Ileana Montoya Perez^{1,2}, Jussi Toivonen^{1,2}, Parisa Movahedi^{1,2}, Harri Merisaari^{2,3}, Janne Verho^{2}, Pekka Taimen^{4}, Peter J. BostrĂ¶m^{5}, Tapio Pahikkala^{1}, Hannu J. Aronen^{2,6}, and Ivan Jambor^{2,6}

We evaluated the
repeatability of apparent diffusion coefficient, derived using monoexponential
function (ADCm) from prostate cancer DWI (12 b values, 0-2000 s/mm^{2}),
radiomics of prostate cancer and their potential to predict prostate cancer Gleason
score (histological grading system of prostate cancer aggressiveness). Statistical
features (mean, median, 10^{th}, 25^{th} percentile) and Gabor
texture feature of DWI ADCm parametric maps showed high repeatability and
correlated significantly with Gleason score. In contrast, homogeneity
gray-level co-occurrence matrix showed low repeatability despite having
significant correlation with Gleason score.

**Introduction**

**Materials and Methods**

**Results**

**Discussion**

**Conclusion**

1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA: A Cancer Journal for Clinicians. 2017;67(1):7–30.

2. Epstein JI, Allsbrook WCJ, Amin MB, Egevad LL. The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. The American journal of surgical pathology. 2005;29(9):1228–1242.

3. Quentin M, Blondin D, Klasen J, Lanzman RS, Miese FR, Arsov C, Albers P, Antoch G, Wittsack HJ. Comparison of different mathematical models of diffusion-weighted prostate MR imaging. Magnetic Resonance Imaging. 2012;30(10):1468–1474.

4. Toivonen J, Merisaari H, Pesola M, Taimen P, Boström PJ, Pahikkala T, Aronen HJ, Jambor I. Mathematical models for diffusion-weighted imaging of prostate cancer using b values up to 2000 s/mm2: Correlation with Gleason score and repeatability of region of interest analysis. Magnetic Resonance in Medicine. 2015;74(4):1116–1124.

5. Boesen L, Chabanova E, Logager V, Balslev I, Thomsen HS. Apparent diffusion coefficient ratio correlates significantly with prostate cancer gleason score at final pathology. Journal of Magnetic Resonance Imaging. 2015;42(2):446–453.

6. Donati OF, Afaq A, Vargas HA, Mazaheri Y, Zheng J, Moskowitz CS, Hricak H, Akin O. Prostate MRI: Evaluating tumor volume and apparent diffusion coefficient as surrogate biomarkers for predicting tumor Gleason score. Clinical Cancer Research. 2014;20(14):3705–3711.

7. Jambor I, Merisaari H, Taimen P, Boström P, Minn H, Pesola M, Aronen HJ. Evaluation of different mathematical models for diffusion-weighted imaging of normal prostate and prostate cancer using high b-values: A repeatability study. Magnetic resonance in medicine. 2015;73(5):1988–1998.

8. Merisaari H, Jambor I. Optimization of b-value distribution for four mathematical models of prostate cancer diffusion-weighted imaging using b values up to 2000 s/mm2: Simulation and repeatability study. Magnetic resonance in medicine. 2015;73(5):1954–1969.

9. Jambor I, Pesola M, Merisaari H, Taimen P, Boström PJ, Liimatainen T, Aronen HJ. Relaxation along fictitious field, diffusion-weighted imaging, and T2 mapping of prostate cancer: Prediction of cancer aggressiveness. Magnetic resonance in medicine. 2016;75(5):2130–2140.

10. Fehr D, Veeraraghavan H, Wibmer A, Gondo T, Matsumoto K, Vargas HA, Sala E, Hricak H, Deasy JO. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proceedings of the National Academy of Sciences of the United States of America. 2015;112(46):E6265-73.

11. Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychological bulletin. 1979;86(2):420.

Table 2: Spearman correlation coefficient (ρ) for correlation analysis between ADCm features and
Gleason score. * significance with p<0.0001