Gabriel Nketiah1, Mattijs Elschot2, Eugene Kim2, Jose R Teruel2, Tom W Scheenen3, Tone F Bathen2,4, Kirsten M Selnæs2,4. 1. Department of Circulation and Medical Imaging, Faculty of Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway. gabriel.nketiah@ntnu.no. 2. Department of Circulation and Medical Imaging, Faculty of Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway. 3. Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands. 4. St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
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.
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 cancerpatients 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.
Authors: Andrew B Rosenkrantz; Michael J Triolo; Jonathan Melamed; Henry Rusinek; Samir S Taneja; Fang-Ming Deng Journal: J Magn Reson Imaging Date: 2014-02-25 Impact factor: 4.813
Authors: Satish E Viswanath; Nicholas B Bloch; Jonathan C Chappelow; Robert Toth; Neil M Rofsky; Elizabeth M Genega; Robert E Lenkinski; Anant Madabhushi Journal: J Magn Reson Imaging Date: 2012-02-15 Impact factor: 4.813
Authors: Duc Fehr; Harini Veeraraghavan; Andreas Wibmer; Tatsuo Gondo; Kazuhiro Matsumoto; Herbert Alberto Vargas; Evis Sala; Hedvig Hricak; Joseph O Deasy Journal: Proc Natl Acad Sci U S A Date: 2015-11-02 Impact factor: 11.205
Authors: John V Hegde; Robert V Mulkern; Lawrence P Panych; Fiona M Fennessy; Andriy Fedorov; Stephan E Maier; Clare M C Tempany Journal: J Magn Reson Imaging Date: 2013-05 Impact factor: 4.813
Authors: Jennifer R Stark; Sven Perner; Meir J Stampfer; Jennifer A Sinnott; Stephen Finn; Anna S Eisenstein; Jing Ma; Michelangelo Fiorentino; Tobias Kurth; Massimo Loda; Edward L Giovannucci; Mark A Rubin; Lorelei A Mucci Journal: J Clin Oncol Date: 2009-05-11 Impact factor: 44.544
Authors: P S Tofts; G Brix; D L Buckley; J L Evelhoch; E Henderson; M V Knopp; H B Larsson; T Y Lee; N A Mayr; G J Parker; R E Port; J Taylor; R M Weisskoff Journal: J Magn Reson Imaging Date: 1999-09 Impact factor: 4.813
Authors: Deukwoo Kwon; Isildinha M Reis; Adrian L Breto; Yohann Tschudi; Nicole Gautney; Olmo Zavala-Romero; Christopher Lopez; John C Ford; Sanoj Punnen; Alan Pollack; Radka Stoyanova Journal: J Med Imaging (Bellingham) Date: 2018-09-06
Authors: Rodrigo Delgadillo; John C Ford; Matthew C Abramowitz; Alan Dal Pra; Alan Pollack; Radka Stoyanova Journal: Strahlenther Onkol Date: 2020-08-21 Impact factor: 3.621