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At least 865 records · Page 48

A Phase 2 Trial of Concurrent Chemotherapy and Proton Therapy for Stage III Non-Small Cell Lung Cancer: Results and Reflections Following Early Closure of a Single-Institution Study

Purpose: Proton therapy has been shown to reduce radiation dose to organs at risk (OAR) and could be used to safely escalate the radiation dose. We analyzed outcomes in a group of phase 2 study patients treated with dose-escalated proton therapy with concurrent chemotherapy for stage 3 non-small cell lung cancer (NSCLC). Methods and Materials: From 2009 through 2013, LU02, a phase 2 trial of proton therapy delivering 74 to 80 Gy at 2 Gy/fraction with concurrent chemotherapy for stage 3 NSCLC, was opened to accrual at our institution. Due to slow accrual and competing trials, the study was closed after just 14 patients (stage IIIA, 9 patients; stage IIIB, 5 patients) were accrued over 4 years. During that same time period, 55 additional stage III patients were treated with high-dose proton therapy, including 7 in multi-institutional proton clinical trials, 4 not enrolled due to physician preference, and 44 who were ineligible based on strict entry criteria. An unknown number of patients were ineligible for enrollment due to insurance coverage issues and thus were treated with photon radiation. Median follow-up of surviving patients was 52 months. Results: Two-year overall survival and progression-free survival rates were 57% and 25%, respectively. Median lengths of overall survival and progression-free survival were 33 months and 14 months, respectively. There were no acute grade 3 toxicities related to proton therapy. Late grade 3 gastrointestinal toxicity and pulmonary toxicity each occurred in 1 patient. Conclusions: Dose-escalated proton therapy with concurrent chemotherapy was well tolerated with encouraging results among a small cohort of patients. Unfortunately, single-institution proton studies may be difficult to accrue and consideration for pragmatic and/or multicenter trial design should be considered when developing future proton clinical trials.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Entropy-based closure for probabilistic learning on manifolds

In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically consistent with an existing dataset construed as a realization from a non-Gaussian random vector. The manifold structure is learned using diffusion manifolds and the statistical sample generation is accomplished using a projected Itˆo stochastic differential equation. This probabilistic learning approach has been extended to polynomial chaos representation of databases on manifolds and to probabilistic nonconvex constrained optimization with a fixed budget of function evaluations. The methodology introduces an isotropic-diffusion kernel with hyperparameter ". Currently, " is more or less arbitrarily chosen. In this paper, we propose a selection criterion for identifying an optimal value of ", based on a maximum entropy argument. The result is a comprehensive, closed, probabilistic model for characterizing data sets with hidden constraints. This entropy argument ensures that out of all possible models, this is the one that is the most uncertain beyond any specified constraints, which is selected. Applications are presented for several databases.

97 MATHEMATICS AND COMPUTING↗