1. Manikis GC, Marias K, Lambregts DMJ, Nikiforaki K, van Heeswijk MM, Bakers FCH, et al. (2017) Diffu-

sion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian

models. PLoS ONE 12(9): e0184197. https://doi.org/10.1371/journal.pone.0184197 PMID: 28863161

PLOS ONE | https://doi.org/10.1371/journal.pone.0196262 April 17, 2018 1 / 1

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OPEN ACCESS

Citation: Manikis GC, Marias K, Lambregts DMJ,

Nikiforaki K, van Heeswijk MM, Bakers FCH, et al.

(2018) Correction: Diffusion weighted imaging in

patients with rectal cancer: Comparison between

Gaussian and non-Gaussian models. PLoS ONE 13

(4): e0196262. https://doi.org/10.1371/journal.

pone.0196262

Published: April 17, 2018

access article distributed under the terms of the

Creative Commons Attribution License, which

permits unrestricted use, distribution, and

reproduction in any medium, provided the original

author and source are credited.

ResearchGate has not been able to resolve any citations for this publication.

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models

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PLOS ONE

Purpose: The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer. Material and methods: Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio. Results: All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area. Conclusion: No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior.

Diffusion Modelling Tool (DMT) for the analysis of Diffusion Weighted Imaging (DWI) Magnetic Resonance Imaging (MRI) data

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This paper presents a graphical user interface (GUI) for empowering users to read, analyze, visualize and quantify the diffusion weighted magnetic resonance imaging (DWI-MRI) data into diffusion related imaging biomarkers. The process of random motion of water molecules in human tissues called diffusivity is a significant physical process that can give an insight in the complexity of tissue microarchitecture. Several mathematical models have been proposed for associating diffusivity with the DWI-MRI data, aiming to provide biomarkers for quantifying this physical process. DMT follows a pipeline workflow comprising: a) reading and preparing the DWI-MRI data for analysis, b) measuring various DWI derived biomarkers from several mathematical models, c) assessing qualitatively the derived biomarkers by visually depicting their generated parametric maps, d) statistically measuring the accuracy of the models, and e) performing histogram analysis to the biomarkers and exporting the results into images and tables.

Various diffusion magnetic resonance imaging techniques for pancreatic cancer

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Pancreatic cancer is one of the most common malignant tumors and remains a treatment-refractory cancer with a poor prognosis. Currently, the diagnosis of pancreatic neoplasm depends mainly on imaging and which methods are conducive to detecting small lesions. Compared to the other techniques, magnetic resonance imaging (MRI) has irreplaceable advantages and can provide valuable information unattainable with other noninvasive or minimally invasive imaging techniques. Advances in MR hardware and pulse sequence design have particularly improved the quality and robustness of MRI of the pancreas. Diffusion MR imaging serves as one of the common functional MRI techniques and is the only technique that can be used to reflect the diffusion movement of water molecules in vivo. It is generally known that diffusion properties depend on the characterization of intrinsic features of tissue microdynamics and microstructure. With the improvement of the diffusion models, diffusion MR imaging techniques are increasingly varied, from the simplest and most commonly used technique to the more complex. In this review, the various diffusion MRI techniques for pancreatic cancer are discussed, including conventional diffusion weighted imaging (DWI), multi-b DWI based on intra-voxel incoherent motion theory, diffusion tensor imaging and diffusion kurtosis imaging. The principles, main parameters, advantages and limitations of these techniques, as well as future directions for pancreatic diffusion imaging are also discussed.

Diffusion-Weighted Magnetic Resonance Imaging as a Cancer Biomarker: Consensus and Recommendations

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On May 3, 2008, a National Cancer Institute (NCI)-sponsored open consensus conference was held in Toronto, Ontario, Canada, during the 2008 International Society for Magnetic Resonance in Medicine Meeting. Approximately 100 experts and stakeholders summarized the current understanding of diffusion-weighted magnetic resonance imaging (DW-MRI) and reached consensus on the use of DW-MRI as a cancer imaging biomarker. DW-MRI should be tested as an imaging biomarker in the context of well-defined clinical trials, by adding DW-MRI to existing NCI-sponsored trials, particularly those with tissue sampling or survival indicators. Where possible, DW-MRI measurements should be compared with histologic indices including cellularity and tissue response. There is a need for tissue equivalent diffusivity phantoms; meanwhile, simple fluid-filled phantoms should be used. Monoexponential assessments of apparent diffusion coefficient values should use two b values (>100 and between 500 and 1000 mm2/sec depending on the application). Free breathing with multiple acquisitions is superior to complex gating techniques. Baseline patient reproducibility studies should be part of study designs. Both region of interest and histogram analysis of apparent diffusion coefficient measurements should be obtained. Standards for measurement, analysis, and display are needed. Annotated data from validation studies (along with outcome measures) should be made publicly available. Magnetic resonance imaging vendors should be engaged in this process. The NCI should establish a task force of experts (physicists, radiologists, and oncologists) to plan, organize technical aspects, and conduct pilot trials. The American College of Radiology Imaging Network infrastructure may be suitable for these purposes. There is an extraordinary opportunity for DW-MRI to evolve into a clinically valuable imaging tool, potentially important for drug development.

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Quantitative Non-Gaussian Diffusion and Intravoxel Incoherent Motion Magnetic Resonance Imaging Differentiation of Malignant and Benign Breast Lesions

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Objectives: The purpose of this study was to explore the potential of non-Gaussian diffusion and perfusion magnetic resonance imaging (MRI) using intravoxel incoherent motion (IVIM) MRI for the diagnosis of breast lesions. Materials and Methods: This study included 26 women with breast lesions. Diffusion-weighted images were acquired using 16 b values up to 2500 s/mm(2) and analyzed using a kurtosis diffusion model (apparent diffusion coefficient [ADC(0)] and kurtosis [K]) for the diffusion component and IVIM model (perfusion fraction [fIVIM] and pseudodiffusion coefficient [D*]) for the perfusion component. Diagnostic performance of diffusion and perfusion parameters was evaluated from receiver operating characteristic analyses. Results: The ADC(0) in malignant lesions was significantly lower than that in benign lesions and normal tissue (P < 0.001, P < 0.001), whereas K was significantly higher (P < 0.05, P < 0.001), as well as fIVIM (P < 0.05, P < 0.01). No significant difference was found in D*. The receiver operating characteristic analysis gave high area under the curve values for ADC(0), K, and fIVIM for distinguishing malignant from benign lesions (0.99, 0.85, and 0.82, respectively). The ADC(0) allowed benign tumors to be identified with 100% negative predictive value and malignant tumors with 100% sensitivity. The malignant/benign diagnosis thresholds were 1.4 +/- 10(-3) mm(2)/s as well as 0.6 and 7%, respectively, for ADC(0), K, and fIVIM. Conclusions: With a proper methodological framework, IVIM MRI can provide valuable information on tissue structure and microvasculature beneficial for the diagnosis of breast cancer lesions.

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