Comparative study of the collapsed cone convolution and Monte Carlo algorithms for radiation therapy planning of canine sinonasal tumors reveals significant dosimetric differences.

Computer-based radiation therapy requires high targeting and dosimetric precision. Analytical dosimetric algorithms typically are fast and clinically viable but can have increasing errors near air-bone interfaces. These are commonly found within dogs undergoing radiation planning for sinonasal cancer. This retrospective methods comparison study is designed to compare the dosimetry of both tumor volumes and organs at risk and quantify the differences between collapsed cone convolution (CCC) and Monte Carlo (MC) algorithms. Canine sinonasal tumor plans were optimized with CCC and then recalculated by MC with identical control points and monitor units. Planning target volume (PTV)air , PTVsoft tissue , and PTVbone were created to analyze the dose discrepancy within the PTV. Thirty imaging sets of dogs were included. Monte Carlo served as the gold standard calculation for the dosimetric comparison. Collapsed cone convolution overestimated the mean dose (Dmean ) to PTV and PTVsoft tissue by 0.9% and 0.5%, respectively (both P < 0.001). Collapsed cone convolution overestimated Dmean to PTVbone by 3% (P < 0.001). Collapsed cone convolution underestimated the near-maximum dose (D2 ) to PTVair by 1.1% (P < 0.001), and underestimated conformity index and homogeneity index in PTV (both P < 0.001). Mean doses of contralateral and ipsilateral eyes were overestimated by CCC by 1.6% and 1.7%, respectively (both P < 0.001). Near-maximum doses of skin and brain were overestimated by CCC by 2.2% and 0.7%, respectively (both P < 0.001). As clinical accessibility of Monte Carlo becomes more widespread, dose constraints may need to be re-evaluated with appropriate plan evaluation and follow-up.

Detection Study of Bipolar Depression Through the Application of a Model-Based Algorithm in Terms of Clinical Feature and Peripheral Biomarkers.

Objectives: The nature of the diagnostic classification of mood disorder is a typical dichotomous data problem and the method of combining different dimensions of evidences to make judgments might be more statistically reliable. In this paper, we aimed to explore whether peripheral neurotrophic factors could be helpful for early detection of bipolar depression. Methods: A screening method combining peripheral biomarkers and clinical characteristics was applied in 30 patients with major depressive disorder (MDD) and 23 patients with depressive episode of bipolar disorder. By a model-based algorithm, some information was extracted from the dataset and used as a "model" to approach penalized regression model for stably differential diagnosis for bipolar depression. Results: A simple and efficient model of approaching the diagnosis of individuals with depressive symptoms was established with a fitting degree (90.58%) and an acceptable cross-validation error rate. Neurotrophic factors of our interest were successfully screened out from the feature selection and optimized model performance as reliable predictive variables. Conclusion: It seems to be feasible to combine different types of clinical characteristics with biomarkers in order to detect bipolarity of all depressive episodes. Neurotrophic factors of our interest presented its stable discriminant potentiality in unipolar and bipolar depression, deserving validation analysis in larger samples.

Dosimetric comparison of different inhomogeneity correction algorithms for external photon beam dose calculations.

Dose calculation algorithm is one of the main sources of uncertainty in the radiotherapy sequences. The aim of this study was to compare the accuracy of different inhomogeneity correction algorithms for external photon beam dose calculations. The methodology was based on International Atomic Energy Agency TEC-DOC 1583. The phantom was scanned in every center, using computed tomography and seven tests were planned on three-dimensional treatment planning systems (TPSs). The doses were measured with ion chambers and the deviation between measured and TPS calculated dose was reported. This methodology was tested in five different hospitals which were using six different algorithms/inhomogeneity correction methods implemented in different TPSs. The algorithms in this study were divided into two groups: Measurement-based algorithms (type (a)) and model-based algorithms (type (b)). In type (a) algorithms, we saw 7.6% and 11.3% deviations out of agreement criteria for low- and high-energy photons, respectively. While in type (b) algorithms, these values were 4.3% and 5.1%, respectively. As a general trend, the numbers of measurements with results outside the agreement criteria increase with the beam energy and decrease with advancement of TPS algorithms. More advanced algorithm would be preferable and therefore should be implanted in clinical practice, especially for calculation in inhomogeneous medias like lung and bone and for high-energy beams calculation at low depth points.