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An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) constitutes a leading cause of cancer-related mortality despite advances in detection and treatment methods. While computed tomography (CT) serves as the current gold standard for initial evaluation of PDAC, its prognostic value remains limited, as it relies o...

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Autores principales: Vezakis, Ioannis, Vezakis, Antonios, Gourtsoyianni, Sofia, Koutoulidis, Vassilis, Polydorou, Andreas A., Matsopoulos, George K., Koutsouris, Dimitrios D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10530933/
https://www.ncbi.nlm.nih.gov/pubmed/37761882
http://dx.doi.org/10.3390/genes14091742
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author Vezakis, Ioannis
Vezakis, Antonios
Gourtsoyianni, Sofia
Koutoulidis, Vassilis
Polydorou, Andreas A.
Matsopoulos, George K.
Koutsouris, Dimitrios D.
author_facet Vezakis, Ioannis
Vezakis, Antonios
Gourtsoyianni, Sofia
Koutoulidis, Vassilis
Polydorou, Andreas A.
Matsopoulos, George K.
Koutsouris, Dimitrios D.
author_sort Vezakis, Ioannis
collection PubMed
description Pancreatic ductal adenocarcinoma (PDAC) constitutes a leading cause of cancer-related mortality despite advances in detection and treatment methods. While computed tomography (CT) serves as the current gold standard for initial evaluation of PDAC, its prognostic value remains limited, as it relies on diagnostic stage parameters encompassing tumor size, lymph node involvement, and metastasis. Radiomics have recently shown promise in predicting postoperative survival of PDAC patients; however, they rely on manual pancreas and tumor delineation by clinicians. In this study, we collected a dataset of pre-operative CT scans from a cohort of 40 PDAC patients to evaluate a fully automated pipeline for survival prediction. Employing nnU-Net trained on an external dataset, we generated automated pancreas and tumor segmentations. Subsequently, we extracted 854 radiomic features from each segmentation, which we narrowed down to 29 via feature selection. We then combined these features with the Tumor, Node, Metastasis (TNM) system staging parameters, as well as the patient’s age. We trained a random survival forest model to perform an overall survival prediction over time, as well as a random forest classifier for the binary classification of two-year survival, using repeated cross-validation for evaluation. Our results exhibited promise, with a mean C-index of 0.731 for survival modeling and a mean accuracy of 0.76 in two-year survival prediction, providing evidence of the feasibility and potential efficacy of a fully automated pipeline for PDAC prognostication. By eliminating the labor-intensive manual segmentation process, our streamlined pipeline demonstrates an efficient and accurate prognostication process, laying the foundation for future research endeavors.
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spelling pubmed-105309332023-09-28 An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma Vezakis, Ioannis Vezakis, Antonios Gourtsoyianni, Sofia Koutoulidis, Vassilis Polydorou, Andreas A. Matsopoulos, George K. Koutsouris, Dimitrios D. Genes (Basel) Article Pancreatic ductal adenocarcinoma (PDAC) constitutes a leading cause of cancer-related mortality despite advances in detection and treatment methods. While computed tomography (CT) serves as the current gold standard for initial evaluation of PDAC, its prognostic value remains limited, as it relies on diagnostic stage parameters encompassing tumor size, lymph node involvement, and metastasis. Radiomics have recently shown promise in predicting postoperative survival of PDAC patients; however, they rely on manual pancreas and tumor delineation by clinicians. In this study, we collected a dataset of pre-operative CT scans from a cohort of 40 PDAC patients to evaluate a fully automated pipeline for survival prediction. Employing nnU-Net trained on an external dataset, we generated automated pancreas and tumor segmentations. Subsequently, we extracted 854 radiomic features from each segmentation, which we narrowed down to 29 via feature selection. We then combined these features with the Tumor, Node, Metastasis (TNM) system staging parameters, as well as the patient’s age. We trained a random survival forest model to perform an overall survival prediction over time, as well as a random forest classifier for the binary classification of two-year survival, using repeated cross-validation for evaluation. Our results exhibited promise, with a mean C-index of 0.731 for survival modeling and a mean accuracy of 0.76 in two-year survival prediction, providing evidence of the feasibility and potential efficacy of a fully automated pipeline for PDAC prognostication. By eliminating the labor-intensive manual segmentation process, our streamlined pipeline demonstrates an efficient and accurate prognostication process, laying the foundation for future research endeavors. MDPI 2023-08-31 /pmc/articles/PMC10530933/ /pubmed/37761882 http://dx.doi.org/10.3390/genes14091742 Text en © 2023 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
Vezakis, Ioannis
Vezakis, Antonios
Gourtsoyianni, Sofia
Koutoulidis, Vassilis
Polydorou, Andreas A.
Matsopoulos, George K.
Koutsouris, Dimitrios D.
An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma
title An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma
title_full An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma
title_fullStr An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma
title_full_unstemmed An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma
title_short An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma
title_sort automated prognostic model for pancreatic ductal adenocarcinoma
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10530933/
https://www.ncbi.nlm.nih.gov/pubmed/37761882
http://dx.doi.org/10.3390/genes14091742
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