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A prognostic model integrating PET‐derived metrics and image texture analyses with clinical risk factors from GOYA

Image texture analysis (radiomics) uses radiographic images to quantify characteristics that may identify tumour heterogeneity and associated patient outcomes. Using fluoro‐deoxy‐glucose positron emission tomography/computed tomography (FDG‐PET/CT)‐derived data, including quantitative metrics, image...

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Autores principales: Kostakoglu, Lale, Dalmasso, Federico, Berchialla, Paola, Pierce, Larry A., Vitolo, Umberto, Martelli, Maurizio, Sehn, Laurie H., Trněný, Marek, Nielsen, Tina G., Bolen, Christopher R., Sahin, Deniz, Lee, Calvin, El‐Galaly, Tarec Christoffer, Mattiello, Federico, Kinahan, Paul E., Chauvie, Stephane
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9175666/
https://www.ncbi.nlm.nih.gov/pubmed/35846039
http://dx.doi.org/10.1002/jha2.421
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author Kostakoglu, Lale
Dalmasso, Federico
Berchialla, Paola
Pierce, Larry A.
Vitolo, Umberto
Martelli, Maurizio
Sehn, Laurie H.
Trněný, Marek
Nielsen, Tina G.
Bolen, Christopher R.
Sahin, Deniz
Lee, Calvin
El‐Galaly, Tarec Christoffer
Mattiello, Federico
Kinahan, Paul E.
Chauvie, Stephane
author_facet Kostakoglu, Lale
Dalmasso, Federico
Berchialla, Paola
Pierce, Larry A.
Vitolo, Umberto
Martelli, Maurizio
Sehn, Laurie H.
Trněný, Marek
Nielsen, Tina G.
Bolen, Christopher R.
Sahin, Deniz
Lee, Calvin
El‐Galaly, Tarec Christoffer
Mattiello, Federico
Kinahan, Paul E.
Chauvie, Stephane
author_sort Kostakoglu, Lale
collection PubMed
description Image texture analysis (radiomics) uses radiographic images to quantify characteristics that may identify tumour heterogeneity and associated patient outcomes. Using fluoro‐deoxy‐glucose positron emission tomography/computed tomography (FDG‐PET/CT)‐derived data, including quantitative metrics, image texture analysis and other clinical risk factors, we aimed to develop a prognostic model that predicts survival in patients with previously untreated diffuse large B‐cell lymphoma (DLBCL) from GOYA (NCT01287741). Image texture features and clinical risk factors were combined into a random forest model and compared with the international prognostic index (IPI) for DLBCL based on progression‐free survival (PFS) and overall survival (OS) predictions. Baseline FDG‐PET scans were available for 1263 patients, 832 patients of these were cell‐of‐origin (COO)‐evaluable. Patients were stratified by IPI or radiomics features plus clinical risk factors into low‐, intermediate‐ and high‐risk groups. The random forest model with COO subgroups identified a clearer high‐risk population (45% 2‐year PFS [95% confidence interval (CI) 40%–52%]; 65% 2‐year OS [95% CI 59%–71%]) than the IPI (58% 2‐year PFS [95% CI 50%–67%]; 69% 2‐year OS [95% CI 62%–77%]). This study confirms that standard clinical risk factors can be combined with PET‐derived image texture features to provide an improved prognostic model predicting survival in untreated DLBCL.
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spelling pubmed-91756662022-07-14 A prognostic model integrating PET‐derived metrics and image texture analyses with clinical risk factors from GOYA Kostakoglu, Lale Dalmasso, Federico Berchialla, Paola Pierce, Larry A. Vitolo, Umberto Martelli, Maurizio Sehn, Laurie H. Trněný, Marek Nielsen, Tina G. Bolen, Christopher R. Sahin, Deniz Lee, Calvin El‐Galaly, Tarec Christoffer Mattiello, Federico Kinahan, Paul E. Chauvie, Stephane EJHaem Haematologic Malignancy ‐ Lymphoid Image texture analysis (radiomics) uses radiographic images to quantify characteristics that may identify tumour heterogeneity and associated patient outcomes. Using fluoro‐deoxy‐glucose positron emission tomography/computed tomography (FDG‐PET/CT)‐derived data, including quantitative metrics, image texture analysis and other clinical risk factors, we aimed to develop a prognostic model that predicts survival in patients with previously untreated diffuse large B‐cell lymphoma (DLBCL) from GOYA (NCT01287741). Image texture features and clinical risk factors were combined into a random forest model and compared with the international prognostic index (IPI) for DLBCL based on progression‐free survival (PFS) and overall survival (OS) predictions. Baseline FDG‐PET scans were available for 1263 patients, 832 patients of these were cell‐of‐origin (COO)‐evaluable. Patients were stratified by IPI or radiomics features plus clinical risk factors into low‐, intermediate‐ and high‐risk groups. The random forest model with COO subgroups identified a clearer high‐risk population (45% 2‐year PFS [95% confidence interval (CI) 40%–52%]; 65% 2‐year OS [95% CI 59%–71%]) than the IPI (58% 2‐year PFS [95% CI 50%–67%]; 69% 2‐year OS [95% CI 62%–77%]). This study confirms that standard clinical risk factors can be combined with PET‐derived image texture features to provide an improved prognostic model predicting survival in untreated DLBCL. John Wiley and Sons Inc. 2022-03-24 /pmc/articles/PMC9175666/ /pubmed/35846039 http://dx.doi.org/10.1002/jha2.421 Text en © 2022 The Authors. eJHaem published by British Society for Haematology and John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Haematologic Malignancy ‐ Lymphoid
Kostakoglu, Lale
Dalmasso, Federico
Berchialla, Paola
Pierce, Larry A.
Vitolo, Umberto
Martelli, Maurizio
Sehn, Laurie H.
Trněný, Marek
Nielsen, Tina G.
Bolen, Christopher R.
Sahin, Deniz
Lee, Calvin
El‐Galaly, Tarec Christoffer
Mattiello, Federico
Kinahan, Paul E.
Chauvie, Stephane
A prognostic model integrating PET‐derived metrics and image texture analyses with clinical risk factors from GOYA
title A prognostic model integrating PET‐derived metrics and image texture analyses with clinical risk factors from GOYA
title_full A prognostic model integrating PET‐derived metrics and image texture analyses with clinical risk factors from GOYA
title_fullStr A prognostic model integrating PET‐derived metrics and image texture analyses with clinical risk factors from GOYA
title_full_unstemmed A prognostic model integrating PET‐derived metrics and image texture analyses with clinical risk factors from GOYA
title_short A prognostic model integrating PET‐derived metrics and image texture analyses with clinical risk factors from GOYA
title_sort prognostic model integrating pet‐derived metrics and image texture analyses with clinical risk factors from goya
topic Haematologic Malignancy ‐ Lymphoid
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9175666/
https://www.ncbi.nlm.nih.gov/pubmed/35846039
http://dx.doi.org/10.1002/jha2.421
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