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Development and external validation of clinical prediction models for pituitary surgery
INTRODUCTION: Gross total resection (GTR), Biochemical Remission (BR) and restitution of a priorly disrupted hypothalamus pituitary axis (new improvement, IMP) are important factors in pituitary adenoma (PA) resection surgery. Prediction of these metrics using simple and preoperatively available dat...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668061/ https://www.ncbi.nlm.nih.gov/pubmed/38020983 http://dx.doi.org/10.1016/j.bas.2023.102668 |
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author | Zanier, Olivier Zoli, Matteo Staartjes, Victor E. Alalfi, Mohammed O. Guaraldi, Federica Asioli, Sofia Rustici, Arianna Pasquini, Ernesto Faustini-Fustini, Marco Erlic, Zoran Hugelshofer, Michael Voglis, Stefanos Regli, Luca Mazzatenta, Diego Serra, Carlo |
author_facet | Zanier, Olivier Zoli, Matteo Staartjes, Victor E. Alalfi, Mohammed O. Guaraldi, Federica Asioli, Sofia Rustici, Arianna Pasquini, Ernesto Faustini-Fustini, Marco Erlic, Zoran Hugelshofer, Michael Voglis, Stefanos Regli, Luca Mazzatenta, Diego Serra, Carlo |
author_sort | Zanier, Olivier |
collection | PubMed |
description | INTRODUCTION: Gross total resection (GTR), Biochemical Remission (BR) and restitution of a priorly disrupted hypothalamus pituitary axis (new improvement, IMP) are important factors in pituitary adenoma (PA) resection surgery. Prediction of these metrics using simple and preoperatively available data might help improve patient care and contribute to a more personalized medicine. RESEARCH QUESTION: This study aims to develop machine learning models predicting GTR, BR, and IMP in PA resection surgery, using preoperatively available data. MATERIAL AND METHODS: With data from patients undergoing endoscopic transsphenoidal surgery for PAs machine learning models for prediction of GTR, BR and IMP were developed and externally validated. Development was carried out on a registry from Bologna, Italy while external validation was conducted using patient data from Zurich, Switzerland. RESULTS: The model development cohort consisted of 1203 patients. GTR was achieved in 207 (17.2%, 945 (78.6%) missing), BR in 173 (14.4%, 992 (82.5%) missing) and IMP in 208 (17.3%, 167 (13.9%) missing) cases. In the external validation cohort 206 patients were included and GTR was achieved in 121 (58.7%, 32 (15.5%) missing), BR in 46 (22.3%, 145 (70.4%) missing) and IMP in 42 (20.4%, 7 (3.4%) missing) cases. The AUC at external validation amounted to 0.72 (95% CI: 0.63–0.80) for GTR, 0.69 (0.52–0.83) for BR, as well as 0.82 (0.76–0.89) for IMP. DISCUSSION AND CONCLUSION: All models showed adequate generalizability, performing similarly in training and external validation, confirming the possible potentials of machine learning in helping to adapt surgical therapy to the individual patient. |
format | Online Article Text |
id | pubmed-10668061 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-106680612023-08-28 Development and external validation of clinical prediction models for pituitary surgery Zanier, Olivier Zoli, Matteo Staartjes, Victor E. Alalfi, Mohammed O. Guaraldi, Federica Asioli, Sofia Rustici, Arianna Pasquini, Ernesto Faustini-Fustini, Marco Erlic, Zoran Hugelshofer, Michael Voglis, Stefanos Regli, Luca Mazzatenta, Diego Serra, Carlo Brain Spine Article INTRODUCTION: Gross total resection (GTR), Biochemical Remission (BR) and restitution of a priorly disrupted hypothalamus pituitary axis (new improvement, IMP) are important factors in pituitary adenoma (PA) resection surgery. Prediction of these metrics using simple and preoperatively available data might help improve patient care and contribute to a more personalized medicine. RESEARCH QUESTION: This study aims to develop machine learning models predicting GTR, BR, and IMP in PA resection surgery, using preoperatively available data. MATERIAL AND METHODS: With data from patients undergoing endoscopic transsphenoidal surgery for PAs machine learning models for prediction of GTR, BR and IMP were developed and externally validated. Development was carried out on a registry from Bologna, Italy while external validation was conducted using patient data from Zurich, Switzerland. RESULTS: The model development cohort consisted of 1203 patients. GTR was achieved in 207 (17.2%, 945 (78.6%) missing), BR in 173 (14.4%, 992 (82.5%) missing) and IMP in 208 (17.3%, 167 (13.9%) missing) cases. In the external validation cohort 206 patients were included and GTR was achieved in 121 (58.7%, 32 (15.5%) missing), BR in 46 (22.3%, 145 (70.4%) missing) and IMP in 42 (20.4%, 7 (3.4%) missing) cases. The AUC at external validation amounted to 0.72 (95% CI: 0.63–0.80) for GTR, 0.69 (0.52–0.83) for BR, as well as 0.82 (0.76–0.89) for IMP. DISCUSSION AND CONCLUSION: All models showed adequate generalizability, performing similarly in training and external validation, confirming the possible potentials of machine learning in helping to adapt surgical therapy to the individual patient. Elsevier 2023-08-28 /pmc/articles/PMC10668061/ /pubmed/38020983 http://dx.doi.org/10.1016/j.bas.2023.102668 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zanier, Olivier Zoli, Matteo Staartjes, Victor E. Alalfi, Mohammed O. Guaraldi, Federica Asioli, Sofia Rustici, Arianna Pasquini, Ernesto Faustini-Fustini, Marco Erlic, Zoran Hugelshofer, Michael Voglis, Stefanos Regli, Luca Mazzatenta, Diego Serra, Carlo Development and external validation of clinical prediction models for pituitary surgery |
title | Development and external validation of clinical prediction models for pituitary surgery |
title_full | Development and external validation of clinical prediction models for pituitary surgery |
title_fullStr | Development and external validation of clinical prediction models for pituitary surgery |
title_full_unstemmed | Development and external validation of clinical prediction models for pituitary surgery |
title_short | Development and external validation of clinical prediction models for pituitary surgery |
title_sort | development and external validation of clinical prediction models for pituitary surgery |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668061/ https://www.ncbi.nlm.nih.gov/pubmed/38020983 http://dx.doi.org/10.1016/j.bas.2023.102668 |
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