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Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics

BACKGROUND: The advent of next-generation computed tomography (CT)- and magnetic resonance imaging (MRI) opened many new perspectives in the evaluation of tumor characteristics. An increasing body of evidence suggests the incorporation of quantitative imaging biomarkers into clinical decision-making...

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Autores principales: Koch, Vitali, Weitzer, Nils, Dos Santos, Daniel Pinto, Gruenewald, Leon D., Mahmoudi, Scherwin, Martin, Simon S., Eichler, Katrin, Bernatz, Simon, Gruber-Rouh, Tatjana, Booz, Christian, Hammerstingl, Renate M., Biciusca, Teodora, Rosbach, Nicolas, Gökduman, Aynur, D’Angelo, Tommaso, Finkelmeier, Fabian, Yel, Ibrahim, Alizadeh, Leona S., Sommer, Christof M., Cengiz, Duygu, Vogl, Thomas J., Albrecht, Moritz H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10114410/
https://www.ncbi.nlm.nih.gov/pubmed/37072856
http://dx.doi.org/10.1186/s40644-023-00549-8
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author Koch, Vitali
Weitzer, Nils
Dos Santos, Daniel Pinto
Gruenewald, Leon D.
Mahmoudi, Scherwin
Martin, Simon S.
Eichler, Katrin
Bernatz, Simon
Gruber-Rouh, Tatjana
Booz, Christian
Hammerstingl, Renate M.
Biciusca, Teodora
Rosbach, Nicolas
Gökduman, Aynur
D’Angelo, Tommaso
Finkelmeier, Fabian
Yel, Ibrahim
Alizadeh, Leona S.
Sommer, Christof M.
Cengiz, Duygu
Vogl, Thomas J.
Albrecht, Moritz H.
author_facet Koch, Vitali
Weitzer, Nils
Dos Santos, Daniel Pinto
Gruenewald, Leon D.
Mahmoudi, Scherwin
Martin, Simon S.
Eichler, Katrin
Bernatz, Simon
Gruber-Rouh, Tatjana
Booz, Christian
Hammerstingl, Renate M.
Biciusca, Teodora
Rosbach, Nicolas
Gökduman, Aynur
D’Angelo, Tommaso
Finkelmeier, Fabian
Yel, Ibrahim
Alizadeh, Leona S.
Sommer, Christof M.
Cengiz, Duygu
Vogl, Thomas J.
Albrecht, Moritz H.
author_sort Koch, Vitali
collection PubMed
description BACKGROUND: The advent of next-generation computed tomography (CT)- and magnetic resonance imaging (MRI) opened many new perspectives in the evaluation of tumor characteristics. An increasing body of evidence suggests the incorporation of quantitative imaging biomarkers into clinical decision-making to provide mineable tissue information. The present study sought to evaluate the diagnostic and predictive value of a multiparametric approach involving radiomics texture analysis, dual-energy CT-derived iodine concentration (DECT-IC), and diffusion-weighted MRI (DWI) in participants with histologically proven pancreatic cancer. METHODS: In this study, a total of 143 participants (63 years ± 13, 48 females) who underwent third-generation dual-source DECT and DWI between November 2014 and October 2022 were included. Among these, 83 received a final diagnosis of pancreatic cancer, 20 had pancreatitis, and 40 had no evidence of pancreatic pathologies. Data comparisons were performed using chi-square statistic tests, one-way ANOVA, or two-tailed Student’s t-test. For the assessment of the association of texture features with overall survival, receiver operating characteristics analysis and Cox regression tests were used. RESULTS: Malignant pancreatic tissue differed significantly from normal or inflamed tissue regarding radiomics features (overall P < .001, respectively) and iodine uptake (overall P < .001, respectively). The performance for the distinction of malignant from normal or inflamed pancreatic tissue ranged between an AUC of ≥ 0.995 (95% CI, 0.955–1.0; P < .001) for radiomics features, ≥ 0.852 (95% CI, 0.767–0.914; P < .001) for DECT-IC, and ≥ 0.690 (95% CI, 0.587–0.780; P = .01) for DWI, respectively. During a follow-up of 14 ± 12 months (range, 10–44 months), the multiparametric approach showed a moderate prognostic power to predict all-cause mortality (c-index = 0.778 [95% CI, 0.697–0.864], P = .01). CONCLUSIONS: Our reported multiparametric approach allowed for accurate discrimination of pancreatic cancer and revealed great potential to provide independent prognostic information on all-cause mortality. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-023-00549-8.
