Predicting local failure of brain metastases after stereotactic radiosurgery with radiomics on planning MR images and dose maps

PURPOSE

Stereotactic radiosurgery (SRS) has become an important modality in the treatment of brain metastases. The purpose of this study is to investigate the potential of radiomic features from planning magnetic resonance (MR) images and dose maps to predict local failure after SRS for brain metastases.

MATERIALS/METHODS

Twenty-eight patients who received Gamma Knife (GK) radiosurgery for brain metastases were retrospectively reviewed in this IRB-approved study. 179 irradiated tumors included 42 that locally failed within one-year follow-up. Using SRS tumor volumes, radiomic features were calculated on T1-weighted contrast-enhanced MR images acquired for treatment planning and planned dose maps. 125 radiomic features regarding tumor shape, dose distribution, MR intensities and textures were extracted for each tumor. Logistic regression with automatic feature selection was built to predict tumor progression from local control after SRS. Feature selection and model evaluation using receiver operating characteristic (ROC) curves were performed in a nested cross validation (CV) scheme. The associations between selected radiomic features and treatment outcomes were statistically assessed by univariate analysis.

RESULTS

The logistic model with feature selection achieved ROC AUC of 0.82 ± 0.09 on 5-fold CV, providing 83% sensitivity and 70% specificity for predicting local failure. A total of 10 radiomic features including 1 shape feature, 6 MR images and 3 dose distribution features were selected. These features were significantly associated with treatment outcomes (p < 0.05). The model was validated on independent holdout data with an AUC of 0.78.

CONCLUSIONS

Radiomic features from planning MR images and dose maps provided prognostic information in SRS for brain metastases. A model built on the radiomic features shows promise for early prediction of tumor local failure after treatment, potentially aiding in personalized care for brain metastases.

Relationship between Glioblastoma Dose Volume Parameters Measured by Dual Time Point Fluoroethylthyrosine-PET and Clinical Outcomes

Glioblastoma multiforme (GBM) is highly invasive. Despite irradiation with wide margins, GBM usually recurs in-field. Recent in vitro data have suggested that progression might be promoted by sublethal irradiation. Fluoroethylthyrosine-PET (FET-PET) can be used to detect glioblastoma invasion not apparent on MRI. We therefore performed a retrospective analysis of a prospective clinical study to examine whether glioblastoma outcomes depend on dose volume parameters measured by MRI and FET-PET. Twenty-three patients were prospectively recruited to a study examining the role of dual time point FET-PET in the treatment planning of GBM radiotherapy. The dose delivered to the site of recurrence was subdivided into suboptimal-dose (SOD) and high-dose (HD) areas. Types of progression were defined for correlation with dosimetric parameters including V100% of gross tumor volume (GTV)_PET, GTV_PETMRI, and GTV_MRI. The HD area did not cover the entire GTV_PETMRI in any case. Recurrences were significantly more frequent in the SubD area (chi-squared test, p = 0.004). There was no relationship between increasing dose volume and progression. The V100% for GTV_PET and progression-free survival (PFS) was positively correlated (Spearman’s rho 0.417; p = 0.038). Progression is more common in areas with suboptimal dosing. Dose heterogeneity within GTV_PET may be responsible for shorter PFS.