Bibliographic Details
| Title: |
A Clinical Semantic and Radiomics Nomogram for Predicting Brain Invasion in WHO Grade II Meningioma Based on Tumor and Tumor-to-Brain Interface Features |
| Authors: |
Ning Li, Yan Mo, Chencui Huang, Kai Han, Mengna He, Xiaolan Wang, Jiaqi Wen, Siyu Yang, Haoting Wu, Fei Dong, Fenglei Sun, Yiming Li, Yizhou Yu, Minming Zhang, Xiaojun Guan, Xiaojun Xu |
| Source: |
Frontiers in Oncology, Vol 11 (2021) |
| Publisher Information: |
Frontiers Media S.A., 2021. |
| Publication Year: |
2021 |
| Collection: |
LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| Subject Terms: |
atypical meningioma, brain invasion, magnetic resonance imaging, radiomics, semantic, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| More Details: |
BackgroundBrain invasion in meningioma has independent associations with increased risks of tumor progression, lesion recurrence, and poor prognosis. Therefore, this study aimed to construct a model for predicting brain invasion in WHO grade II meningioma by using preoperative MRI.MethodsOne hundred seventy-three patients with brain invasion and 111 patients without brain invasion were included. Three mainstream features, namely, traditional semantic features and radiomics features from tumor and tumor-to-brain interface regions, were acquired. Predictive models correspondingly constructed on each feature set or joint feature set were constructed.ResultsTraditional semantic findings, e.g., peritumoral edema and other four features, had comparable performance in predicting brain invasion with each radiomics feature set. By taking advantage of semantic features and radiomics features from tumoral and tumor-to-brain interface regions, an integrated nomogram that quantifies the risk factor of each selected feature was constructed and had the best performance in predicting brain invasion (area under the curve values were 0.905 in the training set and 0.895 in the test set).ConclusionsThis study provided a clinically available and promising approach to predict brain invasion in WHO grade II meningiomas by using preoperative MRI. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2234-943X |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fonc.2021.752158/full; https://doaj.org/toc/2234-943X |
| DOI: |
10.3389/fonc.2021.752158 |
| Access URL: |
https://doaj.org/article/7a3611a874a54445ab2e92e0283a37be |
| Accession Number: |
edsdoj.7a3611a874a54445ab2e92e0283a37be |
| Database: |
Directory of Open Access Journals |
