Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
European Radiology
Published in: European Radiology 5/2015

01-05-2015 | Oncology

Multivariate modelling of prostate cancer combining magnetic resonance derived T2, diffusion, dynamic contrast-enhanced and spectroscopic parameters

Authors: S. F. Riches, G. S. Payne, V. A. Morgan, D. Dearnaley, S. Morgan, M. Partridge, N. Livni, C. Ogden, N. M. deSouza

Published in: European Radiology | Issue 5/2015

Login to get access

Abstract

Objectives

The objectives are determine the optimal combination of MR parameters for discriminating tumour within the prostate using linear discriminant analysis (LDA) and to compare model accuracy with that of an experienced radiologist.

Methods

Multiparameter MRIs in 24 patients before prostatectomy were acquired. Tumour outlines from whole-mount histology, T2-defined peripheral zone (PZ), and central gland (CG) were superimposed onto slice-matched parametric maps. T2, Apparent Diffusion Coefficient, initial area under the gadolinium curve, vascular parameters (Ktrans,Kep,Ve), and (choline+polyamines+creatine)/citrate were compared between tumour and non-tumour tissues. Receiver operating characteristic (ROC) curves determined sensitivity and specificity at spectroscopic voxel resolution and per lesion, and LDA determined the optimal multiparametric model for identifying tumours. Accuracy was compared with an expert observer.

Results

Tumours were significantly different from PZ and CG for all parameters (all p < 0.001). Area under the ROC curve for discriminating tumour from non-tumour was significantly greater (p < 0.001) for the multiparametric model than for individual parameters; at 90 % specificity, sensitivity was 41 % (MRSI voxel resolution) and 59 % per lesion. At this specificity, an expert observer achieved 28 % and 49 % sensitivity, respectively.

Conclusion

The model was more accurate when parameters from all techniques were included and performed better than an expert observer evaluating these data.

Key Points

• The combined model increases diagnostic accuracy in prostate cancer compared with individual parameters
• The optimal combined model includes parameters from diffusion, spectroscopy, perfusion, and anatominal MRI
• The computed model improves tumour detection compared to an expert viewing parametric maps
Literature
1.
Roethke MC, Kniess M, Kaufmann S et al (2014) Can high-spatial resolution T2-weighted endorectal MRI rule out clinically significant prostate cancer? World J Urol 32:379–383CrossRefPubMed
2.
Yamamura J, Salomon G, Buchert R et al (2011) Magnetic resonance imaging of prostate cancer: diffusion-weighted imaging in comparison with sextant biopsy. J Comput Assist Tomogr 35:223–228CrossRefPubMed
3.
Nagayama M, Watanabe Y, Terai A et al (2011) Determination of the cutoff level of apparent diffusion coefficient values for detection of prostate cancer. Jpn J Radiol 29:488–494CrossRefPubMed
4.
Mazaheri Y, Hricak H, Fine SW et al (2009) Prostate tumor volume measurement with combined T2-weighted imaging and diffusion-weighted MR: correlation with pathologic tumor volume. Radiology 252:449–457CrossRefPubMedCentralPubMed
5.
Langer DL, van der Kwast TH, Evans AJ et al (2009) Prostate cancer detection with multi-parametric MRI: Logistic regression analysis of quantitative T2, diffusion-weighted imaging, and dynamic contrast-enhanced MRI. J Magn Reson Imaging 30:327–334CrossRefPubMed
6.
Bittencourt LK, Barentsz JO, de Miranda LC, Gasparetto EL (2012) Prostate MRI: diffusion-weighted imaging at 1.5T correlates better with prostatectomy Gleason Grades than TRUS-guided biopsies in peripheral zone tumours. Eur Radiol 22:468–475CrossRefPubMed
7.
Oto A, Yang C, Kayhan A et al (2011) Diffusion-weighted and dynamic contrast-enhanced MRI of prostate cancer: correlation of quantitative MR parameters with Gleason score and tumor angiogenesis. AJR Am J Roentgenol 197:1382–1390CrossRefPubMed
8.
Langer DL, van der Kwast TH, Evans AJ et al (2010) Prostate tissue composition and MR measurements: investigating the relationships between ADC, T2, K(trans), v(e), and corresponding histologic features. Radiology 255:485–494CrossRefPubMed
9.
Jackson AS, Reinsberg SA, Sohaib SA et al (2009) Dynamic contrast-enhanced MRI for prostate cancer localization. Br J Radiol 82:148–156CrossRefPubMed
10.
Franiel T, Hamm B, Hricak H (2011) Dynamic contrast-enhanced magnetic resonance imaging and pharmacokinetic models in prostate cancer. Eur Radiol 21:616–626CrossRefPubMed
11.
Yamamura J, Salomon G, Buchert R et al (2011) MR imaging of prostate cancer: diffusion weighted imaging and (3D) hydrogen 1 (H) MR spectroscopy in comparison with histology. Radiol Res Pract 2011:616852PubMedCentralPubMed
12.
Reinsberg SA, Payne GS, Riches SF et al (2007) Combined use of diffusion-weighted MRI and 1H MR spectroscopy to increase accuracy in prostate cancer detection. AJR Am J Roentgenol 188:91–98CrossRefPubMed
13.
Weinreb JC, Blume JD, Coakley FV et al (2009) Prostate cancer: sextant localization at MR imaging and MR spectroscopic imaging before prostatectomy–results of ACRIN prospective multi-institutional clinicopathologic study. Radiology 251:122–133CrossRefPubMedCentralPubMed
14.
Scheenen TW, Futterer J, Weiland E et al (2011) Discriminating cancer from noncancer tissue in the prostate by 3-dimensional proton magnetic resonance spectroscopic imaging: a prospective multicenter validation study. Investig Radiol 46:25–33CrossRef
15.
Garcia-Martin ML, Adrados M, Ortega MP et al (2011) Quantitative (1) H MR spectroscopic imaging of the prostate gland using LCModel and a dedicated basis-set: correlation with histologic findings. Magn Reson Med 65:329–339CrossRefPubMed
16.
Turkbey B, Pinto PA, Mani H et al (2010) Prostate cancer: value of multiparametric MR imaging at 3 T for detection–histopathologic correlation. Radiology 255:89–99CrossRefPubMedCentralPubMed
17.
Kumar V, Jagannathan NR, Kumar R et al (2006) Correlation between metabolite ratios and ADC values of prostate in men with increased PSA level. Magn Reson Imaging 24:541–548CrossRefPubMed
18.
Groenendaal G, Borren A, Moman MR et al (2012) Pathologic validation of a model based on diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging for tumor delineation in the prostate peripheral zone. Int J Radiat Oncol Biol Phys 82:e537–e544CrossRefPubMed
19.
Futterer JJ, Heijmink SW, Scheenen TW et al (2006) Prostate cancer localization with dynamic contrast-enhanced MR imaging and proton MR spectroscopic imaging. Radiology 241:449–458CrossRefPubMed
20.
de Rooij M, Hamoen EH, Futterer JJ, Barentsz JO, Rovers MM (2014) Accuracy of multiparametric MRI for prostate cancer detection: a meta-analysis. AJR Am J Roentgenol 202:343–351CrossRefPubMed
21.
Borren A, Groenendaal G, Moman MR et al (2014) Accurate prostate tumour detection with multiparametric magnetic resonance imaging: dependence on histological properties. Acta Oncol 53:88–95CrossRefPubMed
