Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study
BackgroundMeningioma invasion can be preoperatively recognized by radiomics features, which significantly contributes to treatment decision-making. Here, we aimed to evaluate the comparative performance of radiomics signatures derived from varying regions of interests (ROIs) in predicting BI and asc...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2021-08-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2021.708040/full |
| _version_ | 1828915357701636096 |
|---|---|
| author | Dongdong Xiao Zhen Zhao Jun Liu Xuan Wang Peng Fu Jehane Michael Le Grange Jihua Wang Xuebing Guo Hongyang Zhao Jiawei Shi Jiawei Shi Jiawei Shi Pengfei Yan Xiaobing Jiang |
| author_facet | Dongdong Xiao Zhen Zhao Jun Liu Xuan Wang Peng Fu Jehane Michael Le Grange Jihua Wang Xuebing Guo Hongyang Zhao Jiawei Shi Jiawei Shi Jiawei Shi Pengfei Yan Xiaobing Jiang |
| author_sort | Dongdong Xiao |
| collection | DOAJ |
| description | BackgroundMeningioma invasion can be preoperatively recognized by radiomics features, which significantly contributes to treatment decision-making. Here, we aimed to evaluate the comparative performance of radiomics signatures derived from varying regions of interests (ROIs) in predicting BI and ascertaining the optimal width of the peritumoral regions needed for accurate analysis.MethodsFive hundred and five patients from Wuhan Union Hospital (internal cohort) and 214 cases from Taihe Hospital (external validation cohort) pathologically diagnosed as meningioma were included in our study. Feature selection was performed from 1,015 radiomics features respectively obtained from nine different ROIs (brain-tumor interface (BTI)2–5mm; whole tumor; the amalgamation of the two regions) on contrast-enhanced T1-weighted imaging using least-absolute shrinkage and selection operator and random forest. Principal component analysis with varimax rotation was employed for feature reduction. Receiver operator curve was utilized for assessing discrimination of the classifier. Furthermore, clinical index was used to detect the predictive power.ResultsModel obtained from BTI4mm ROI has the maximum AUC in the training set (0.891 (0.85, 0.932)), internal validation set (0.851 (0.743, 0.96)), and external validation set (0.881 (0.833, 0.928)) and displayed statistically significant results between nine radiomics models. The most predictive radiomics features are almost entirely generated from GLCM and GLDM statistics. The addition of PEV to radiomics features (BTI4mm) enhanced model discrimination of invasive meningiomas.ConclusionsThe combined model (radiomics classifier with BTI4mm ROI + PEV) had greater diagnostic performance than other models and its clinical application may positively contribute to the management of meningioma patients. |
| first_indexed | 2024-12-13T20:13:27Z |
| format | Article |
| id | doaj.art-6fee39c4449f4c279f76c975df02c509 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-13T20:13:27Z |
| publishDate | 2021-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-6fee39c4449f4c279f76c975df02c5092022-12-21T23:32:52ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-08-011110.3389/fonc.2021.708040708040Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter StudyDongdong Xiao0Zhen Zhao1Jun Liu2Xuan Wang3Peng Fu4Jehane Michael Le Grange5Jihua Wang6Xuebing Guo7Hongyang Zhao8Jiawei Shi9Jiawei Shi10Jiawei Shi11Pengfei Yan12Xiaobing Jiang13Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Neurosurgery, Taihe Hospital, Hubei University of Medicine, Shiyan, ChinaDepartment of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaSt Mary’s Hospital, Isle of Wight NHS Trust, Newport, United KingdomDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaClinical Research Center for Medical Imaging in Hubei Province, Wuhan, ChinaHubei Province Key Laboratory of Molecular Imaging, Wuhan, ChinaDepartment of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaBackgroundMeningioma invasion can be preoperatively recognized by radiomics features, which significantly contributes to treatment decision-making. Here, we aimed to evaluate the comparative performance of radiomics signatures derived from varying regions of interests (ROIs) in predicting BI and ascertaining the optimal width of the peritumoral regions needed for accurate