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T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results

  • Magnetic Resonance
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Abstract

Purpose

To evaluate the diagnostic relevance of T2-weighted (T2W) MRI-derived textural features relative to quantitative physiological parameters derived from diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI in Gleason score (GS) 3+4 and 4+3 prostate cancers.

Materials and Methods

3T multiparametric-MRI was performed on 23 prostate cancer patients prior to prostatectomy. Textural features [angular second moment (ASM), contrast, correlation, entropy], apparent diffusion coefficient (ADC), and DCE pharmacokinetic parameters (Ktrans and Ve) were calculated from index tumours delineated on the T2W, DW, and DCE images, respectively. The association between the textural features and prostatectomy GS and the MRI-derived parameters, and the utility of the parameters in differentiating between GS 3+4 and 4+3 prostate cancers were assessed statistically.

Results

ASM and entropy correlated significantly (p < 0.05) with both GS and median ADC. Contrast correlated moderately with median ADC. The textural features correlated insignificantly with Ktrans and Ve. GS 4+3 cancers had significantly lower ASM and higher entropy than 3+4 cancers, but insignificant differences in median ADC, Ktrans, and Ve. The combined texture-MRI parameters yielded higher classification accuracy (91%) than the individual parameter sets.

Conclusion

T2W MRI-derived textural features could serve as potential diagnostic markers, sensitive to the pathological differences in prostate cancers.

Key Points

• T2W MRI-derived textural features correlate significantly with Gleason score and ADC.

• T2W MRI-derived textural features differentiate Gleason score 3+4 from 4+3 cancers.

• T2W image textural features could augment tumour characterization.

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Acknowledgments

The research on which this study was based is funded by The Norwegian Cancer Society.

Gleason grading of the histopathology specimen was performed by Trond Viset, a senior pathologist at St. Olavs University Hospital, Trondheim. The scientific guarantor of this publication is Tone F. Bathen (tone.f.bathen@ntnu.no). 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. No complex statistical methods were necessary for this paper. Institutional Review Board approval was obtained from St. Olavs University Hospital, Trondheim, and the Regional Committee for Medical and Health Research Ethics, Central Norway. Written informed consent was obtained from all subjects (patients) in this study. Approval from the institutional animal care committee was not required because this study is on humans. The study subjects or cohorts have not been previously reported. Methodology: Retrospective, diagnostic or prognostic study, performed at one institution.

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Authors and Affiliations

  1. Department of Circulation and Medical Imaging, Faculty of Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway

    Gabriel Nketiah, Mattijs Elschot, Eugene Kim, Jose R. Teruel, Tone F. Bathen & Kirsten M. Selnæs

  2. Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands

    Tom W. Scheenen

  3. St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

    Tone F. Bathen & Kirsten M. Selnæs

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  1. Gabriel Nketiah
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Correspondence to Gabriel Nketiah.

Appendix

Appendix

Notation

G is the number of distinct levels in the histogram equalized image; \( N\left(i,j\right) \) is the \( \left(i,j\right) \) th entry in a normalized spatial GLCM; and \( {p}_x(i) \) is the i th entry in the marginal-probability matrix obtained by summing the rows of \( p\left(i,j\right)={\sum}_jp\left(i,j\right) \).

$$ {p}_{x+y}(n)={\displaystyle \sum_i}{\displaystyle \sum_{\begin{array}{c}\hfill j\hfill \\ {}\hfill i+j=n\hfill \end{array}}}N\left(i,j\right),\ n=2,3,\dots, 2G $$
$$ {p}_{x-y}(n)={\displaystyle \sum_i}{\displaystyle \sum_{\begin{array}{c}\hfill j\hfill \\ {}\hfill \left|i-j\right|=n\hfill \end{array}}}N\left(i,j\right),\kern0.5em n=0,1,\dots, G-1 $$

Textural Features

$$ Angular\ Second\ Moment={\displaystyle \sum_i}{\displaystyle \sum_j}N{\left(i,j\right)}^2 $$
$$ Contrast={\displaystyle \sum_{n=0}^{G-1}}{n}^2{p}_{x-y}(n) $$
$$ Correlation=\frac{{\displaystyle {\sum}_i}{\displaystyle {\sum}_j}(ij)N\left(i,j\right)-{\mu}_x^2}{\sigma_x^2} $$

where \( {\mu}_x \) and \( {\sigma}_x \) are the mean and standard deviation of \( {p}_x \), respectively.

$$ Entropy=-{\displaystyle \sum_i}{\displaystyle \sum_j}N\left(i,j\right) log\left(N\left(i,j\right)\right) $$

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Nketiah, G., Elschot, M., Kim, E. et al. T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results. Eur Radiol 27, 3050–3059 (2017). https://doi.org/10.1007/s00330-016-4663-1

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