Keywords: Radiomics, Prostate, multi-parametric MRI, prostate cancer radiosensitivity, genomic siganture, PORTOSGenomic classifiers, such as PORTOS, have shown great promise in the prediction of prostate cancer radiosensitivity. However, the spatial heterogeneity of prostate cancer may confound genomic assessment due to tumor sampling error. We aimed to develop a model predictive of PORTOS genomic signature using multiparametric MRI (mpMRI) radiomics features and machine learning. Lesions were localized based on Habitat Risk Score maps. Eight radiomic features were selected (out of 167) including T2, ADC, high B-value intensity and texture variables and used to build logistic regression models through cross-validation. Our analysis shows association between the radiomics profile and prostate lesion radiosensitivity phenotype.
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