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
Jussi Toivonen
Department of Diagnostic Radiology, University of Turku, Turku, Finland
Department of Information Technology, University of Turku, Turku, Finland
Search for more papers by this authorHarri Merisaari
Department of Information Technology, University of Turku, Turku, Finland
Turku PET Centre, University of Turku, Turku, Finland
Search for more papers by this authorMarko Pesola
Department of Diagnostic Radiology, University of Turku, Turku, Finland
Search for more papers by this authorPekka Taimen
Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland
Search for more papers by this authorPeter J. Boström
Department of Urology, Turku University Hospital, Turku, Finland
Search for more papers by this authorTapio Pahikkala
Department of Information Technology, University of Turku, Turku, Finland
Search for more papers by this authorHannu J. Aronen
Department of Diagnostic Radiology, University of Turku, Turku, Finland
Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
Search for more papers by this authorCorresponding Author
Ivan Jambor
Department of Diagnostic Radiology, University of Turku, Turku, Finland
Correspondence to: Ivan Jambor, M.D., Department of Diagnostic Radiology, University of Turku, Kiinamyllynkatu 4–8, P.O. Box 52, FI-20521 Turku, Finland. E-mail: [email protected]Search for more papers by this authorJussi Toivonen
Department of Diagnostic Radiology, University of Turku, Turku, Finland
Department of Information Technology, University of Turku, Turku, Finland
Search for more papers by this authorHarri Merisaari
Department of Information Technology, University of Turku, Turku, Finland
Turku PET Centre, University of Turku, Turku, Finland
Search for more papers by this authorMarko Pesola
Department of Diagnostic Radiology, University of Turku, Turku, Finland
Search for more papers by this authorPekka Taimen
Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland
Search for more papers by this authorPeter J. Boström
Department of Urology, Turku University Hospital, Turku, Finland
Search for more papers by this authorTapio Pahikkala
Department of Information Technology, University of Turku, Turku, Finland
Search for more papers by this authorHannu J. Aronen
Department of Diagnostic Radiology, University of Turku, Turku, Finland
Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
Search for more papers by this authorCorresponding Author
Ivan Jambor
Department of Diagnostic Radiology, University of Turku, Turku, Finland
Correspondence to: Ivan Jambor, M.D., Department of Diagnostic Radiology, University of Turku, Kiinamyllynkatu 4–8, P.O. Box 52, FI-20521 Turku, Finland. E-mail: [email protected]Search for more papers by this authorAbstract
Purpose
To evaluate four mathematical models for diffusion weighted imaging (DWI) of prostate cancer (PCa) in terms of PCa detection and characterization.
Methods
Fifty patients with histologically confirmed PCa underwent two repeated 3 Tesla DWI examinations using 12 equally distributed b values, the highest b value of 2000 s/mm2. Normalized mean signal intensities of regions-of-interest were fitted using monoexponential, kurtosis, stretched exponential, and biexponential models. Tumors were classified into low, intermediate, and high Gleason score groups. Areas under receiver operating characteristic curve (AUCs) were estimated to evaluate performance in PCa detection and Gleason score classifications. The fitted parameters were correlated with Gleason score groups by using the Spearman correlation coefficient (ρ). Coefficient of repeatability and intraclass correlation coefficient [specifically ICC(3,1)], were calculated to evaluate repeatability of the fitted parameters.
Results
The AUC and ρ values were similar between parameters of monoexponential, kurtosis, and stretched exponential (with the exception of the α parameter) models. The absolute ρ values for ADCm, ADCk, K, and ADCs were in the range from 0.31 to 0.53 (P < 0.01). Parameters of the biexponential model demonstrated low repeatability.
Conclusion
In region-of-interest based analysis, the monoexponential model for DWI of PCa using b values up to 2000 s/mm2 was sufficient for PCa detection and characterization. Magn Reson Med 74:1116–1124, 2015. © 2014 Wiley Periodicals, Inc.
Supporting Information
Additional Supporting Information may be found in the online version of this article.
| Filename | Description |
|---|---|
| mrm25482-sup-0001-suppinfo01.pdf6.9 MB |
Supporting Table S1. Patient characteristics. Supporting Table S2. Number of regions of interest in prostate cancer lesions according to Gleason score. Supporting Table S3. Execution time for each model. Supporting Figure S1–S51. The positions of regions of interest placed in the prostate cancer (red color) and peripheral zone (green color) are shown on b = 0 s/mm2 images (1st column) of the 1st (1st row), and 2nd (2nd row) repetitions. The corresponding b = 2000 s/mm2 images without overlaid regions of interest are shown in the 2nd column. The closest matching T2-weighted image and whole mount prostatectomy section are shown in the 3rd column. Prostate cancer is outlined in green/blue on the corresponding whole mount prostatectomy section. In 9 patients, additional region of interest was placed in a separate PCa lesion (marked by blue color). |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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