Quantitative MRI biomarker for classification of clinically significant prostate cancer: Calibration for reproducibility across echo times.

calibration diffusion‐weighted imaging echo time prostate cancer quantitative biomarker restricted spectrum imaging restriction score

Journal

Journal of applied clinical medical physics
ISSN: 1526-9914
Titre abrégé: J Appl Clin Med Phys
Pays: United States
ID NLM: 101089176

Informations de publication

Date de publication:
07 Oct 2024
Historique:
revised: 08 08 2024
received: 12 02 2024
accepted: 14 08 2024
medline: 7 10 2024
pubmed: 7 10 2024
entrez: 7 10 2024
Statut: aheadofprint

Résumé

The purpose of the present study is to develop a calibration method to account for differences in echo times (TE) and facilitate the use of restriction spectrum imaging restriction score (RSIrs) as a quantitative biomarker for the detection of clinically significant prostate cancer (csPCa). This study included 197 consecutive patients who underwent MRI and biopsy examination; 97 were diagnosed with csPCa (grade group ≥ 2). RSI data were acquired three times during the same session: twice at minimum TE ~75 ms and once at TE = 90 ms (TEmin Scaling factors for C The proposed linear calibration method produces similar quantitative biomarker values for acquisitions with different TE, reducing TE-induced error by 72% and 55% for non-csPCa and csPCa, respectively.

Identifiants

pubmed: 39374162
doi: 10.1002/acm2.14514
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e14514

Subventions

Organisme : NIH HHS
ID : NIH/NIBIB K08EB026503
Pays : United States
Organisme : NIH HHS
ID : NIH UL1TR000100
Pays : United States
Organisme : American Society for Radiation Oncology
Organisme : Prostate Cancer Foundation
Organisme : Department of Defense
ID : DOD/CDMRP PC220278

Informations de copyright

© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.

