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Use Test of Automated Machine Learning in Cancer Diagnostics

Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status, we analyze whether it is possible to use AutoML t...

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Autores principales: Musigmann, Manfred, Nacul, Nabila Gala, Kasap, Dilek N., Heindel, Walter, Mannil, Manoj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378334/
https://www.ncbi.nlm.nih.gov/pubmed/37510059
http://dx.doi.org/10.3390/diagnostics13142315
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author Musigmann, Manfred
Nacul, Nabila Gala
Kasap, Dilek N.
Heindel, Walter
Mannil, Manoj
author_facet Musigmann, Manfred
Nacul, Nabila Gala
Kasap, Dilek N.
Heindel, Walter
Mannil, Manoj
author_sort Musigmann, Manfred
collection PubMed
description Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status, we analyze whether it is possible to use AutoML to develop models that are comparable in performance to conventional machine learning models (ML) developed by experts. For this purpose, we develop AutoML models using different feature preselection methods and compare the results with previously developed conventional ML models. The cohort used for our study comprises T2-weighted MRI images of 124 patients with histologically confirmed gliomas. Using AutoML, we were able to develop sophisticated models in a very short time with only a few lines of computer code. In predicting IDH mutation status, we obtained a mean AUC of 0.7400 and a mean AUPRC of 0.8582. ATRX mutation status was predicted with very similar discriminatory power, with a mean AUC of 0.7810 and a mean AUPRC of 0.8511. In both cases, AutoML was even able to achieve a discriminatory power slightly above that of the respective conventionally developed models in a very short computing time, thus making such methods accessible to non-experts in the near future.
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spelling pubmed-103783342023-07-29 Use Test of Automated Machine Learning in Cancer Diagnostics Musigmann, Manfred Nacul, Nabila Gala Kasap, Dilek N. Heindel, Walter Mannil, Manoj Diagnostics (Basel) Article Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status, we analyze whether it is possible to use AutoML to develop models that are comparable in performance to conventional machine learning models (ML) developed by experts. For this purpose, we develop AutoML models using different feature preselection methods and compare the results with previously developed conventional ML models. The cohort used for our study comprises T2-weighted MRI images of 124 patients with histologically confirmed gliomas. Using AutoML, we were able to develop sophisticated models in a very short time with only a few lines of computer code. In predicting IDH mutation status, we obtained a mean AUC of 0.7400 and a mean AUPRC of 0.8582. ATRX mutation status was predicted with very similar discriminatory power, with a mean AUC of 0.7810 and a mean AUPRC of 0.8511. In both cases, AutoML was even able to achieve a discriminatory power slightly above that of the respective conventionally developed models in a very short computing time, thus making such methods accessible to non-experts in the near future. MDPI 2023-07-08 /pmc/articles/PMC10378334/ /pubmed/37510059 http://dx.doi.org/10.3390/diagnostics13142315 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
Musigmann, Manfred
Nacul, Nabila Gala
Kasap, Dilek N.
Heindel, Walter
Mannil, Manoj
Use Test of Automated Machine Learning in Cancer Diagnostics
title Use Test of Automated Machine Learning in Cancer Diagnostics
title_full Use Test of Automated Machine Learning in Cancer Diagnostics
title_fullStr Use Test of Automated Machine Learning in Cancer Diagnostics
title_full_unstemmed Use Test of Automated Machine Learning in Cancer Diagnostics
title_short Use Test of Automated Machine Learning in Cancer Diagnostics
title_sort use test of automated machine learning in cancer diagnostics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378334/
https://www.ncbi.nlm.nih.gov/pubmed/37510059
http://dx.doi.org/10.3390/diagnostics13142315
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