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Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy

PURPOSE: To develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET). MATERIAL AND METHODS: Consecutive patients, who underwent (68)Ga-PSMA11-PET/...

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Autores principales: Spohn, Simon K. B., Schmidt-Hegemann, Nina-Sophie, Ruf, Juri, Mix, Michael, Benndorf, Matthias, Bamberg, Fabian, Makowski, Marcus R., Kirste, Simon, Rühle, Alexander, Nouvel, Jerome, Sprave, Tanja, Vogel, Marco M. E., Galitsnaya, Polina, Gschwend, Jürgen E., Gratzke, Christian, Stief, Christian, Löck, Steffen, Zwanenburg, Alex, Trapp, Christian, Bernhardt, Denise, Nekolla, Stephan G., Li, Minglun, Belka, Claus, Combs, Stephanie E., Eiber, Matthias, Unterrainer, Lena, Unterrainer, Marcus, Bartenstein, Peter, Grosu, Anca-L., Zamboglou, Constantinos, Peeken, Jan C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250433/
https://www.ncbi.nlm.nih.gov/pubmed/36929180
http://dx.doi.org/10.1007/s00259-023-06195-3
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author Spohn, Simon K. B.
Schmidt-Hegemann, Nina-Sophie
Ruf, Juri
Mix, Michael
Benndorf, Matthias
Bamberg, Fabian
Makowski, Marcus R.
Kirste, Simon
Rühle, Alexander
Nouvel, Jerome
Sprave, Tanja
Vogel, Marco M. E.
Galitsnaya, Polina
Gschwend, Jürgen E.
Gratzke, Christian
Stief, Christian
Löck, Steffen
Zwanenburg, Alex
Trapp, Christian
Bernhardt, Denise
Nekolla, Stephan G.
Li, Minglun
Belka, Claus
Combs, Stephanie E.
Eiber, Matthias
Unterrainer, Lena
Unterrainer, Marcus
Bartenstein, Peter
Grosu, Anca-L.
Zamboglou, Constantinos
Peeken, Jan C.
author_facet Spohn, Simon K. B.
Schmidt-Hegemann, Nina-Sophie
Ruf, Juri
Mix, Michael
Benndorf, Matthias
Bamberg, Fabian
Makowski, Marcus R.
Kirste, Simon
Rühle, Alexander
Nouvel, Jerome
Sprave, Tanja
Vogel, Marco M. E.
Galitsnaya, Polina
Gschwend, Jürgen E.
Gratzke, Christian
Stief, Christian
Löck, Steffen
Zwanenburg, Alex
Trapp, Christian
Bernhardt, Denise
Nekolla, Stephan G.
Li, Minglun
Belka, Claus
Combs, Stephanie E.
Eiber, Matthias
Unterrainer, Lena
Unterrainer, Marcus
Bartenstein, Peter
Grosu, Anca-L.
Zamboglou, Constantinos
Peeken, Jan C.
author_sort Spohn, Simon K. B.
collection PubMed
description PURPOSE: To develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET). MATERIAL AND METHODS: Consecutive patients, who underwent (68)Ga-PSMA11-PET/CT-guided sRT from three high-volume centers in Germany, were included in this retrospective multicenter study. Patients had PET-positive local recurrences and were treated with intensity-modulated sRT. Radiomic features were extracted from volumes of interests on CT guided by focal PSMA-PET uptakes. After preprocessing, clinical, radiomics, and combined clinical-radiomic models were developed combining different feature reduction techniques and Cox proportional hazard models within a nested cross validation approach. RESULTS: Among 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature. CONCLUSION: This is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00259-023-06195-3.
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spelling pubmed-102504332023-06-10 Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy Spohn, Simon K. B. Schmidt-Hegemann, Nina-Sophie Ruf, Juri Mix, Michael Benndorf, Matthias Bamberg, Fabian Makowski, Marcus R. Kirste, Simon Rühle, Alexander Nouvel, Jerome Sprave, Tanja Vogel, Marco M. E. Galitsnaya, Polina Gschwend, Jürgen E. Gratzke, Christian Stief, Christian Löck, Steffen Zwanenburg, Alex Trapp, Christian Bernhardt, Denise Nekolla, Stephan G. Li, Minglun Belka, Claus Combs, Stephanie E. Eiber, Matthias Unterrainer, Lena Unterrainer, Marcus Bartenstein, Peter Grosu, Anca-L. Zamboglou, Constantinos Peeken, Jan C. Eur J Nucl Med Mol Imaging Original Article PURPOSE: To develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET). MATERIAL AND METHODS: Consecutive patients, who underwent (68)Ga-PSMA11-PET/CT-guided sRT from three high-volume centers in Germany, were included in this retrospective multicenter study. Patients had PET-positive local recurrences and were treated with intensity-modulated sRT. Radiomic features were extracted from volumes of interests on CT guided by focal PSMA-PET uptakes. After preprocessing, clinical, radiomics, and combined clinical-radiomic models were developed combining different feature reduction techniques and Cox proportional hazard models within a nested cross validation approach. RESULTS: Among 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature. CONCLUSION: This is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00259-023-06195-3. Springer Berlin Heidelberg 2023-03-16 2023 /pmc/articles/PMC10250433/ /pubmed/36929180 http://dx.doi.org/10.1007/s00259-023-06195-3 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/) .
spellingShingle Original Article
Spohn, Simon K. B.
Schmidt-Hegemann, Nina-Sophie
Ruf, Juri
Mix, Michael
Benndorf, Matthias
Bamberg, Fabian
Makowski, Marcus R.
Kirste, Simon
Rühle, Alexander
Nouvel, Jerome
Sprave, Tanja
Vogel, Marco M. E.
Galitsnaya, Polina
Gschwend, Jürgen E.
Gratzke, Christian
Stief, Christian
Löck, Steffen
Zwanenburg, Alex
Trapp, Christian
Bernhardt, Denise
Nekolla, Stephan G.
Li, Minglun
Belka, Claus
Combs, Stephanie E.
Eiber, Matthias
Unterrainer, Lena
Unterrainer, Marcus
Bartenstein, Peter
Grosu, Anca-L.
Zamboglou, Constantinos
Peeken, Jan C.
Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy
title Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy
title_full Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy
title_fullStr Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy
title_full_unstemmed Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy
title_short Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy
title_sort development of psma-pet-guided ct-based radiomic signature to predict biochemical recurrence after salvage radiotherapy
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250433/
https://www.ncbi.nlm.nih.gov/pubmed/36929180
http://dx.doi.org/10.1007/s00259-023-06195-3
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