Improved Visualization of Prostate Cancer Using Multichannel Computed Diffusion Images: Combining ADC and DWI Article Swipe
Matthias Hammon
,
Marc Saake
,
Frederik B. Laun
,
R. Heiß
,
Nicola Seuss
,
Rolf Janka
,
Alexander Cavallaro
,
Michael Uder
,
Hannes Seuß
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics12071592
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics12071592
(1) Background: For the peripheral zone of the prostate, diffusion weighted imaging (DWI) is the most important MRI technique; however, a high b-value image (hbDWI) must always be evaluated in conjunction with an apparent diffusion coefficient (ADC) map. We aimed to unify the important contrast features of both a hbDWI and ADC in one single image, termed multichannel computed diffusion images (mcDI), and evaluate the values of these images in a retrospective clinical study; (2) Methods: Based on the 2D histograms of hbDWI and ADC images of 70 patients with histologically proven prostate cancer (PCa) in the peripheral zone, an algorithm was designed to generate the mcDI. Then, three radiologists evaluated the data of 56 other patients twice in three settings (T2w images +): (1) hbDWI and ADC; (2) mcDI; and (3) mcDI, hbDWI, and ADC. The sensitivity, specificity, and inter-reader variability were evaluated; (3) Results: The overall sensitivity/specificity were 0.91/0.78 (hbDWI + ADC), 0.85/0.88 (mcDI), and 0.97/0.88 (mcDI + hbDWI + ADC). The kappa-values for the inter-reader variability were 0.732 (hbDWI + ADC), 0.800 (mcDI), and 0.853 (mcDI + hbDWI + ADC). (4) Conclusions: By using mcDI, the specificity of the MRI detection of PCa was increased at the expense of the sensitivity. By combining the conventional diffusion data with the mcDI data, both the sensitivity and specificity were improved.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics12071592
- https://www.mdpi.com/2075-4418/12/7/1592/pdf?version=1656565684
- OA Status
- gold
- Cited By
- 2
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283733713
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4283733713Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/diagnostics12071592Digital Object Identifier
- Title
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Improved Visualization of Prostate Cancer Using Multichannel Computed Diffusion Images: Combining ADC and DWIWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-06-30Full publication date if available
- Authors
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Matthias Hammon, Marc Saake, Frederik B. Laun, R. Heiß, Nicola Seuss, Rolf Janka, Alexander Cavallaro, Michael Uder, Hannes SeußList of authors in order
- Landing page
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https://doi.org/10.3390/diagnostics12071592Publisher landing page
- PDF URL
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https://www.mdpi.com/2075-4418/12/7/1592/pdf?version=1656565684Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2075-4418/12/7/1592/pdf?version=1656565684Direct OA link when available
- Concepts
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Effective diffusion coefficient, Prostate cancer, Diffusion MRI, Nuclear medicine, Medicine, Magnetic resonance imaging, Radiology, Artificial intelligence, Computer science, Cancer, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 1, 2023: 1Per-year citation counts (last 5 years)
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13Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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