ABSTRACT
Purpose: Pseudoprogression mimicking recurrent glioblastoma remains a diagnostic challenge that may adversely confound or delay appropriate treatment or clinical trial enrollment. We sought to build a radiomic classifier to predict pseudoprogression in patients with primary isocitrate dehydrogenase wild type glioblastoma. Methods and Materials: We retrospectively examined a training cohort of 74 patients with isocitrate dehydrogenase wild type glioblastomas with brain magnetic resonance imaging including dynamic contrast enhanced T1 perfusion before resection of an enhancing lesion indeterminate for recurrent tumor or pseudoprogression. A recursive feature elimination random forest classifier was built using nested cross-validation without and with O6-methylguanine-DNA methyltransferase status to predict pseudoprogression. Results: A classifier constructed with cross-validation on the training cohort achieved an area under the receiver operating curve of 81% for predicting pseudoprogression. This was further improved to 89% with the addition of O6-methylguanine-DNA methyltransferase status into the classifier. Conclusions: Our results suggest that radiomic analysis of contrast T1-weighted images and magnetic resonance imaging perfusion images can assist the prompt diagnosis of pseudoprogression. Validation on external and independent data sets is necessary to verify these advanced analyses, which can be performed on routinely acquired clinical images and may help inform clinical treatment decisions.
ABSTRACT
PURPOSE: To determine how particle density affects dose distribution and outcomes after lobar radioembolization. METHODS: Matched pairs of patients, treated with glass versus resin microspheres, were selected by propensity score matching (114 patients), in this single-institution retrospective study. For each patient, tumor and liver particle density (particles/cm3) and dose (Gy) were determined. Tumor-to-normal ratio was measured on both 99mTc-MAA SPECT/CT and post-90Y bremsstrahlung SPECT/CT. Microdosimetry simulations were used to calculate first percentile dose, which is the dose in the cold spots between microspheres. Local progression-free survival (LPFS) and overall survival were analyzed. RESULTS: As more particles were delivered, doses on 90Y SPECT/CT became more uniform throughout the treatment volume: tumor and liver doses became more similar (p = 0.04), and microscopic cold spots between particles disappeared. For hypervascular tumors (tumor-to-normal ratio ≥ 2.6 on MAA scan), delivering fewer particles (< 6000 particles/cm3 treatment volume) was associated with better LPFS (p = 0.03). For less vascular tumors (tumor-to-normal ratio < 2.6), delivering more particles (≥ 6000 particles/cm3) was associated with better LPFS (p = 0.02). In matched pairs of patients, using the optimal particle density resulted in improved overall survival (11.5 vs. 6.8 months, p = 0.047), compared to using suboptimal particle density. Microdosimetry resulted in better predictions of LPFS (p = 0.03), and overall survival (p = 0.02), compared to conventional dosimetry. CONCLUSION: The number of particles delivered can be chosen to maximize the tumor dose and minimize the liver dose, based on tumor vascularity. Optimizing the particle density resulted in improved LPFS and overall survival.