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Multivariate modelling of prostate cancer combining magnetic resonance derived T2, diffusion, dynamic contrast-enhanced and spectroscopic parameters

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

The objectives are determine the optimal combination of MR parameters for discriminating tumour within the prostate using linear discriminant analysis (LDA) and to compare model accuracy with that of an experienced radiologist.

Methods

Multiparameter MRIs in 24 patients before prostatectomy were acquired. Tumour outlines from whole-mount histology, T2-defined peripheral zone (PZ), and central gland (CG) were superimposed onto slice-matched parametric maps. T2, Apparent Diffusion Coefficient, initial area under the gadolinium curve, vascular parameters (Ktrans,Kep,Ve), and (choline+polyamines+creatine)/citrate were compared between tumour and non-tumour tissues. Receiver operating characteristic (ROC) curves determined sensitivity and specificity at spectroscopic voxel resolution and per lesion, and LDA determined the optimal multiparametric model for identifying tumours. Accuracy was compared with an expert observer.

Results

Tumours were significantly different from PZ and CG for all parameters (all p < 0.001). Area under the ROC curve for discriminating tumour from non-tumour was significantly greater (p < 0.001) for the multiparametric model than for individual parameters; at 90 % specificity, sensitivity was 41 % (MRSI voxel resolution) and 59 % per lesion. At this specificity, an expert observer achieved 28 % and 49 % sensitivity, respectively.

Conclusion

The model was more accurate when parameters from all techniques were included and performed better than an expert observer evaluating these data.

Key Points

• The combined model increases diagnostic accuracy in prostate cancer compared with individual parameters

• The optimal combined model includes parameters from diffusion, spectroscopy, perfusion, and anatominal MRI

• The computed model improves tumour detection compared to an expert viewing parametric maps

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Abbreviations

ADC:

Apparent Diffusion Coefficient

CG:

Central Gland

DCE-MRI:

Dynamic Contrast-Enhanced MRI

DWI:

Diffusion Weighted Imaging

PZ:

Peripheral Zone

T2W:

T2-weighted

MRSI:

Magnetic Resonance Spectroscopic Imaging

LDA:

Linear Discriminant Analysis

IAUGC:

Initial Area Under Gadolinium Curve

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Acknowledgments

The scientific guarantor of this publication is Prof Nandita deSouza. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. This study has received funding for the CRUK & EPSRC Cancer Imaging Centre in association with MRC and Dept of Health C1060/A10334 & NHS funding to the NIHR Biomedical Research Centre and the Clinical Research Facility in Imaging. Sophie Riches was funded by a Personal Award Scheme Researcher Developer Award from the National Institute for Health Research. Scott Morgan was funded by a research fellowship from the Canadian Association of Radiation Oncology and Elekta AB. One of the authors has significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Methodology: Prospective, diagnostic or prognostic study, performed at one institution.

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Authors and Affiliations

  1. CRUK & EPSRC Cancer Imaging Centre, Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Sutton, Surrey, UK

    S. F. Riches, G. S. Payne, V. A. Morgan & N. M. deSouza

  2. Department of Urology and Department of Academic Radiotherapy, Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Sutton, Surrey, UK

    D. Dearnaley

  3. Division of Radiation Oncology, The Ottawa Hospital Cancer Centre and the University of Ottawa, Ottawa, Ontario, Canada

    S. Morgan

  4. Section of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, Surrey, UK

    M. Partridge

  5. The Gray Institute for Radiation Oncology and Biology, University of Oxford, Oxford, UK

    M. Partridge

  6. Department of Histopathology, Royal Marsden NHS Foundation Trust Chelsea, London, UK

    N. Livni

  7. Department of Urology, Royal Marsden NHS Foundation Trust Chelsea, London, UK

    C. Ogden

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  1. S. F. Riches
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  2. G. S. Payne
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  3. V. A. Morgan
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  5. S. Morgan
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  9. N. M. deSouza
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Corresponding author

Correspondence to S. F. Riches.

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Riches, S.F., Payne, G.S., Morgan, V.A. et al. Multivariate modelling of prostate cancer combining magnetic resonance derived T2, diffusion, dynamic contrast-enhanced and spectroscopic parameters. Eur Radiol 25, 1247–1256 (2015). https://doi.org/10.1007/s00330-014-3479-0

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  • DOI: https://doi.org/10.1007/s00330-014-3479-0

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