Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images

Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images

Abstract We developed end-to-end deep learning models using whole slide images of adults diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to predict prognosis and the mutation status of a somatic biomarker, isocitrate dehydrogenase (IDH) 1/2. The models, which u...

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Autores principales: Shuai Jiang, George J. Zanazzi, Saeed Hassanpour
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
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Science
Q
Acceso en línea:https://doaj.org/article/f7a305b6c0774af5b9b9e09e00f7d2d9
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spelling oai:doaj.org-article:f7a305b6c0774af5b9b9e09e00f7d2d92021-12-02T18:51:47ZPredicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images10.1038/s41598-021-95948-x2045-2322https://doaj.org/article/f7a305b6c0774af5b9b9e09e00f7d2d92021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95948-xhttps://doaj.org/toc/2045-2322Abstract We developed end-to-end deep learning models using whole slide images of adults diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to predict prognosis and the mutation status of a somatic biomarker, isocitrate dehydrogenase (IDH) 1/2. The models, which utilize ResNet-18 as a backbone, were developed and validated on 296 patients from The Cancer Genome Atlas (TCGA) database. To account for the small sample size, repeated random train/test splits were performed for hyperparameter tuning, and the out-of-sample predictions were pooled for evaluation. Our models achieved a concordance- (C-) index of 0.715 (95% CI: 0.569, 0.830) for predicting prognosis and an area under the curve (AUC) of 0.667 (0.532, 0.784) for predicting IDH mutations. When combined with additional clinical information, the performance metrics increased to 0.784 (95% CI: 0.655, 0.880) and 0.739 (95% CI: 0.613, 0.856), respectively. When evaluated on the WHO grade 3 gliomas from the TCGA dataset, which were not used for training, our models predicted survival with a C-index of 0.654 (95% CI: 0.537, 0.768) and IDH mutations with an AUC of 0.814 (95% CI: 0.721, 0.897). If validated in a prospective study, our method could potentially assist clinicians in managing and treating patients with diffusely infiltrating gliomas.Shuai JiangGeorge J. ZanazziSaeed HassanpourNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Shuai Jiang
George J. Zanazzi
Saeed Hassanpour
Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images
description Abstract We developed end-to-end deep learning models using whole slide images of adults diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to predict prognosis and the mutation status of a somatic biomarker, isocitrate dehydrogenase (IDH) 1/2. The models, which utilize ResNet-18 as a backbone, were developed and validated on 296 patients from The Cancer Genome Atlas (TCGA) database. To account for the small sample size, repeated random train/test splits were performed for hyperparameter tuning, and the out-of-sample predictions were pooled for evaluation. Our models achieved a concordance- (C-) index of 0.715 (95% CI: 0.569, 0.830) for predicting prognosis and an area under the curve (AUC) of 0.667 (0.532, 0.784) for predicting IDH mutations. When combined with additional clinical information, the performance metrics increased to 0.784 (95% CI: 0.655, 0.880) and 0.739 (95% CI: 0.613, 0.856), respectively. When evaluated on the WHO grade 3 gliomas from the TCGA dataset, which were not used for training, our models predicted survival with a C-index of 0.654 (95% CI: 0.537, 0.768) and IDH mutations with an AUC of 0.814 (95% CI: 0.721, 0.897). If validated in a prospective study, our method could potentially assist clinicians in managing and treating patients with diffusely infiltrating gliomas.
format article
author Shuai Jiang
George J. Zanazzi
Saeed Hassanpour
author_facet Shuai Jiang
George J. Zanazzi
Saeed Hassanpour
author_sort Shuai Jiang
title Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images
title_short Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images
title_full Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images
title_fullStr Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images
title_full_unstemmed Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images
title_sort predicting prognosis and idh mutation status for patients with lower-grade gliomas using whole slide images
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/f7a305b6c0774af5b9b9e09e00f7d2d9
work_keys_str_mv AT shuaijiang predictingprognosisandidhmutationstatusforpatientswithlowergradegliomasusingwholeslideimages
AT georgejzanazzi predictingprognosisandidhmutationstatusforpatientswithlowergradegliomasusingwholeslideimages
AT saeedhassanpour predictingprognosisandidhmutationstatusforpatientswithlowergradegliomasusingwholeslideimages
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