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Evaluation of Apparent Diffusion Coefficient as a Predictor of Grade Reclassification in Men on Active Surveillance for Prostate Cancer

Published:January 15, 2020DOI:https://doi.org/10.1016/j.urology.2020.01.001

      ABSTRACT

      OBJECTIVE

      To evaluate the association between apparent diffusion coefficient (ADC) on initial multiparametric MRI (mpMRI) and biopsy grade reclassification (GR) to grade group (GG) ≥2 prostate cancer (CaP) in men on active surveillance (AS) with GG 1 CaP.

      METHODS

      We retrospectively identified 242 AS patients with reported ADC values on their initial mpMRI. ADC value from the index lesion was assessed as an independent predictor of GR using a Cox model. To ease clinical interpretation, we used a log-rank test to establish an ADC cutoff of 1128 × 10−6 mm2/s for Kaplan-Meier analysis.

      RESULTS

      Of the 242 men, 70 underwent GR following initial mpMRI, of which 26 (37%) had GR at the index lesion. There was no significant difference in the median interval between biopsies for men with and without GR (P >.9). Men with GR had significantly lower median ADC than those without GR (P = .01). In multivariable analysis adjusting for age, prostate-specific antigen density, and National Comprehensive Cancer Network risk group, a 100-unit decrease in ADC was associated with a 12% increase in the risk of GR (HR = 1.12, 95% CI: 1.01–1.22, P = .03). Two- and 4-year rates of freedom from GR were significantly lower for men with ADC <1128 × 10−6 mm2/s vs ADC ≥1128 × 10−6 mm2/s (62% and 42% vs 78% and 68%, respectively; P <.001).

      CONCLUSION

      For AS patients, lower ADC on initial mpMRI index lesion is associated with increased risk of GR to GG ≥2 CaP and would be a useful component of multivariable risk prediction tools.
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      References

