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This paper uses two recently developed tests to identify neglected nonlinearity in the relationship between excess returns on four asset classes and several economic and financial variables. Having found some evidence of possible nonlinearity, it was then investigated whether the predictive power of these variables could be enhanced by using neural network models instead of linear regression or GARCH models.Some evidence of nonlinearity in the relationships between the explanatory variables and large stocks and corporate bonds was found. It was also found that the GARCH models are conditionally efficient with respect to neural network models, but the neural network models outperform GARCH models if financial performance measures are used. In resonance with the results reported for the tests for neglected nonlinearity, it was found that the neural network forecasts are conditionally efficient with respect to linear regression models for large stocks and corporate bonds, whereas the evidence is not statistically significant for small stocks and intermediate-term government bonds. This difference persists even when financial performance measures for individual asset classes are used for comparison.

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Purpose The plan‐class specific reference field concept could theoretically improve the calibration of radiation detectors in a beam environment much closer to clinical deliveries than existing broad beam dosimetry protocols. Due to a lack of quantitative guidelines and representative data, however, the pcsr field concept has not yet been widely implemented. This work utilizes quantitative plan complexity metrics from modulated clinical treatments in order to investigate the establishment of potential plan classes using two different clustering methodologies. The utility of these potential plan clusters is then further explored by analyzing their relevance to actual dosimetric correction factors. Methods Two clinical databases containing several hundred modulated plans originally delivered on two Varian linear accelerators were analyzed using 21 plan complexity metrics. In the first approach, each database’s plans were further subdivided into groups based on the anatomic site of treatment and then compared to one another using a series of nonparametric statistical tests. In the second approach, objective clustering algorithms were used to seek potential plan clusters in the multidimensional complexity‐metric space. Concurrently, beam‐ and detector‐specific dosimetric corrections for a subset of the modulated clinical plans were determined using Monte Carlo for three different ionization chambers. The distributions of the dosimetric correction factors were compared to the derived plan clusters to see which plan clusters, if any, could help predict the correction factor magnitudes. Ultimately, a simplified volume averaging metric (SVAM) is shown to be much more relevant to the total dosimetric correction factor than the established plan clusters. Results Plan groups based on the site of treatment did not show noticeable distinction from one another in the context of the metrics investigated. An objective clustering algorithm was able to discriminate volumetric modulated arc therapy (VMAT) plans from step‐and‐shoot intensity‐modulated radiation therapy plans with an accuracy of 90.8%, but no clusters were found to exist at any level more specific than delivery modality. Monte Carlo determined correction factors for the modulated plans ranged from 0.970 to 1.104, 0.983 to 1.027, and 0.986 to 1.009 for the A12, A1SL, and A26 chambers, respectively, and were highly variable even within the treatment modality plan clusters. The magnitudes of these correction factors were explained almost entirely by volume averaging with SVAM demonstrating positive correlation with all Monte Carlo established total correction factors. Conclusions Plan complexity metrics do provide some quantitative basis for the investigation of plan clusters, but an objective clustering algorithm demonstrated that quantifiable differences could only be found between VMAT and step‐and‐shoot beams delivered on the same treatment machine. The inherent variability of the Monte Carlo determined correction factors could not be explained solely by the modalit...

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Purpose The determination of absorbed dose to water from external beam radiotherapy using radiation detectors is currently rooted in calibration protocols that do not account for modulations encountered in patient‐specific deliveries. Detector response in composite clinical fields has not been extensively studied due to the time and effort required to determine these corrections on a case‐by‐case basis. To help bridge this gap in knowledge, corrections for the Exradin A1SL scanning chamber were determined in a large number of composite clinical fields using Monte Carlo methods. The chamber‐specific perturbations that contribute the most to the overall correction factor were also determined. Methods A total of 131 patient deliveries comprised of 834 beams from a Varian C‐arm linear accelerator were converted to EGSnrc Monte Carlo inputs. A validated BEAMnrc 21EX linear accelerator model was used as a particle source throughout the EGSnrc simulations. Composite field dose distributions were compared against a commercial treatment planning system for validation. The simulation geometry consisted of a cylindrically symmetric water‐equivalent phantom with the Exradin A1SL scanning chamber embedded inside. Various chamber perturbation factors were investigated in the egs_chamber user code of EGSnrc and were compared to reference field conditions to determine the plan‐specific correction factor. Results The simulation results indicated that the Exradin A1SL scanning chamber is suitable to use as an absolute dosimeter within a high‐dose and low‐gradient target region in most nonstandard composite fields; however, there are still individual cases that require larger delivery‐specific corrections. The volume averaging and replacement perturbations showed the largest impact on the overall plan‐specific correction factor for the Exradin A1SL scanning chamber, and both volumetric modulated arc therapy (VMAT) and step‐and‐shoot beams demonstrated similar correction factor magnitudes among the data investigated. Total correction magnitudes greater than 2% were required by 9.1% of step‐and‐shoot beams and 14.5% of VMAT beams. When examining full composite plan deliveries as opposed to individual beams, 0.0% of composite step‐and‐shoot plans and 2.6% of composite VMAT plans required correction magnitudes greater than 2%. Conclusions The A1SL scanning chamber was found to be suitable to use for absolute dosimetry in high‐dose and low‐gradient dose regions of composite IMRT plans but even if a composite dose distribution is large compared to the detector used, a correction‐free absorbed dose‐to‐water measurement is not guaranteed.

Purpose: To validate an MR-compatible version of the ScandiDos Delta 4 Phan-tom+ on a 0.35T MR guided linear accelerator (MR-Linac) system and to determine the effect of plan complexity on the measurement results. Methods/Materials: 36 clinical treatment plans originally delivered on a 0.35T MR linac system were re-planned on the Delta 4 Phantom+ MR geometry following our clinical quality assurance (QA) protocol. The QA plans were then measured using the Delta 4 Phantom+ MR and the global gamma pass rates were compared to previous results measured using a Sun Nuclear ArcCHECK-MR. Both 3%/3mm and 2%/ 2mm global gamma pass rates with a 20% dose threshold were recorded and compared. Plan complexity was quantified for each clinical plan investigated using 24 different plan metrics and each metric's correlation with the overall 2%/2mm global gamma pass rate was investigated using Pearson correlation coefficients. Results: Both systems demonstrated comparable levels of gamma pass rates at both the 3%/3mm and 2%/2mm level for all plan complexity metrics. Nine plan metrics including area, number of active MLCs, perimeter, edge metric, leaf segment variability, complete irradiation area outline, irregularity, leaf travel index, and unique opening index were moderately (|r| > 0.5) correlated with the Delta 4 2%/2mm global gamma pass rates whereas those same metrics had weak correlation with the Arc-CHECK-MR pass rates. Only the perimeter to area ratio and small aperture score (20 mm) metrics showed moderate correlation with the ArcCHECK-MR gamma pass rates. Conclusions: The MR-compatible version of the ScandiDos Delta 4 Phantom+ MR has been validated for clinical use on a 0.35T MR-Linac with results being comparable to an ArcCHECK-MR system in use clinically for almost five years. Most plan complexity metrics did not correlate with lower 2%/2mm gamma pass rates using the ArcCHECK-MR but several metrics were found to be moderately correlated with lower 2%/2mm global gamma pass rates for the Delta 4 Phantom+ MR.