Références

Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17‐48.
Bengtsson J, Thimansson E, Baubeta E, et al. Correlation between ADC, ADC ratio, and Gleason Grade group in prostate cancer patients undergoing radical prostatectomy: retrospective multicenter study with different MRI scanners. Front Oncol. 2023;13:1079040.
Turkbey B, Rosenkrantz AB, Haider MA, et al. Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol. 2019;76:340‐351.
Le Bihan D. Apparent diffusion coefficient and beyond: what diffusion MR imaging can tell us about tissue structure. Radiology. 2013;268:318‐322.
Riches SF, Hawtin K, Charles‐Edwards EM, et al. Diffusion‐weighted imaging of the prostate and rectal wall: comparison of biexponential and monoexponential modelled diffusion and associated perfusion coefficients. NMR Biomed. 2009;22:318‐325.
Le Bihan D, Breton E, Lallemand D, et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988;168:497‐505.
Rosenkrantz AB, Sigmund EE, Johnson G, et al. Prostate cancer: feasibility and preliminary experience of a diffusional kurtosis model for detection and assessment of aggressiveness of peripheral zone cancer. Radiology. 2012;264:126‐135.
Si Y, Liu R‐B. Diagnostic performance of monoexponential dwi versus diffusion kurtosis imaging in prostate cancer: a systematic review and meta‐analysis. AJR Am J Roentgenol. 2018;211:358‐368.
Panagiotaki E, Chan RW, Dikaios N, et al. Microstructural characterization of normal and malignant human prostate tissue with vascular, extracellular, and restricted diffusion for cytometry in tumours magnetic resonance imaging. Invest Radiol. 2015;50:218‐227.
Johnston EW, Bonet‐Carne E, Ferizi U, et al. VERDICT MRI for prostate cancer: intracellular volume fraction versus apparent diffusion coefficient. Radiology. 2019;291:391‐397.
Singh S, Rogers H, Kanber B, et al. Avoiding unnecessary biopsy after multiparametric prostate MRI with VERDICT analysis: the INNOVATE study. Radiology. 2022;305:212536.
Chatterjee A, Watson G, Myint E, et al. Changes in epithelium, stroma, and lumen space correlate more strongly with Gleason pattern and are stronger predictors of prostate adc changes than cellularity metrics. Radiology. 2015;277:751‐762.
Sadinski M, Karczmar G, Peng Y, et al. Pilot study of the use of hybrid multidimensional T2‐weighted imaging‐DWI for the diagnosis of prostate cancer and evaluation of Gleason score. AJR Am J Roentgenol. 2016;207:592‐598.
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;287:864‐873.
Lee GH, Chatterjee A, Karademir I, et al. Comparing radiologist performance in diagnosing clinically significant prostate cancer with multiparametric versus hybrid multidimensional MRI. Radiology. 2022;305:399‐407.
Brunsing RL, Schenker‐Ahmed NM, White NS, et al. Restriction spectrum imaging: an evolving imaging biomarker in prostate MRI. J Magn Reson Imaging. 2017;45:323‐336.
Conlin CC, Feng CH, Rodriguez‐Soto AE, et al. Improved characterization of diffusion in normal and cancerous prostate tissue through optimization of multicompartmental signal models. J Magn Reson Imaging. 2021;53:628‐639.
Feng CH, Conlin CC, Batra K, et al. Voxel‐level classification of prostate cancer on magnetic resonance imaging: improving accuracy using four‐compartment restriction spectrum imaging. J Magn Reson Imaging. 2021;54:975‐984.
Zhong AY, Digma LA, Hussain T, et al. Automated patient‐level prostate cancer detection with quantitative diffusion magnetic resonance imaging. Eur Urol Open Sci. 2023;47:20‐28.
White NS, McDonald C, McDonald CR, et al. Diffusion‐weighted imaging in cancer: physical foundations and applications of restriction spectrum imaging. Cancer Res. 2014;74:4638‐4652.
Egnell L, Jerome NP, Andreassen MMS, et al. Effects of echo time on IVIM quantifications of locally advanced breast cancer in clinical diffusion‐weighted MRI at 3 T. NMR Biomed. 2022;35:e4654.
Holland D, Kuperman JM, Dale AM. Efficient correction of inhomogeneous static magnetic field‐induced distortion in echo planar imaging. NeuroImage. 2010;50:175‐183.
Zhuang J, Hrabe J, Kangarlu A, et al. Correction of eddy‐current distortions in diffusion tensor images using the known directions and strengths of diffusion gradients. J Magn Reson Imaging. 2006;24:1188‐1193.
Jäger F, Hornegger J. Nonrigid registration of joint histograms for intensity standardization in magnetic resonance imaging. IEEE Trans Med Imaging. 2009;28:137‐150.
Ceranka J, Lecouvet F, Michoux N, et al. Comparison of intra‐ and inter‐patient intensity standardization methods for multi‐parametric whole‐body MRI. Biomed Phys Eng Express. 2023;9:035028.
Torbati ME, Tudorascu DL, Minhas DS, et al. Multi‐scanner harmonization of paired neuroimaging data via structure preserving embedding learning. In: IEEE Int Conf Comput Vis Workshop IEEE Int Conf Comput Vis 2021. IEEE; 2021:3277‐3286.

Auteurs

Karoline Kallis (K)

Department of Radiation Medicine and Applied Sciences, UC San Diego Health, La Jolla, California, USA.

Christopher C Conlin (CC)

Department of Radiology, UC San Diego Health, La Jolla, California, USA.

Courtney Ollison (C)

Department of Radiation Medicine and Applied Sciences, UC San Diego Health, La Jolla, California, USA.

Michael E Hahn (ME)

Department of Radiology, UC San Diego Health, La Jolla, California, USA.

Rebecca Rakow-Penner (R)

Department of Radiology, UC San Diego Health, La Jolla, California, USA.

Anders M Dale (AM)

Department of Radiology, UC San Diego Health, La Jolla, California, USA.
Department of Neurosciences, UC San Diego Health, La Jolla, California, USA.
Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, California, USA.

Tyler M Seibert (TM)

Department of Radiation Medicine and Applied Sciences, UC San Diego Health, La Jolla, California, USA.
Department of Radiology, UC San Diego Health, La Jolla, California, USA.
Department of Bioengineering, UC San Diego Jacobs School of Engineering, La Jolla, California, USA.

Classifications MeSH