22.
Riches SF, Payne GS, Morgan VA et al (2009) MRI in the detection of prostate cancer: combined apparent diffusion coefficient, metabolite ratio, and vascular parameters. AJR Am J Roentgenol 193:1583–1591CrossRefPubMed
23.
Altman DG (1990) Practical Statistics for Medical Research. Taylor & Francis.
24.
d’Arcy JA, Collins DJ, Padhani AR et al (2006) Magnetic Resonance Imaging Workbench: analysis and visualization of dynamic contrast-enhanced MR imaging data. Radiographics 26:621–632CrossRefPubMed
25.
Orton MR, d'Arcy JA, Walker-Samuel S et al (2008) Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI. Phys Med Biol 53:1225–1239CrossRefPubMed
26.
Parker GJ, Roberts C, Macdonald A et al (2006) Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med 56:993–1000CrossRefPubMed
27.
Rohrer M, Bauer H, Mintorovitch J, Requardt M, Weinmann HJ (2005) Comparison of magnetic properties of MRI contrast media solutions at different magnetic field strengths. Investig Radiol 40:715–724CrossRef
28.
Provencher SW (2001) Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR Biomed 14:260–264CrossRefPubMed
29.
Jhavar SG, Fisher C, Jackson A et al (2005) Processing of radical prostatectomy specimens for correlation of data from histopathological, molecular biological, and radiological studies: a new whole organ technique. J Clin Pathol 58:504–508CrossRefPubMedCentralPubMed
30.
31.
Selnaes KM, Heerschap A, Jensen LR et al (2012) Peripheral zone prostate cancer localization by multiparametric magnetic resonance at 3 T: unbiased cancer identification by matching to histopathology. Investig Radiol 47:624–633CrossRef
32.
Shah V, Turkbey B, Mani H et al (2012) Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging. Med Phys 39:4093–4103CrossRefPubMedCentralPubMed
33.
Wefer AE, Hricak H, Vigneron DB et al (2000) Sextant localization of prostate cancer: comparison of sextant biopsy, magnetic resonance imaging and magnetic resonance spectroscopic imaging with step section histology. J Urol 164:400–404CrossRefPubMed
34.
Mazaheri Y, Shukla-Dave A, Hricak H et al (2008) Prostate cancer: identification with combined diffusion-weighted MR imaging and 3D 1H MR spectroscopic imaging–correlation with pathologic findings. Radiology 246:480–488CrossRefPubMed
35.
Le Nobin J, Orczyk C, Deng FM et al (2014) Prostate Tumor Volumes: Agreement Between MRI and Histology Using Novel Co-registration Software. BJU Int.
36.
Reisaeter LA, Futterer JJ, Halvorsen OJ et al (2014) 1.5-T multiparametric MRI using PI-RADS: a region by region analysis to localize the index-tumor of prostate cancer in patients undergoing prostatectomy. Acta Radiol.
37.
Engelbrecht MR, Huisman HJ, Laheij RJ et al (2003) Discrimination of prostate cancer from normal peripheral zone and central gland tissue by using dynamic contrast-enhanced MR imaging. Radiology 229:248–254CrossRefPubMed
38.
Padhani AR, Gapinski CJ, Macvicar DA et al (2000) Dynamic contrast enhanced MRI of prostate cancer: correlation with morphology and tumour stage, histological grade and PSA. Clin Radiol 55:99–109CrossRefPubMed
39.
Noworolski SM, Vigneron DB, Chen AP, Kurhanewicz J (2008) Dynamic contrast-enhanced MRI and MR diffusion imaging to distinguish between glandular and stromal prostatic tissues. Magn Reson Imaging 26:1071–1080CrossRefPubMedCentralPubMed
40.
Kozlowski P, Chang SD, Jones EC et al (2006) Combined diffusion-weighted and dynamic contrast-enhanced MRI for prostate cancer diagnosis–correlation with biopsy and histopathology. J Magn Reson Imaging 24:108–113CrossRefPubMed
Metadata
Title
Multivariate modelling of prostate cancer combining magnetic resonance derived T2, diffusion, dynamic contrast-enhanced and spectroscopic parameters
Authors
S. F. Riches
G. S. Payne
V. A. Morgan
D. Dearnaley
S. Morgan
M. Partridge
N. Livni
C. Ogden
N. M. deSouza
Publication date
01-05-2015
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 5/2015
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-014-3479-0

Other articles of this Issue 5/2015

European Radiology 5/2015 Go to the issue

Emergency Radiology

Mandatory imaging cuts costs and reduces the rate of unnecessary surgeries in the diagnostic work-up of patients suspected of having appendicitis

Hepatobiliary-Pancreas

Imaging-based evaluation of liver function: comparison of 99mTc-mebrofenin hepatobiliary scintigraphy and Gd-EOB-DTPA-enhanced MRI

Neuro

Cerebral computed tomography angiography using a 70 kVp protocol: improved vascular enhancement with a reduced volume of contrast medium and radiation dose

Neuro

Discriminating pyogenic brain abscesses, necrotic glioblastomas, and necrotic metastatic brain tumors by means of susceptibility-weighted imaging

Breast

3.0 Tesla breast magnetic resonance imaging in patients with nipple discharge when mammography and ultrasound fail

Forensic Medicine

Diffusion-weighted perinatal postmortem magnetic resonance imaging as a marker of postmortem interval