title: Separation of type and grade in cervical tumours using non-mono-exponential models of diffusion-weighted MRI.
abstract: OBJECTIVE
  Assessment of empirical diffusion-weighted MRI (DW-MRI) models in cervical tumours to investigate whether fitted parameters distinguish between types and grades of tumours.
  
  
  METHODS
  Forty-two patients (24 squamous cell carcinomas, 14 well/moderately differentiated, 10 poorly differentiated; 15 adenocarcinomas, 13 well/moderately differentiated, two poorly differentiated; three rare types) were imaged at 3 T using nine b-values (0 to 800 s mm-2). Mono-exponential, stretched exponential, kurtosis, statistical, and bi-exponential models were fitted. Model preference was assessed using Bayesian Information Criterion analysis. Differences in fitted parameters between tumour types/grades and correlation between fitted parameters were assessed using two-way analysis of variance and Pearson's linear correlation coefficient, respectively.
  
  
  RESULTS
  Non-mono-exponential models were preferred by 83 % of tumours with bi-exponential and stretched exponential models preferred by the largest numbers of tumours. Apparent diffusion coefficient (ADC) and diffusion coefficients from non-mono-exponential models were significantly lower in poorly differentiated tumours than well/moderately differentiated tumours. α (stretched exponential), K (kurtosis), f and D* (bi-exponential) were significantly different between tumour types. Strong correlation was observed between ADC and diffusion coefficients from other models.
  
  
  CONCLUSIONS
  Non-mono-exponential models were preferred to the mono-exponential model in DW-MRI data from cervical tumours. Parameters of non-mono-exponential models showed significant differences between types and grades of tumours.
  
  
  CONCLUSIONS
  • Non-mono-exponential DW-MRI models are preferred in the majority of cervical tumours. • Poorly differentiated cervical tumours exhibit lower diffusion coefficients than well/moderately differentiated tumours. • Non-mono-exponential model parameters α, K, f, and D* differ between tumour types. • Micro-structural features are likely to affect parameters in non-mono-exponential models differently.
authors.affiliation: MRI Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK. jessica.winfield@icr.ac.uk.|Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK. jessica.winfield@icr.ac.uk.
authors.forename: Jessica M
authors.initials: JM
authors.lastname: Winfield
authors.affiliation: Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK.
authors.forename: Matthew R
authors.initials: MR
authors.lastname: Orton
authors.affiliation: MRI Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK.|Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK.
authors.forename: David J
authors.initials: DJ
authors.lastname: Collins
authors.affiliation: Gynaecology Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK.
authors.forename: Thomas E J
authors.initials: TE
authors.lastname: Ind
authors.affiliation: Department of Histopathology, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK.
authors.forename: Ayoma
authors.initials: A
authors.lastname: Attygalle
authors.affiliation: Department of Histopathology, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK.
authors.forename: Steve
authors.initials: S
authors.lastname: Hazell
authors.affiliation: MRI Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK.|Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK.
authors.forename: Veronica A
authors.initials: VA
authors.lastname: Morgan
authors.affiliation: MRI Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK.|Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK.
authors.forename: Nandita M
authors.initials: NM
authors.lastname: deSouza
mesh_terms: D000230:Adenocarcinoma / Q000000981:diagnostic imaging*; D001499:Bayes Theorem; D002294:Carcinoma, Squamous Cell / Q000000981:diagnostic imaging* / Q000473:pathology; D002584:Cervix Uteri / Q000000981:diagnostic imaging / Q000473:pathology; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D060787:Neoplasm Grading; D011446:Prospective Studies; D002583:Uterine Cervical Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*
keywords: Analysis, regression; Apparent diffusion coefficient; Cervical cancer; Diffusion-weighted magnetic resonance imaging; Intravoxel incoherent motion (IVIM)