Efficacy of sequential radiation and chemotherapy in treating glioblastoma with poor performance status.

INTRODUCTION: While the current standard of care after maximal safe resection for glioblastoma (GBM) is concomitant radiation and chemotherapy, the ideal therapy for patients with poor performance status remains in question due to concerns about treatment tolerance. We sought to evaluate an alternative regimen, sequential radiation and chemotherapy, to assess its efficacy as a treatment option for poorly performing patients. METHODS: We performed a retrospective analysis using the 2015 National Cancer Database in which the survival of patients with a KPS ≤ 70 who received sequential radiation and chemotherapy were compared to those who received radiation therapy alone. Survival outcomes were compared using Kaplan-Meier curves with log rank testing and Cox proportional hazard regression. RESULTS: There were 84 patients analyzed in this study, all of whom had a KPS between 10 and 70. Of those analyzed, 73.8% received radiation therapy alone, and 26.2% received sequential radiation and chemotherapy. There was no difference in survival between the two treatment groups (p = 0.84). Patient age of 70 years or older (n = 31) was associated with decreased survival (HR 1.06 per year, p < 0.0001), regardless of KPS and a KPS of < 70 correlated with a near-significant trend toward worse survival (HR 1.63, p = 0.06). CONCLUSIONS: Treatment with sequential radiation and chemotherapy in poorly performing patients is not associated with an advantage in survival outcome when compared to radiation alone in GBM patients with poor performance status.

Predictive Value of Multiparametric Magnetic Resonance Imaging in Risk Group Stratification of Prostate Adenocarcinoma.

Purpose: The aim of this study was to further assess the clinical utility of multiparametric magnetic resonance imaging (MP-MRI) in prostate cancer (PC) staging following 2023 clinical guideline changes, both as an independent predictor of high-stage (>T3a) or high-risk PC and when combined with patient characteristics. Methods and Materials: The present study was a retrospective review of 171 patients from 2008 to 2018 who underwent MP-MRI before radical prostatectomy at a single institution. The accuracy of clinical staging was compared between conventional staging and MP-MRI-based clinical staging. Sensitivity, specificity, positive predictive value, and negative predictive value were compared, and receiver operating characteristic curves were generated. Linear regression analyses were used to calculate concordance (C-statistic). Results: Of the 171 patients, final pathology revealed 95 (55.6%) with T2 disease, 62 (36.3%) with T3a disease, and 14 (8.2%) with T3b disease. Compared with conventional staging, MP-MRI-based staging demonstrated significantly increased accuracy in identifying T3a disease, intermediate risk, and high/very-high-risk PC. When combined with clinical characteristics, MP-MRI-based staging improved the area under the curve from 0.753 to 0.808 (P = .0175), compared with conventional staging. Conclusions: MP-MRI improved the identification of T3a PC, intermediate-risk PC, and high- or very-high-risk PC. Further, when combined with clinical characteristics, MP-MRI-based staging significantly improved risk stratification, compared with conventional staging. These findings represent further evidence to support the integration of MP-MRI into prostate adenocarcinoma clinical staging guidelines.

Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy.

(1) Background: Radiomics use high-throughput mining of medical imaging data to extract unique information and predict tumor behavior. Currently available clinical prediction models poorly predict treatment outcomes in pancreatic adenocarcinoma. Therefore, we used radiomic features of primary pancreatic tumors to develop outcome prediction models and compared them to traditional clinical models. (2) Methods: We extracted and analyzed radiomic data from pre-radiation contrast-enhanced CTs of 74 pancreatic cancer patients undergoing stereotactic body radiotherapy. A panel of over 800 radiomic features was screened to create overall survival and local-regional recurrence prediction models, which were compared to clinical prediction models and models combining radiomic and clinical information. (3) Results: A 6-feature radiomic signature was identified that achieved better overall survival prediction performance than the clinical model (mean concordance index: 0.66 vs. 0.54 on resampled cross-validation test sets), and the combined model improved the performance slightly further to 0.68. Similarly, a 7-feature radiomic signature better predicted recurrence than the clinical model (mean AUC of 0.78 vs. 0.66). (4) Conclusion: Overall survival and recurrence can be better predicted with models based on radiomic features than with those based on clinical features for pancreatic cancer.