Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning
<p dir="ltr">Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma,...
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2020
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| _version_ | 1864513513396895744 |
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| author | Sidong Liu (1901311) |
| author2 | Zubair Shah (231886) Aydin Sav (2428) Carlo Russo (7259) Shlomo Berkovsky (18622984) Yi Qian (466487) Enrico Coiera (64865) Antonio Di Ieva (14943) |
| author2_role | author author author author author author author |
| author_facet | Sidong Liu (1901311) Zubair Shah (231886) Aydin Sav (2428) Carlo Russo (7259) Shlomo Berkovsky (18622984) Yi Qian (466487) Enrico Coiera (64865) Antonio Di Ieva (14943) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sidong Liu (1901311) Zubair Shah (231886) Aydin Sav (2428) Carlo Russo (7259) Shlomo Berkovsky (18622984) Yi Qian (466487) Enrico Coiera (64865) Antonio Di Ieva (14943) |
| dc.date.none.fl_str_mv | 2020-05-07T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-020-64588-y |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Isocitrate_dehydrogenase_IDH_status_prediction_in_histopathology_images_of_gliomas_using_deep_learning/25911478 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Oncology and carcinogenesis Engineering Biomedical engineering Information and computing sciences Machine learning Adult Deep Learning Glioma Humans Isocitrate Dehydrogenase Magnetic Resonance Imaging Middle Aged Mutation Prognosis |
| dc.title.none.fl_str_mv | Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematoxylin and eosin (H&E) staining is a valuable tool in precision oncology as it guides histopathology-based diagnosis and proceeding patient’s treatment. However, H&E staining alone does not determine the IDH mutational status of a tumour. Deep learning methods applied to MRI data have been demonstrated to be a useful tool in IDH status prediction, however the effectiveness of deep learning on H&E slides in the clinical setting has not been investigated so far. Furthermore, the performance of deep learning methods in medical imaging has been practically limited by small sample sizes currently available. Here we propose a data augmentation method based on the Generative Adversarial Networks (GAN) deep learning methodology, to improve the prediction performance of IDH mutational status using H&E slides. The H&E slides were acquired from 266 grade II-IV glioma patients from a mixture of public and private databases, including 130 IDH-wildtype and 136 IDH-mutant patients. A baseline deep learning model without data augmentation achieved an accuracy of 0.794 (AUC = 0.920). With GAN-based data augmentation, the accuracy of the IDH mutational status prediction was improved to 0.853 (AUC = 0.927) when the 3,000 GAN generated training samples were added to the original training set (24,000 samples). By integrating also patients’ age into the model, the accuracy improved further to 0.882 (AUC = 0.931). Our findings show that deep learning methodology, enhanced by GAN data augmentation, can support physicians in gliomas’ IDH status prediction.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-020-64588-y" target="_blank">https://dx.doi.org/10.1038/s41598-020-64588-y</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8daeaa54020ca68c08579dc3c3fc8516 |
| identifier_str_mv | 10.1038/s41598-020-64588-y |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25911478 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learningSidong Liu (1901311)Zubair Shah (231886)Aydin Sav (2428)Carlo Russo (7259)Shlomo Berkovsky (18622984)Yi Qian (466487)Enrico Coiera (64865)Antonio Di Ieva (14943)Biomedical and clinical sciencesClinical sciencesOncology and carcinogenesisEngineeringBiomedical engineeringInformation and computing sciencesMachine learningAdultDeep LearningGliomaHumansIsocitrate DehydrogenaseMagnetic Resonance ImagingMiddle AgedMutationPrognosis<p dir="ltr">Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematoxylin and eosin (H&E) staining is a valuable tool in precision oncology as it guides histopathology-based diagnosis and proceeding patient’s treatment. However, H&E staining alone does not determine the IDH mutational status of a tumour. Deep learning methods applied to MRI data have been demonstrated to be a useful tool in IDH status prediction, however the effectiveness of deep learning on H&E slides in the clinical setting has not been investigated so far. Furthermore, the performance of deep learning methods in medical imaging has been practically limited by small sample sizes currently available. Here we propose a data augmentation method based on the Generative Adversarial Networks (GAN) deep learning methodology, to improve the prediction performance of IDH mutational status using H&E slides. The H&E slides were acquired from 266 grade II-IV glioma patients from a mixture of public and private databases, including 130 IDH-wildtype and 136 IDH-mutant patients. A baseline deep learning model without data augmentation achieved an accuracy of 0.794 (AUC = 0.920). With GAN-based data augmentation, the accuracy of the IDH mutational status prediction was improved to 0.853 (AUC = 0.927) when the 3,000 GAN generated training samples were added to the original training set (24,000 samples). By integrating also patients’ age into the model, the accuracy improved further to 0.882 (AUC = 0.931). Our findings show that deep learning methodology, enhanced by GAN data augmentation, can support physicians in gliomas’ IDH status prediction.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-020-64588-y" target="_blank">https://dx.doi.org/10.1038/s41598-020-64588-y</a></p>2020-05-07T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-020-64588-yhttps://figshare.com/articles/journal_contribution/Isocitrate_dehydrogenase_IDH_status_prediction_in_histopathology_images_of_gliomas_using_deep_learning/25911478CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/259114782020-05-07T12:00:00Z |
| spellingShingle | Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning Sidong Liu (1901311) Biomedical and clinical sciences Clinical sciences Oncology and carcinogenesis Engineering Biomedical engineering Information and computing sciences Machine learning Adult Deep Learning Glioma Humans Isocitrate Dehydrogenase Magnetic Resonance Imaging Middle Aged Mutation Prognosis |
| status_str | publishedVersion |
| title | Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning |
| title_full | Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning |
| title_fullStr | Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning |
| title_full_unstemmed | Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning |
| title_short | Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning |
| title_sort | Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning |
| topic | Biomedical and clinical sciences Clinical sciences Oncology and carcinogenesis Engineering Biomedical engineering Information and computing sciences Machine learning Adult Deep Learning Glioma Humans Isocitrate Dehydrogenase Magnetic Resonance Imaging Middle Aged Mutation Prognosis |