Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features : model development and validation
We evaluated the diagnostic performance and generalizability of traditional machine learning and deep learning models for distinguishing glioblastoma from single brain metastasis using radiomics. The training and external validation cohorts comprised 166 (109 glioblastomas and 57 metastases) and 82 (50 glioblastomas and 32 metastases) patients, respectively. Two-hundred-and-sixty-five radiomic features were extracted from semiautomatically segmented regions on contrast-enhancing and peritumoral T2 hyperintense masks and used as input data. For each of a deep neural network (DNN) and seven traditional machine learning classifiers combined with one of five feature selection methods, hyperparameters were optimized through tenfold cross-validation in the training cohort. The diagnostic performance of the optimized models and two neuroradiologists was tested in the validation cohort for distinguishing glioblastoma from metastasis. In the external validation, DNN showed the highest diagnostic performance, with an area under receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.956 (95% confidence interval [CI], 0.918-0.990), 90.6% (95% CI, 80.5-100), 88.0% (95% CI, 79.0-97.0), and 89.0% (95% CI, 82.3-95.8), respectively, compared to the best-performing traditional machine learning model (adaptive boosting combined with tree-based feature selection; AUC, 0.890 (95% CI, 0.823-0.947)) and human readers (AUC, 0.774 [95% CI, 0.685-0.852] and 0.904 [95% CI, 0.852-0.951]). The results demonstrated deep learning using radiomic features can be useful for distinguishing glioblastoma from metastasis with good generalizability.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Scientific reports - 10(2020), 1 vom: 21. Juli, Seite 12110 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bae, Sohi [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 04.12.2020 Date Revised 21.07.2021 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41598-020-68980-6 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM312708327 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a We evaluated the diagnostic performance and generalizability of traditional machine learning and deep learning models for distinguishing glioblastoma from single brain metastasis using radiomics. The training and external validation cohorts comprised 166 (109 glioblastomas and 57 metastases) and 82 (50 glioblastomas and 32 metastases) patients, respectively. Two-hundred-and-sixty-five radiomic features were extracted from semiautomatically segmented regions on contrast-enhancing and peritumoral T2 hyperintense masks and used as input data. For each of a deep neural network (DNN) and seven traditional machine learning classifiers combined with one of five feature selection methods, hyperparameters were optimized through tenfold cross-validation in the training cohort. The diagnostic performance of the optimized models and two neuroradiologists was tested in the validation cohort for distinguishing glioblastoma from metastasis. In the external validation, DNN showed the highest diagnostic performance, with an area under receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.956 (95% confidence interval [CI], 0.918-0.990), 90.6% (95% CI, 80.5-100), 88.0% (95% CI, 79.0-97.0), and 89.0% (95% CI, 82.3-95.8), respectively, compared to the best-performing traditional machine learning model (adaptive boosting combined with tree-based feature selection; AUC, 0.890 (95% CI, 0.823-0.947)) and human readers (AUC, 0.774 [95% CI, 0.685-0.852] and 0.904 [95% CI, 0.852-0.951]). The results demonstrated deep learning using radiomic features can be useful for distinguishing glioblastoma from metastasis with good generalizability | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a An, Chansik |e verfasserin |4 aut | |
| 700 | 1 | |a Ahn, Sung Soo |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Hwiyoung |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Kyunghwa |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Sang Wook |e verfasserin |4 aut | |
| 700 | 1 | |a Park, Ji Eun |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Ho Sung |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Seung-Koo |e verfasserin |4 aut | |
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