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

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.

[1]  Charles L. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[2]  G. Glatting,et al.  Choosing the optimal fit function: comparison of the Akaike information criterion and the F-test. , 2007, Medical physics.

[3]  Kostas Marias,et al.  Diffusion Modelling Tool (DMT) for the analysis of Diffusion Weighted Imaging (DWI) Magnetic Resonance Imaging (MRI) data , 2016, CGI.

[4]  Tian-wu Chen,et al.  Various diffusion magnetic resonance imaging techniques for pancreatic cancer. , 2015, World journal of radiology.

[5]  B. Stieltjes,et al.  Evaluation of Diffusion Kurtosis Imaging Versus Standard Diffusion Imaging for Detection and Grading of Peripheral Zone Prostate Cancer , 2015, Investigative radiology.

[6]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[7]  E. Sigmund,et al.  Assessment of hepatocellular carcinoma using apparent diffusion coefficient and diffusion kurtosis indices: preliminary experience in fresh liver explants. , 2012, Magnetic resonance imaging.

[8]  Stuart A. Taylor,et al.  Magnetic resonance imaging for the clinical management of rectal cancer patients: recommendations from the 2012 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus meeting , 2018, European Radiology.

[9]  K. Haustermans,et al.  Diffusion-Weighted MRI for Selection of Complete Responders After Chemoradiation for Locally Advanced Rectal Cancer: A Multicenter Study , 2011, Annals of Surgical Oncology.

[10]  D. Le Bihan,et al.  Quantitative Non-Gaussian Diffusion and Intravoxel Incoherent Motion Magnetic Resonance Imaging: Differentiation of Malignant and Benign Breast Lesions , 2015, Investigative radiology.

[11]  Andrej-Nikolai Spiess,et al.  An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach , 2010, BMC pharmacology.

[12]  N. Shah,et al.  Non-Gaussian Diffusion Imaging for Enhanced Contrast of Brain Tissue Affected by Ischemic Stroke , 2014, PloS one.

[13]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[14]  S. F. Carbone,et al.  Assessment of response to chemoradiation therapy in rectal cancer using MR volumetry based on diffusion-weighted data sets: a preliminary report , 2012, La radiologia medica.

[15]  R. Berendsen,et al.  Locally advanced rectal cancer: is diffusion weighted MRI helpful for the identification of complete responders (ypT0N0) after neoadjuvant chemoradiation therapy? , 2013, European Radiology.

[16]  L. Sachs Angewandte Statistik : Anwendung statistischer Methoden , 1984 .

[17]  Y. Mazaheri,et al.  Extension of the intravoxel incoherent motion model to non‐gaussian diffusion in head and neck cancer , 2012, Journal of magnetic resonance imaging : JMRI.

[18]  K. Miyazaki,et al.  Assessment of aggressiveness of rectal cancer using 3-T MRI: correlation between the apparent diffusion coefficient as a potential imaging biomarker and histologic prognostic factors , 2014, Acta radiologica.

[19]  M. Gollub,et al.  Multiparametric MRI of Rectal Cancer in the Assessment of Response to Therapy: A Systematic Review , 2014, Diseases of the colon and rectum.

[20]  C. V. D. van de Velde,et al.  A new paradigm for rectal cancer: Organ preservation: Introducing the International Watch & Wait Database (IWWD). , 2015, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.

[21]  M. Kim,et al.  Value of diffusion-weighted imaging in the detection of viable tumour after neoadjuvant chemoradiation therapy in patients with locally advanced rectal cancer: comparison with T2 weighted and PET/CT imaging. , 2012, The British journal of radiology.

[22]  D. Le Bihan,et al.  Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. , 1988, Radiology.

[23]  J. Helpern,et al.  Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[24]  S. Schoenberg,et al.  Measurement of signal‐to‐noise ratios in MR images: Influence of multichannel coils, parallel imaging, and reconstruction filters , 2007, Journal of magnetic resonance imaging : JMRI.

[25]  G. Choi,et al.  Locally advanced rectal cancer: post-chemoradiotherapy ADC histogram analysis for predicting a complete response , 2015, Acta radiologica.

[26]  E. Wagenmakers,et al.  AIC model selection using Akaike weights , 2004, Psychonomic bulletin & review.

[27]  L. Stassen,et al.  Long-term Outcome of an Organ Preservation Program After Neoadjuvant Treatment for Rectal Cancer. , 2016, Journal of the National Cancer Institute.

[28]  P. Choyke,et al.  Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. , 2009, Neoplasia.