        • Romero-Otero J
        • García-Gómez B
        • Duarte-Ojeda JM
        • et al.
        Active surveillance for prostate cancer.
        Int J Urol. 2016; 23: 211-218https://doi.org/10.1111/iju.13016
        • Mamawala MM
        • Rao K
        • Landis P
        • et al.
        Risk prediction tool for grade re-classification in men with favourable-risk prostate cancer on active surveillance.
        BJU Int. 2017; 120: 25-31https://doi.org/10.1111/bju.13608
        • Coley RY
        • Zeger SL
        • Mamawala M
        • Pienta KJ
        • Carter HB
        Prediction of the pathologic gleason score to inform a personalized management program for prostate cancer.
        Eur Urol. 2017; 72: 135-141https://doi.org/10.1016/j.eururo.2016.08.005
        • Thompson IM
        • Ankerst DP
        • Chi C
        • et al.
        Assessing prostate cancer risk: results from the prostate cancer prevention trial.
        J Natl Cancer Inst. 2006; 98: 529-534https://doi.org/10.1093/jnci/djj131
        • Shaish H
        • Kang SK
        • Rosenkrantz AB
        The utility of quantitative ADC values for differentiating high-risk from low-risk prostate cancer: a systematic review and meta-analysis.
        Abdom Radiol. 2017; 42: 260-270https://doi.org/10.1007/s00261-016-0848-y
        • Wu X
        • Reinikainen P
        • Vanhanen A
        • et al.
        Correlation between apparent diffusion coefficient value on diffusion-weighted MR imaging and Gleason score in prostate cancer.
        Diagn Interv Imaging. 2016; https://doi.org/10.1016/j.diii.2016.08.009
        • Donati OF
        • Mazaheri Y
        • Afaq A
        • et al.
        Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient.
        Radiology. 2014; https://doi.org/10.1148/radiol.13130973
        • Barbieri S
        • Brönnimann M
        • Boxler S
        • Vermathen P
        • Thoeny HC
        Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI.
        Eur Radiol. 2017; https://doi.org/10.1007/s00330-016-4449-5
        • Tamada T
        • Dani H
        • Taneja SS
        • Rosenkrantz AB
        The role of whole-lesion apparent diffusion coefficient analysis for predicting outcomes of prostate cancer patients on active surveillance.
        Abdom Radiol. 2017; 42: 2340-2345https://doi.org/10.1007/s00261-017-1135-2
        • Hutchinson R
        • Lotan Y
        Cost consideration in utilization of multiparametric magnetic resonance imaging in prostate cancer.
        Transl Androl Urol. 2017; 6: 345-354https://doi.org/10.21037/tau.2017.01.13
        • Rais-Bahrami S
        • Siddiqui MM
        • Vourganti S
        • et al.
        Diagnostic value of biparametric magnetic resonance imaging (MRI) as an adjunct to prostate-specific antigen (PSA)-based detection of prostate cancer in men without prior biopsies.
        BJU Int. 2015; 115: 381-388https://doi.org/10.1111/bju.12639
        • Edelstein WA
        • Mahesh M
        • Carrino JA
        MRI: time is dose—and money and versatility.
        J Am Coll Radiol. 2010; https://doi.org/10.1016/j.jacr.2010.05.002
        • Epstein JI
        • Walsh PC
        • Carmichael M
        • Brendler CB
        Pathologic and clinical findings to predict tumor extent of nonpalpable (Stage T1 c) prostate cancer.
        JAMA J Am Med Assoc. 1994; 271: 368-374https://doi.org/10.1001/jama.1994.03510290050036
        • Chen RC
        • Rumble RB
        • Loblaw DA
        • et al.
        Active surveillance for the management of localized prostate cancer (Cancer Care Ontario Guideline): American Society of Clinical Oncology clinical practice guideline endorsement.
        J Clin Oncol. 2016; 12 (JCO.2015.65.7759-)https://doi.org/10.1200/JCO.2015.65.7759
        • Mohler JL
        • Kantoff PW
        • Armstrong AJ
        • et al.
        Prostate cancer, version 2.2014.
        J Natl Compr Canc Netw. 2014; 12: 686-718https://doi.org/10.6004/JNCCN.2014.0072
        • Kitajima K
        • Kaji Y
        • Kuroda K
        • Sugimura K
        High b-value diffusion-weighted imaging in normal and malignant peripheral zone tissue of the prostate: effect of signal-to-noise ratio.
        Magn Reson Med Sci. 2008; https://doi.org/10.2463/mrms.7.93
        • Contal C
        • O'Quigley J
        An application of changepoint methods in studying the effect of age on survival in breast cancer.
        Comput Stat Data Anal. 1999; 30: 253-270https://doi.org/10.1016/S0167-9473(98)00096-6
        • Siddiqui MM
        • Rais-Bahrami S
        • Turkbey B
        • et al.
        Comparison of MR/ultrasound fusion–guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer.
        JAMA. 2016; 1210: 390-397https://doi.org/10.1001/jama.2014.17942
        • Alam R
        • Carter HB
        • Epstein JI
        • Tosoian JJ
        Active surveillance of prostate cancer : current state of practice and utility of multiparametric magnetic resonance imaging.
        Rev Urol. 2017; 19: 77-88https://doi.org/10.3909/riu0757
        • Felker ER
        • Wu J
        • Natarajan S
        • et al.
        Serial magnetic resonance imaging in active surveillance of prostate cancer: incremental value.
        J Urol. 2016; 195: 1421-1427https://doi.org/10.1016/j.juro.2015.11.055
        • Muller BG
        • Shih JH
        • Sankineni S
        • et al.
        Prostate cancer: interobserver agreement and accuracy with the revised prostate imaging reporting and data system at multiparametric MR imaging.
        Radiology. 2015; https://doi.org/10.1148/radiol.2015142818
        • Rosenkrantz AB
        • Ginocchio LA
        • Cornfeld D
        • et al.
        Interobserver reproducibility of the PI-RADS Version 2 Lexicon: a multicenter study of six experienced prostate radiologists.
        Radiology. 2016; https://doi.org/10.1148/radiol.2016152542
        • Sonn GA
        • Fan RE
        • Ghanouni P
        • et al.
        Prostate magnetic resonance imaging interpretation varies substantially across radiologists.
        Eur Urol Focus. 2018; 5: 592-599https://doi.org/10.1016/j.euf.2017.11.010
        • Weinreb JC
        • Barentsz JO
        • Choyke PL
        • et al.
        PI-RADS prostate imaging - reporting and data system: 2015, Version 2.
        Eur Urol. 2016; 69: 16-40https://doi.org/10.1016/j.eururo.2015.08.052
        • Huntley JH
        • Coley RY
        • Carter HB
        • Radhakrishnan A
        • Krakow M
        • Pollack CE
        Clinical evaluation of an individualized risk prediction tool for men on active surveillance for prostate cancer.
        Urology. 2018; 121: 118-124https://doi.org/10.1016/j.urology.2018.08.021
        • Dianat SS
        • Carter HB
        • Macura KJ
        Magnetic resonance-guided prostate biopsy.
        Magn Reson Imaging Clin N Am. 2015; 23: 621-631https://doi.org/10.1016/j.mric.2015.05.005
        • Haffner MC
        • Mosbruger T
        • Esopi DM
        • et al.
        Tracking the clonal origin of lethal prostate cancer.
        J Clin Invest. 2013; https://doi.org/10.1172/JCI70354
        • Haffner MC
        • De Marzo AM
        • Yegnasubramanian S
        • Epstein JI
        • Ballentine Carter H
        Diagnostic challenges of clonal heterogeneity in prostate cancer.
        J Clin Oncol. 2015; https://doi.org/10.1200/JCO.2013.50.3540
        • Kneppers J
        • Krijgsman O
        • Melis M
        • et al.
        Frequent clonal relations between metastases and non-index prostate cancer lesions.
        JCI Insight. 2019; https://doi.org/10.1172/jci.insight.124756
        • Chatterjee A
        • Bourne RM
        • Wang S
        • et al.
        Diagnosis of prostate cancer with noninvasive estimation of prostate tissue composition by using hybrid multidimensional MR imaging: a feasibility study.
        Radiology. 2018; https://doi.org/10.1148/radiol.2018171130