Glioblastoma and brain solitary metastasis from lung cancer have similar peritumoral edema on T2-weighted imaging (T2WI). However, indistinguishable signs between these two tumors embarrass the radiologists and lead to high misdiagnosis rate. To address such issue, radiomics biomarkers were analyzed to detail the tumors’ histologic and morphologic characteristics. Results indicated that radiomics biomarkers including histogram of oriented gradient, shape and grey level co-occurrence matrix, which charaterize the lesion’s shape and signal showed good performance in differentiating these two tumors. Furthermore, using those radiomics biomarkers, a gradient-boosting machine learning model was established and showed good performance (Area under the curve=0.88).
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords