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Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment

(1) Background: As outcome of patients with metastatic melanoma treated with anti-PD1 immunotherapy can vary in success, predictors are needed. We aimed to predict at the patients’ levels, overall survival (OS) and progression-free survival (PFS) after one year of immunotherapy, based on their pre-t...

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Autores principales: Flaus, Anthime, Habouzit, Vincent, de Leiris, Nicolas, Vuillez, Jean-Philippe, Leccia, Marie-Thérèse, Simonson, Mathilde, Perrot, Jean-Luc, Cachin, Florent, Prevot, Nathalie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870749/
https://www.ncbi.nlm.nih.gov/pubmed/35204479
http://dx.doi.org/10.3390/diagnostics12020388
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author Flaus, Anthime
Habouzit, Vincent
de Leiris, Nicolas
Vuillez, Jean-Philippe
Leccia, Marie-Thérèse
Simonson, Mathilde
Perrot, Jean-Luc
Cachin, Florent
Prevot, Nathalie
author_facet Flaus, Anthime
Habouzit, Vincent
de Leiris, Nicolas
Vuillez, Jean-Philippe
Leccia, Marie-Thérèse
Simonson, Mathilde
Perrot, Jean-Luc
Cachin, Florent
Prevot, Nathalie
author_sort Flaus, Anthime
collection PubMed
description (1) Background: As outcome of patients with metastatic melanoma treated with anti-PD1 immunotherapy can vary in success, predictors are needed. We aimed to predict at the patients’ levels, overall survival (OS) and progression-free survival (PFS) after one year of immunotherapy, based on their pre-treatment 18F-FDG PET; (2) Methods: Fifty-six metastatic melanoma patients—without prior systemic treatment—were retrospectively included. Forty-five 18F-FDG PET-based radiomic features were computed and the top five features associated with the patient’s outcome were selected. The analyzed machine learning classifiers were random forest (RF), neural network, naive Bayes, logistic regression and support vector machine. The receiver operating characteristic curve was used to compare model performances, which were validated by cross-validation; (3) Results: The RF model obtained the best performance after validation to predict OS and PFS and presented AUC, sensitivities and specificities (IC95%) of 0.87 ± 0.1, 0.79 ± 0.11 and 0.95 ± 0.06 for OS and 0.9 ± 0.07, 0.88 ± 0.09 and 0.91 ± 0.08 for PFS, respectively. (4) Conclusion: A RF classifier, based on pretreatment 18F-FDG PET radiomic features may be useful for predicting the survival status for melanoma patients, after one year of a first line systemic treatment by immunotherapy.
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spelling pubmed-88707492022-02-25 Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment Flaus, Anthime Habouzit, Vincent de Leiris, Nicolas Vuillez, Jean-Philippe Leccia, Marie-Thérèse Simonson, Mathilde Perrot, Jean-Luc Cachin, Florent Prevot, Nathalie Diagnostics (Basel) Article (1) Background: As outcome of patients with metastatic melanoma treated with anti-PD1 immunotherapy can vary in success, predictors are needed. We aimed to predict at the patients’ levels, overall survival (OS) and progression-free survival (PFS) after one year of immunotherapy, based on their pre-treatment 18F-FDG PET; (2) Methods: Fifty-six metastatic melanoma patients—without prior systemic treatment—were retrospectively included. Forty-five 18F-FDG PET-based radiomic features were computed and the top five features associated with the patient’s outcome were selected. The analyzed machine learning classifiers were random forest (RF), neural network, naive Bayes, logistic regression and support vector machine. The receiver operating characteristic curve was used to compare model performances, which were validated by cross-validation; (3) Results: The RF model obtained the best performance after validation to predict OS and PFS and presented AUC, sensitivities and specificities (IC95%) of 0.87 ± 0.1, 0.79 ± 0.11 and 0.95 ± 0.06 for OS and 0.9 ± 0.07, 0.88 ± 0.09 and 0.91 ± 0.08 for PFS, respectively. (4) Conclusion: A RF classifier, based on pretreatment 18F-FDG PET radiomic features may be useful for predicting the survival status for melanoma patients, after one year of a first line systemic treatment by immunotherapy. MDPI 2022-02-02 /pmc/articles/PMC8870749/ /pubmed/35204479 http://dx.doi.org/10.3390/diagnostics12020388 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
Flaus, Anthime
Habouzit, Vincent
de Leiris, Nicolas
Vuillez, Jean-Philippe
Leccia, Marie-Thérèse
Simonson, Mathilde
Perrot, Jean-Luc
Cachin, Florent
Prevot, Nathalie
Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment
title Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment
title_full Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment
title_fullStr Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment
title_full_unstemmed Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment
title_short Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment
title_sort outcome prediction at patient level derived from pre-treatment 18f-fdg pet due to machine learning in metastatic melanoma treated with anti-pd1 treatment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870749/
https://www.ncbi.nlm.nih.gov/pubmed/35204479
http://dx.doi.org/10.3390/diagnostics12020388
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