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spelling pubmed-101144102023-04-20 Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics Koch, Vitali Weitzer, Nils Dos Santos, Daniel Pinto Gruenewald, Leon D. Mahmoudi, Scherwin Martin, Simon S. Eichler, Katrin Bernatz, Simon Gruber-Rouh, Tatjana Booz, Christian Hammerstingl, Renate M. Biciusca, Teodora Rosbach, Nicolas Gökduman, Aynur D’Angelo, Tommaso Finkelmeier, Fabian Yel, Ibrahim Alizadeh, Leona S. Sommer, Christof M. Cengiz, Duygu Vogl, Thomas J. Albrecht, Moritz H. Cancer Imaging Research Article BACKGROUND: The advent of next-generation computed tomography (CT)- and magnetic resonance imaging (MRI) opened many new perspectives in the evaluation of tumor characteristics. An increasing body of evidence suggests the incorporation of quantitative imaging biomarkers into clinical decision-making to provide mineable tissue information. The present study sought to evaluate the diagnostic and predictive value of a multiparametric approach involving radiomics texture analysis, dual-energy CT-derived iodine concentration (DECT-IC), and diffusion-weighted MRI (DWI) in participants with histologically proven pancreatic cancer. METHODS: In this study, a total of 143 participants (63 years ± 13, 48 females) who underwent third-generation dual-source DECT and DWI between November 2014 and October 2022 were included. Among these, 83 received a final diagnosis of pancreatic cancer, 20 had pancreatitis, and 40 had no evidence of pancreatic pathologies. Data comparisons were performed using chi-square statistic tests, one-way ANOVA, or two-tailed Student’s t-test. For the assessment of the association of texture features with overall survival, receiver operating characteristics analysis and Cox regression tests were used. RESULTS: Malignant pancreatic tissue differed significantly from normal or inflamed tissue regarding radiomics features (overall P < .001, respectively) and iodine uptake (overall P < .001, respectively). The performance for the distinction of malignant from normal or inflamed pancreatic tissue ranged between an AUC of ≥ 0.995 (95% CI, 0.955–1.0; P < .001) for radiomics features, ≥ 0.852 (95% CI, 0.767–0.914; P < .001) for DECT-IC, and ≥ 0.690 (95% CI, 0.587–0.780; P = .01) for DWI, respectively. During a follow-up of 14 ± 12 months (range, 10–44 months), the multiparametric approach showed a moderate prognostic power to predict all-cause mortality (c-index = 0.778 [95% CI, 0.697–0.864], P = .01). CONCLUSIONS: Our reported multiparametric approach allowed for accurate discrimination of pancreatic cancer and revealed great potential to provide independent prognostic information on all-cause mortality. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-023-00549-8. BioMed Central 2023-04-18 /pmc/articles/PMC10114410/ /pubmed/37072856 http://dx.doi.org/10.1186/s40644-023-00549-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Koch, Vitali
Weitzer, Nils
Dos Santos, Daniel Pinto
Gruenewald, Leon D.
Mahmoudi, Scherwin
Martin, Simon S.
Eichler, Katrin
Bernatz, Simon
Gruber-Rouh, Tatjana
Booz, Christian
Hammerstingl, Renate M.
Biciusca, Teodora
Rosbach, Nicolas
Gökduman, Aynur
D’Angelo, Tommaso
Finkelmeier, Fabian
Yel, Ibrahim
Alizadeh, Leona S.
Sommer, Christof M.
Cengiz, Duygu
Vogl, Thomas J.
Albrecht, Moritz H.
Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics
title Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics
title_full Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics
title_fullStr Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics
title_full_unstemmed Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics
title_short Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics
title_sort multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy ct, diffusion-weighted mri, and radiomics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10114410/
https://www.ncbi.nlm.nih.gov/pubmed/37072856
http://dx.doi.org/10.1186/s40644-023-00549-8
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