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External Validation of a Convolutional Neural Network for IDH Mutation Prediction
Background and Objectives: The IDH (isocitrate dehydrogenase) status represents one of the main prognosis factors for gliomas. However, determining it requires invasive procedures and specialized surgical skills. Medical imaging such as MRI is essential in glioma diagnosis and management. Lately, fi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025144/ https://www.ncbi.nlm.nih.gov/pubmed/35454365 http://dx.doi.org/10.3390/medicina58040526 |
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author | Hrapșa, Iona Florian, Ioan Alexandru Șușman, Sergiu Farcaș, Marius Beni, Lehel Florian, Ioan Stefan |
author_facet | Hrapșa, Iona Florian, Ioan Alexandru Șușman, Sergiu Farcaș, Marius Beni, Lehel Florian, Ioan Stefan |
author_sort | Hrapșa, Iona |
collection | PubMed |
description | Background and Objectives: The IDH (isocitrate dehydrogenase) status represents one of the main prognosis factors for gliomas. However, determining it requires invasive procedures and specialized surgical skills. Medical imaging such as MRI is essential in glioma diagnosis and management. Lately, fields such as Radiomics and Radiogenomics emerged as pertinent prediction tools for extracting molecular information out of medical images. These fields are based on Artificial Intelligence algorithms that require external validation in order to evaluate their general performance. The aim of this study was to provide an external validation for the algorithm formulated by Yoon Choi et al. of IDH status prediction using preoperative common MRI sequences and patient age. Material and Methods: We applied Choi’s IDH status prediction algorithm on T1c, T2 and FLAIR preoperative MRI images of gliomas (grades WHO II-IV) of 21 operated adult patients from the Neurosurgery clinic of the Cluj County Emergency Clinical Hospital (CCECH), Cluj-Napoca Romania. We created a script to automate the testing process with DICOM format MRI sequences as input and IDH predicted status as output. Results: In terms of patient characteristics, the mean age was 48.6 ± 15.6; 57% were female and 43% male; 43% were IDH positive and 57% IDH negative. The proportions of WHO grades were 24%, 14% and 62% for II, III and IV, respectively. The validation test achieved a relative accuracy of 76% with 95% CI of (53%, 92%) and an Area Under the Curve (AUC) through DeLong et al. method of 0.74 with 95% CI of (0.53, 0.91) and a p of 0.021. Sensitivity and Specificity were 0.78 with 95% CI of (0.45, 0.96) and 0.75 with 95% CI of (0.47, 0.91), respectively. Conclusions: Although our results match the external test the author made on The Cancer Imaging Archive (TCIA) online dataset, performance of the algorithm on external data is still not high enough for clinical application. Radiogenomic approaches remain a high interest research field that may provide a rapid and accurate diagnosis and prognosis of patients with intracranial glioma. |
format | Online Article Text |
id | pubmed-9025144 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90251442022-04-23 External Validation of a Convolutional Neural Network for IDH Mutation Prediction Hrapșa, Iona Florian, Ioan Alexandru Șușman, Sergiu Farcaș, Marius Beni, Lehel Florian, Ioan Stefan Medicina (Kaunas) Article Background and Objectives: The IDH (isocitrate dehydrogenase) status represents one of the main prognosis factors for gliomas. However, determining it requires invasive procedures and specialized surgical skills. Medical imaging such as MRI is essential in glioma diagnosis and management. Lately, fields such as Radiomics and Radiogenomics emerged as pertinent prediction tools for extracting molecular information out of medical images. These fields are based on Artificial Intelligence algorithms that require external validation in order to evaluate their general performance. The aim of this study was to provide an external validation for the algorithm formulated by Yoon Choi et al. of IDH status prediction using preoperative common MRI sequences and patient age. Material and Methods: We applied Choi’s IDH status prediction algorithm on T1c, T2 and FLAIR preoperative MRI images of gliomas (grades WHO II-IV) of 21 operated adult patients from the Neurosurgery clinic of the Cluj County Emergency Clinical Hospital (CCECH), Cluj-Napoca Romania. We created a script to automate the testing process with DICOM format MRI sequences as input and IDH predicted status as output. Results: In terms of patient characteristics, the mean age was 48.6 ± 15.6; 57% were female and 43% male; 43% were IDH positive and 57% IDH negative. The proportions of WHO grades were 24%, 14% and 62% for II, III and IV, respectively. The validation test achieved a relative accuracy of 76% with 95% CI of (53%, 92%) and an Area Under the Curve (AUC) through DeLong et al. method of 0.74 with 95% CI of (0.53, 0.91) and a p of 0.021. Sensitivity and Specificity were 0.78 with 95% CI of (0.45, 0.96) and 0.75 with 95% CI of (0.47, 0.91), respectively. Conclusions: Although our results match the external test the author made on The Cancer Imaging Archive (TCIA) online dataset, performance of the algorithm on external data is still not high enough for clinical application. Radiogenomic approaches remain a high interest research field that may provide a rapid and accurate diagnosis and prognosis of patients with intracranial glioma. MDPI 2022-04-09 /pmc/articles/PMC9025144/ /pubmed/35454365 http://dx.doi.org/10.3390/medicina58040526 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 Hrapșa, Iona Florian, Ioan Alexandru Șușman, Sergiu Farcaș, Marius Beni, Lehel Florian, Ioan Stefan External Validation of a Convolutional Neural Network for IDH Mutation Prediction |
title | External Validation of a Convolutional Neural Network for IDH Mutation Prediction |
title_full | External Validation of a Convolutional Neural Network for IDH Mutation Prediction |
title_fullStr | External Validation of a Convolutional Neural Network for IDH Mutation Prediction |
title_full_unstemmed | External Validation of a Convolutional Neural Network for IDH Mutation Prediction |
title_short | External Validation of a Convolutional Neural Network for IDH Mutation Prediction |
title_sort | external validation of a convolutional neural network for idh mutation prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025144/ https://www.ncbi.nlm.nih.gov/pubmed/35454365 http://dx.doi.org/10.3390/medicina58040526 |
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