Voxel-wise prediction of recurrent high grade glioma via proximity estimation coupled multi-dimensional SVM

PURPOSES: To provide early and localized glioblastoma (GBM) recurrence prediction, we introduce a novel post-surgery multi-parametric MR-based support vector machine (SVM) method coupling with stem cell niches (SCN) proximity estimation. METHODS: This study utilized post-surgery MRI scans ~2 months before clinically diagnosed recurrence from 50 patients with recurrent GBM. The main prediction pipeline consists of a proximity-based estimator to identify regions with high risks of recurrence (HRR), and an SVM classifier to provide voxel-wise prediction in HRR. The HRRs were estimated using the weighted sum of inverse distances to two possible origins of recurrence – SCN and tumor cavity. Subsequently, multi-parametric voxels (from T1, T1ce, FLAIR, T2, ADC) within the HRR were grouped into recurrent (warped from the clinical diagnosis) and non-recurrent subregions, and fed into the proximity estimation coupled SVM classifier - SVM(PE). The cohort was randomly divided into 40% and 60% for training and testing, respectively. The trained SVM(PE) was then extrapolated to an earlier time point for earlier recurrence prediction. As an exploratory analysis, the SVM(PE) predictive cluster sizes and the image intensities from the five MR sequences were compared across time to assess the progressive subclinical traces. RESULTS: On 2-month pre-recurrence MRIs from 30 test cohort patients, the SVM(PE) classifier achieved a recall of 0.80, a precision of 0.69, an F1-score of 0.73, and an average boundary distance of 7.49 mm. Exploratory analysis at early time points showed spatially consistent but significantly smaller subclinical clusters and significantly increased T1ce and ADC values over time. CONCLUSION: We demonstrated a novel voxel-wise early prediction method, SVM(PE,) for GBM recurrence based on clinical follow-up MR scans. SVM(PE) is promising in localizing subclinical traces of recurrence 2-month ahead of clinical diagnosis and may be used to guide more effective personalized early salvage therapy.