Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features
Tumor infiltration of central nervous system (CNS) malignant tumors may extend beyond visible contrast enhancement. This study explored tumor habitat characteristics in the intratumoral and peritumoral regions to distinguish common malignant brain tumors such as glioblastoma, primary central nervous...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.848846/full |
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| author | Dongming Liu Jiu Chen Jiu Chen Honglin Ge Xinhua Hu Xinhua Hu Kun Yang Yong Liu Guanjie Hu Bei Luo Zhen Yan Kun Song Chaoyong Xiao Yuanjie Zou Wenbin Zhang Wenbin Zhang Hongyi Liu Hongyi Liu |
| author_facet | Dongming Liu Jiu Chen Jiu Chen Honglin Ge Xinhua Hu Xinhua Hu Kun Yang Yong Liu Guanjie Hu Bei Luo Zhen Yan Kun Song Chaoyong Xiao Yuanjie Zou Wenbin Zhang Wenbin Zhang Hongyi Liu Hongyi Liu |
| author_sort | Dongming Liu |
| collection | DOAJ |
| description | Tumor infiltration of central nervous system (CNS) malignant tumors may extend beyond visible contrast enhancement. This study explored tumor habitat characteristics in the intratumoral and peritumoral regions to distinguish common malignant brain tumors such as glioblastoma, primary central nervous system lymphoma, and brain metastases. The preoperative MRI data of 200 patients with solitary malignant brain tumors were included from two datasets for training. Quantitative radiomic features from the intratumoral and peritumoral regions were extracted for model training. The performance of the model was evaluated using data (n = 50) from the third clinical center. When combining the intratumoral and peritumoral features, the Adaboost model achieved the best area under the curve (AUC) of 0.91 and accuracy of 76.9% in the test cohort. Based on the optimal features and classifier, the model in the binary classification diagnosis achieves AUC of 0.98 (glioblastoma and lymphoma), 0.86 (lymphoma and metastases), and 0.70 (glioblastoma and metastases) in the test cohort, respectively. In conclusion, quantitative features from non-enhanced peritumoral regions (especially features from the 10-mm margin around the tumor) can provide additional information for the characterization of regional tumoral heterogeneity, which may offer potential value for future individualized assessment of patients with CNS tumors. |
| first_indexed | 2024-12-10T17:48:50Z |
| format | Article |
| id | doaj.art-d89a89ec08ed434391a56e5f2a3b80b9 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-10T17:48:50Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-d89a89ec08ed434391a56e5f2a3b80b92022-12-22T01:39:08ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-07-011210.3389/fonc.2022.848846848846Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic featuresDongming Liu0Jiu Chen1Jiu Chen2Honglin Ge3Xinhua Hu4Xinhua Hu5Kun Yang6Yong Liu7Guanjie Hu8Bei Luo9Zhen Yan10Kun Song11Chaoyong Xiao12Yuanjie Zou13Wenbin Zhang14Wenbin Zhang15Hongyi Liu16Hongyi Liu17Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaInstitute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, ChinaDepartment of Neurosurgery, Institute of Brain Sciences, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Neurosurgery, Institute of Brain Sciences, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Pathology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Neurosurgery, Institute of Brain Sciences, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Neurosurgery, Institute of Brain Sciences, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, ChinaTumor infiltration of central nervous system (CNS) malignant tumors may extend beyond visible contrast enhancement. This study explored tumor habitat characteristics in the intratumoral and peritumoral regions to distinguish common malignant brain tumors such as glioblastoma, primary central nervous system lymphoma, and brain metastases. The preoperative MRI data of 200 patients with solitary malignant brain tumors were included from two datasets for training. Quantitative radiomic features from the intratumoral and peritumoral regions were extracted for model training. The performance of the model was evaluated using data (n = 50) from the third clinical center. When combining the intratumoral and peritumoral features, the Adaboost model achieved the best area under the curve (AUC) of 0.91 and accuracy of 76.9% in the test cohort. Based on the optimal features and classifier, the model in the binary classification diagnosis achieves AUC of 0.98 (glioblastoma and lymphoma), 0.86 (lymphoma and metastases), and 0.70 (glioblastoma and metastases) in the test cohort, respectively. In conclusion, quantitative features from non-enhanced peritumoral regions (especially features from the 10-mm margin around the tumor) can provide additional information for the characterization of regional tumoral heterogeneity, which may offer potential value for future individualized assessment of patients with CNS tumors.https://www.frontiersin.org/articles/10.3389/fonc.2022.848846/fullradiomicsglioblastomalymphomabrain metastasesperitumoral regions |
| spellingShingle | Dongming Liu Jiu Chen Jiu Chen Honglin Ge Xinhua Hu Xinhua Hu Kun Yang Yong Liu Guanjie Hu Bei Luo Zhen Yan Kun Song Chaoyong Xiao Yuanjie Zou Wenbin Zhang Wenbin Zhang Hongyi Liu Hongyi Liu Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features Frontiers in Oncology radiomics glioblastoma lymphoma brain metastases peritumoral regions |
| title | Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features |
| title_full | Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features |
| title_fullStr | Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features |
| title_full_unstemmed | Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features |
| title_short | Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features |
| title_sort | differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features |
| topic | radiomics glioblastoma lymphoma brain metastases peritumoral regions |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.848846/full |
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