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Improved Visualization of Prostate Cancer Using Multichannel Computed Diffusion Images: Combining ADC and DWI

(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 feat...

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Autores principales: Hammon, Matthias, Saake, Marc, Laun, Frederik B., Heiss, Rafael, Seuss, Nicola, Janka, Rolf, Cavallaro, Alexander, Uder, Michael, Seuss, Hannes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324736/
https://www.ncbi.nlm.nih.gov/pubmed/35885498
http://dx.doi.org/10.3390/diagnostics12071592
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author Hammon, Matthias
Saake, Marc
Laun, Frederik B.
Heiss, Rafael
Seuss, Nicola
Janka, Rolf
Cavallaro, Alexander
Uder, Michael
Seuss, Hannes
author_facet Hammon, Matthias
Saake, Marc
Laun, Frederik B.
Heiss, Rafael
Seuss, Nicola
Janka, Rolf
Cavallaro, Alexander
Uder, Michael
Seuss, Hannes
author_sort Hammon, Matthias
collection PubMed
description (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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spelling pubmed-93247362022-07-27 Improved Visualization of Prostate Cancer Using Multichannel Computed Diffusion Images: Combining ADC and DWI Hammon, Matthias Saake, Marc Laun, Frederik B. Heiss, Rafael Seuss, Nicola Janka, Rolf Cavallaro, Alexander Uder, Michael Seuss, Hannes Diagnostics (Basel) Article (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. MDPI 2022-06-30 /pmc/articles/PMC9324736/ /pubmed/35885498 http://dx.doi.org/10.3390/diagnostics12071592 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hammon, Matthias
Saake, Marc
Laun, Frederik B.
Heiss, Rafael
Seuss, Nicola
Janka, Rolf
Cavallaro, Alexander
Uder, Michael
Seuss, Hannes
Improved Visualization of Prostate Cancer Using Multichannel Computed Diffusion Images: Combining ADC and DWI
title Improved Visualization of Prostate Cancer Using Multichannel Computed Diffusion Images: Combining ADC and DWI
title_full Improved Visualization of Prostate Cancer Using Multichannel Computed Diffusion Images: Combining ADC and DWI
title_fullStr Improved Visualization of Prostate Cancer Using Multichannel Computed Diffusion Images: Combining ADC and DWI
title_full_unstemmed Improved Visualization of Prostate Cancer Using Multichannel Computed Diffusion Images: Combining ADC and DWI
title_short Improved Visualization of Prostate Cancer Using Multichannel Computed Diffusion Images: Combining ADC and DWI
title_sort improved visualization of prostate cancer using multichannel computed diffusion images: combining adc and dwi
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324736/
https://www.ncbi.nlm.nih.gov/pubmed/35885498
http://dx.doi.org/10.3390/diagnostics12071592
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