Background To compare the impact of uncertainties and interplay effect on 3D and 4D robustly optimized intensity-modulated proton therapy (IMPT) plans for lung cancer in an exploratory methodology study. Methods IMPT plans were created for 11 non-randomly selected non-small-cell lung cancer (NSCLC) cases: 3D robustly optimized plans on average CTs with internal gross tumor volume density overridden to irradiate internal target volume, and 4D robustly optimized plans on 4D CTs to irradiate clinical target volume (CTV). Regular fractionation (66 Gy[RBE] in 33 fractions) were considered. In 4D optimization, the CTV of individual phases received non-uniform doses to achieve a uniform cumulative dose. The root-mean-square-dose volume histograms (RVH) measured the sensitivity of the dose to uncertainties, and the areas under the RVH curve (AUCs) were used to evaluate plan robustness. Dose evaluation software modeled time-dependent spot delivery to incorporate interplay effect with randomized starting phases of each field per fraction. Dose-volume histogram indices comparing CTV coverage, homogeneity, and normal tissue sparing were evaluated using Wilcoxon signed-rank test. Results 4D robust optimization plans led to smaller AUC for CTV (14.26 vs. 18.61 (p=0.001), better CTV coverage (Gy[RBE]) [D95% CTV: 60.6 vs 55.2 (p=0.001)], and better CTV homogeneity [D5%–D95% CTV: 10.3 vs 17.7 (p=0.002)] in the face of uncertainties. With interplay effect considered, 4D robust optimization produced plans with better target coverage [D95% CTV: 64.5 vs 63.8 (p=0.0068)], comparable target homogeneity, and comparable normal tissue protection. The benefits from 4D robust optimization were most obvious for the 2 typical stage III lung cancer patients. Conclusions Our exploratory methodology study showed that, compared to 3D robust optimization, 4D robust optimization produced significantly more robust and interplay-effect-resistant plans for targets with comparable dose distributions for normal tissues. A further study with a larger and more realistic patient population is warranted to generalize the conclusions.

Using an eigenspectrum decomposition technique, we separate the host galaxy from the broad line active galactic nucleus (AGN) in a set of 4666 spectra from the Sloan Digital Sky Survey (SDSS), from redshifts near zero up to about 0.75. The decomposition technique uses separate sets of galaxy and quasar eigenspectra to efficiently and reliably separate the AGN and host spectroscopic components. The technique accurately reproduces the host galaxy spectrum, its contributing fraction, and its classification. We show how the accuracy of the decomposition depends upon S/N, host galaxy fraction, and the galaxy class. Based on the eigencoefficients, the sample of SDSS broad-line AGN host galaxies spans a wide range of spectral types, but the distribution differs significantly from inactive galaxies. In particular, post-starburst activity appears to be much more common among AGN host galaxies. The luminosities of the hosts are much higher than expected for normal early-type galaxies, and their colors become increasingly bluer than early-type galaxies with increasing host luminosity. Most of the AGNs with detected hosts are emitting at between 1% and 10% of their estimated Eddington luminosities, but the sensitivity of the technique usually does not extend to the Eddington limit. There are mild correlations among the AGN and host galaxy eigencoefficients, possibly indicating a link between recent star formation and the onset of AGN activity. The catalog of spectral reconstruction parameters is available as an electronic table.Comment: 18 pages; accepted for publication in A

We discuss an analytic approach for modeling structure formation in sheets, filaments, and knots. This is accomplished by combining models of triaxial collapse with the excursion set approach: sheets are defined as objects that have collapsed along only one axis, filaments have collapsed along two axes, and halos are objects in which triaxial collapse is complete. In the simplest version of this approach, which we develop here, large-scale structure shows a clear hierarchy of morphologies: the mass in large-scale sheets is partitioned up among lower mass filaments, which themselves are made up of still lower mass halos. Our approach provides analytic estimates of the mass fraction in sheets, filaments, and halos and its evolution, for any background cosmological model and any initial fluctuation spectrum. In the currently popular ÃCDM model, our analysis suggests that more than 99% of the cosmic mass is in sheets, and 72% in filaments, with mass larger than 10 10 M at the present time. For halos, this number is only 46%. Our approach also provides analytic estimates of how halo abundances at any given time correlate with the morphology of the surrounding large-scale structure and how halo evolution correlates with the morphology of largescale structure. Subject headingg : large-scale structure of universe

Background We compared conventionally optimized intensity-modulated proton therapy (IMPT) treatment plans against the worst-case scenario optimized treatment plans for lung cancer. The comparison of the two IMPT optimization strategies focused on the resulting plans’ ability to retain dose objectives under the influence of patient set-up, inherent proton range uncertainty, and dose perturbation caused by respiratory motion. Methods For each of the 9 lung cancer cases two treatment plans were created accounting for treatment uncertainties in two different ways: the first used the conventional method: delivery of prescribed dose to the planning target volume (PTV) that is geometrically expanded from the internal target volume (ITV). The second employed the worst-case scenario optimization scheme that addressed set-up and range uncertainties through beamlet optimization. The plan optimality and plan robustness were calculated and compared. Furthermore, the effects on dose distributions of the changes in patient anatomy due to respiratory motion was investigated for both strategies by comparing the corresponding plan evaluation metrics at the end-inspiration and end-expiration phase and absolute differences between these phases. The mean plan evaluation metrics of the two groups were compared using two-sided paired t-tests. Results Without respiratory motion considered, we affirmed that worst-case scenario optimization is superior to PTV-based conventional optimization in terms of plan robustness and optimality. With respiratory motion considered, worst-case-scenario optimization still achieved more robust dose distributions to respiratory motion for targets and comparable or even better plan optimality [D95% ITV: 96.6% versus 96.1% (p=0.26), D5% − D95% ITV: 10.0% versus 12.3% (p=0.082), D1% spinal cord: 31.8% versus 36.5% (p =0.035)]. Conclusions Worst-case scenario optimization led to superior solutions for lung IMPT. Despite of the fact that worst-case-scenario optimization did not explicitly account for respiratory motion it produced motion-resistant treatment plans. However, further research is needed to incorporate respiratory motion into IMPT robust optimization.

Robust optimization with a small spot-machine significantly improves heart and esophagus sparing, with comparable plan robustness and interplay effects compared with robust optimization with a large-spot machine. A small-spot machine uses a larger number of spots to cover the same tumors compared with a large-spot machine, which gives the planning system more freedom to compensate for the higher sensitivity to uncertainties and interplay effects for lung cancer treatments.