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FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy

SIMPLE SUMMARY: In this study, we strive to identify clinically relevant image feature (IF) changes during chemoradiation in patients with non-small-cell lung cancer (NSCLC) to be able to predict tumor responses in an early stage of treatment. All patients underwent static (3D) and respiratory-gated...

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Autores principales: Carles, Montserrat, Fechter, Tobias, Radicioni, Gianluca, Schimek-Jasch, Tanja, Adebahr, Sonja, Zamboglou, Constantinos, Nicolay, Nils H., Martí-Bonmatí, Luis, Nestle, Ursula, Grosu, Anca L., Baltas, Dimos, Mix, Michael, Gkika, Eleni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7919471/
https://www.ncbi.nlm.nih.gov/pubmed/33672052
http://dx.doi.org/10.3390/cancers13040814
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author Carles, Montserrat
Fechter, Tobias
Radicioni, Gianluca
Schimek-Jasch, Tanja
Adebahr, Sonja
Zamboglou, Constantinos
Nicolay, Nils H.
Martí-Bonmatí, Luis
Nestle, Ursula
Grosu, Anca L.
Baltas, Dimos
Mix, Michael
Gkika, Eleni
author_facet Carles, Montserrat
Fechter, Tobias
Radicioni, Gianluca
Schimek-Jasch, Tanja
Adebahr, Sonja
Zamboglou, Constantinos
Nicolay, Nils H.
Martí-Bonmatí, Luis
Nestle, Ursula
Grosu, Anca L.
Baltas, Dimos
Mix, Michael
Gkika, Eleni
author_sort Carles, Montserrat
collection PubMed
description SIMPLE SUMMARY: In this study, we strive to identify clinically relevant image feature (IF) changes during chemoradiation in patients with non-small-cell lung cancer (NSCLC) to be able to predict tumor responses in an early stage of treatment. All patients underwent static (3D) and respiratory-gated 4D PET/CT scans before treatment and a 3D scan during or after treatment. Our proposed method rejects IF changes due to intrinsic variability such as noise, resolution and movement through breathing. The IF variability observed across 4D PET is employed as a patient individualized normalization factor to emphasize statistically relevant IF changes during treatment. ABSTRACT: The aim of this study is to identify clinically relevant image feature (IF) changes during chemoradiation and evaluate their efficacy in predicting treatment response. Patients with non-small-cell lung cancer (NSCLC) were enrolled in two prospective trials (STRIPE, PET-Plan). We evaluated 48 patients who underwent static (3D) and retrospectively-respiratory-gated 4D PET/CT scans before treatment and a 3D scan during or after treatment. Our proposed method rejects IF changes due to intrinsic variability. The IF variability observed across 4D PET is employed as a patient individualized normalization factor to emphasize statistically relevant IF changes during treatment. Predictions of overall survival (OS), local recurrence (LR) and distant metastasis (DM) were evaluated. From 135 IFs, only 17 satisfied the required criteria of being normally distributed across 4D PET and robust between 3D and 4D images. Changes during treatment in the area-under-the-curve of the cumulative standard-uptake-value histogram (δ(AUC(CSH))) within primary tumor discriminated (AUC = 0.87, Specificity = 0.78) patients with and without LR. The resulted prognostic model was validated with a different segmentation method (AUC = 0.83) and in a different patient cohort (AUC = 0.63). The quantification of tumor FDG heterogeneity by δ(AUC(CSH)) during chemoradiation correlated with the incidence of local recurrence and might be recommended for monitoring treatment response in patients with NSCLC.
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spelling pubmed-79194712021-03-02 FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy Carles, Montserrat Fechter, Tobias Radicioni, Gianluca Schimek-Jasch, Tanja Adebahr, Sonja Zamboglou, Constantinos Nicolay, Nils H. Martí-Bonmatí, Luis Nestle, Ursula Grosu, Anca L. Baltas, Dimos Mix, Michael Gkika, Eleni Cancers (Basel) Article SIMPLE SUMMARY: In this study, we strive to identify clinically relevant image feature (IF) changes during chemoradiation in patients with non-small-cell lung cancer (NSCLC) to be able to predict tumor responses in an early stage of treatment. All patients underwent static (3D) and respiratory-gated 4D PET/CT scans before treatment and a 3D scan during or after treatment. Our proposed method rejects IF changes due to intrinsic variability such as noise, resolution and movement through breathing. The IF variability observed across 4D PET is employed as a patient individualized normalization factor to emphasize statistically relevant IF changes during treatment. ABSTRACT: The aim of this study is to identify clinically relevant image feature (IF) changes during chemoradiation and evaluate their efficacy in predicting treatment response. Patients with non-small-cell lung cancer (NSCLC) were enrolled in two prospective trials (STRIPE, PET-Plan). We evaluated 48 patients who underwent static (3D) and retrospectively-respiratory-gated 4D PET/CT scans before treatment and a 3D scan during or after treatment. Our proposed method rejects IF changes due to intrinsic variability. The IF variability observed across 4D PET is employed as a patient individualized normalization factor to emphasize statistically relevant IF changes during treatment. Predictions of overall survival (OS), local recurrence (LR) and distant metastasis (DM) were evaluated. From 135 IFs, only 17 satisfied the required criteria of being normally distributed across 4D PET and robust between 3D and 4D images. Changes during treatment in the area-under-the-curve of the cumulative standard-uptake-value histogram (δ(AUC(CSH))) within primary tumor discriminated (AUC = 0.87, Specificity = 0.78) patients with and without LR. The resulted prognostic model was validated with a different segmentation method (AUC = 0.83) and in a different patient cohort (AUC = 0.63). The quantification of tumor FDG heterogeneity by δ(AUC(CSH)) during chemoradiation correlated with the incidence of local recurrence and might be recommended for monitoring treatment response in patients with NSCLC. MDPI 2021-02-15 /pmc/articles/PMC7919471/ /pubmed/33672052 http://dx.doi.org/10.3390/cancers13040814 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Carles, Montserrat
Fechter, Tobias
Radicioni, Gianluca
Schimek-Jasch, Tanja
Adebahr, Sonja
Zamboglou, Constantinos
Nicolay, Nils H.
Martí-Bonmatí, Luis
Nestle, Ursula
Grosu, Anca L.
Baltas, Dimos
Mix, Michael
Gkika, Eleni
FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy
title FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy
title_full FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy
title_fullStr FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy
title_full_unstemmed FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy
title_short FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy
title_sort fdg-pet radiomics for response monitoring in non-small-cell lung cancer treated with radiation therapy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7919471/
https://www.ncbi.nlm.nih.gov/pubmed/33672052
http://dx.doi.org/10.3390/cancers13040814
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