analysis.MethodsFive hundred and five patients from Wuhan Union Hospital (internal cohort) and 214 cases from Taihe Hospital (external validation cohort) pathologically diagnosed as meningioma were included in our study. Feature selection was performed from 1,015 radiomics features respectively obtained from nine different ROIs (brain-tumor interface (BTI)2–5mm; whole tumor; the amalgamation of the two regions) on contrast-enhanced T1-weighted imaging using least-absolute shrinkage and selection operator and random forest. Principal component analysis with varimax rotation was employed for feature reduction. Receiver operator curve was utilized for assessing discrimination of the classifier. Furthermore, clinical index was used to detect the predictive power.ResultsModel obtained from BTI4mm ROI has the maximum AUC in the training set (0.891 (0.85, 0.932)), internal validation set (0.851 (0.743, 0.96)), and external validation set (0.881 (0.833, 0.928)) and displayed statistically significant results between nine radiomics models. The most predictive radiomics features are almost entirely generated from GLCM and GLDM statistics. The addition of PEV to radiomics features (BTI4mm) enhanced model discrimination of invasive meningiomas.ConclusionsThe combined model (radiomics classifier with BTI4mm ROI + PEV) had greater diagnostic performance than other models and its clinical application may positively contribute to the management of meningioma patients.https://www.frontiersin.org/articles/10.3389/fonc.2021.708040/fullmeningiomabrain invasionradiomicsmagnetic resonance imagesperitumoral regionsprediction |
| spellingShingle | Dongdong Xiao Zhen Zhao Jun Liu Xuan Wang Peng Fu Jehane Michael Le Grange Jihua Wang Xuebing Guo Hongyang Zhao Jiawei Shi Jiawei Shi Jiawei Shi Pengfei Yan Xiaobing Jiang Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study Frontiers in Oncology meningioma brain invasion radiomics magnetic resonance images peritumoral regions prediction |
| title | Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study |
| title_full | Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study |
| title_fullStr | Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study |
| title_full_unstemmed | Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study |
| title_short | Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study |
| title_sort | diagnosis of invasive meningioma based on brain tumor interface radiomics features on brain mr images a multicenter study |
| topic | meningioma brain invasion radiomics magnetic resonance images peritumoral regions prediction |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2021.708040/full |
| work_keys_str_mv | AT dongdongxiao diagnosisofinvasivemeningiomabasedonbraintumorinterfaceradiomicsfeaturesonbrainmrimagesamulticenterstudy AT zhenzhao diagnosisofinvasivemeningiomabasedonbraintumorinterfaceradiomicsfeaturesonbrainmrimagesamulticenterstudy AT junliu diagnosisofinvasivemeningiomabasedonbraintumorinterfaceradiomicsfeaturesonbrainmrimagesamulticenterstudy AT xuanwang diagnosisofinvasivemeningiomabasedonbraintumorinterfaceradiomicsfeaturesonbrainmrimagesamulticenterstudy AT pengfu diagnosisofinvasivemeningiomabasedonbraintumorinterfaceradiomicsfeaturesonbrainmrimagesamulticenterstudy AT jehanemichaellegrange diagnosisofinvasivemeningiomabasedonbraintumorinterfaceradiomicsfeaturesonbrainmrimagesamulticenterstudy AT jihuawang diagnosisofinvasivemeningiomabasedonbraintumorinterfaceradiomicsfeaturesonbrainmrimagesamulticenterstudy AT xuebingguo diagnosisofinvasivemeningiomabasedonbraintumorinterfaceradiomicsfeaturesonbrainmrimagesamulticenterstudy AT hongyangzhao diagnosisofinvasivemeningiomabasedonbraintumorinterfaceradiomicsfeaturesonbrainmrimagesamulticenterstudy AT jiaweishi diagnosisofinvasivemeningiomabasedonbraintumorinterfaceradiomicsfeaturesonbrainmrimagesamulticenterstudy AT jiaweishi diagnosisofinvasivemeningiomabasedonbraintumorinterfaceradiomicsfeaturesonbrainmrimagesamulticenterstudy AT jiaweishi diagnosisofinvasivemeningiomabasedonbraintumorinterfaceradiomicsfeaturesonbrainmrimagesamulticenterstudy AT pengfeiyan diagnosisofinvasivemeningiomabasedonbraintumorinterfaceradiomicsfeaturesonbrainmrimagesamulticenterstudy AT xiaobingjiang diagnosisofinvasivemeningiomabasedonbraintumorinterfaceradiomicsfeaturesonbrainmrimagesamulticenterstudy |