Recently, there is a growing interest in using real-world data (RWD) to generate real-world evidence (RWE) that complements clinical trials. Nevertheless, to quantify the treatment effects, it is important to develop meaningful RWD-based endpoints. In cancer trials, two real-world endpoints are particularly of interest: real-world overall survival (rwOS) and real-world time to next treatment (rwTTNT). In this work, we identified ways to calculate these real-world endpoints with structured EHR data, and validated these endpoints against the gold-standard measurements of these endpoints derived from linked EHR and TR data. In addition, we also examined and reported the data quality issues especially the inconsistency between the EHR and TR data. Using survival model, our result showed that patients (1) without subsequent chemotherapy or (2) with subsequent chemotherapy and longer rwTTNT, would have longer rwOS, showing the validity of using rwTTNT as a real-world surrogate marker for measuring cancer endpoints.

No abstract

Purpose:Recently, compressed sensing (CS) based iterative reconstruction (IR) method is receiving attentions to reconstruct high quality cone beam computed tomography (CBCT) images using sparsely sampled or noisy projections. The aim of this study is to develop a novel baseline algorithm called Mask Guided Image Reconstruction (MGIR), which can provide superior image quality for both low‐dose 3DCBCT and 4DCBCT under single mathematical framework.Methods:In MGIR, the unknown CBCT volume was mathematically modeled as a combination of two regions where anatomical structures are 1) within the priori‐defined mask and 2) outside the mask. Then we update each part of images alternatively thorough solving minimization problems based on CS type IR. For low‐dose 3DCBCT, the former region is defined as the anatomically complex region where it is focused to preserve edge information while latter region is defined as contrast uniform, and hence aggressively updated to remove noise/artifact. In 4DCBCT, the regions are separated as the common static part and moving part. Then, static volume and moving volumes were updated with global and phase sorted projection respectively, to optimize the image quality of both moving and static part simultaneously.Results:Examination of MGIR algorithm showed that high quality of both low‐dose 3DCBCT and 4DCBCT images can be reconstructed without compromising the image resolution and imaging dose or scanning time respectively. For low‐dose 3DCBCT, a clinical viable and high resolution head‐and‐neck image can be obtained while cutting the dose by 83%. In 4DCBCT, excellent quality 4DCBCT images could be reconstructed while requiring no more projection data and imaging dose than a typical clinical 3DCBCT scan.Conclusion:The results shown that the image quality of MGIR was superior compared to other published CS based IR algorithms for both 4DCBCT and low‐dose 3DCBCT. This makes our MGIR algorithm potentially useful in various on‐line clinical applications.Provisional Patent: UF#15476; WGS Ref. No. U1198.70067US00