Dosimetric Evaluation of the Treatment Plan on Indigenous Heterogeneous Phantoms using Analytical Anisotropic Algorithm and Acuros-XB Algorithm for Different Photon Energies.

Background: Modern radiotherapy techniques are using advanced algorithms; however, phantoms used for quality assurance have homogeneous density; accordingly, the development of heterogeneous phantom mimicking human body sites is imperative to examine variation between planned and delivered doses. Objective: This study aimed to analyze the accuracy of planned dose by different algorithms using indigenously developed heterogeneous thoracic phantom (HT). Material and Methods: In this experimental study, computed tomography (CT) of HT was done, and the density of different parts was measured. The plan was generated on CT images of HCP with 6 and 15 Megavoltage (MV) photon beams using different treatment techniques, including three-dimensional conformal radiotherapy (3D-CRT), intensity-modulated radiation therapy (IMRT), and volumetric modulated arc therapy (VMAT). Plans were delivered by the linear accelerator, and the dose was measured using the ion chamber (IC) placed in HT; planned and measured doses were compared. Results: Density patterns for different parts of the fabricated phantom, including rib, spine, scapula, lung, chest wall, and heart were 1.849, 1.976, 1.983, 0.173, 0.855, and 0.833 g/cc, respectively. Variation between planned and IC estimated doses with the tolerance (±5%) for all photon energies using different techniques. Acuros-XB (AXB) showed a slightly higher variation between computed and IC estimated doses using HCP compared to the analytical anisotropic algorithm (AAA). Conclusion: The indigenous heterogeneous phantom can accurately simulate the dosimetric scenario for different algorithms (AXB or AAA) and be also utilized for routine patient-specific QA.

Distress Screening in Head and Neck Cancer Patients Planned for Cancer-Directed Radiotherapy.

OBJECTIVE/HYPOTHESIS: To estimate the prevalence of baseline clinically significant distress (distress score ≥ 4) in head and neck cancer patients planned and treated with radical intent radiotherapy using the National Comprehensive Cancer Network Distress Thermometer (DT) and assess factors predictive of distress. STUDY DESIGN: Cross-sectional study. METHODS: This was a cross-sectional study evaluating distress in 600 head and neck cancer patients undergoing radiation therapy. The DT was used to screen patients for distress at baseline before radiotherapy. RESULTS: The median distress score of the entire cohort was 4 interquartile range (IQR) (IQR: 3-5), and 340 patients (56.7%) had clinically significant distress. On univariate analysis, the causal factors predictive of distress were low socioeconomic status (P = .04), presence of proliferative growth at presentation (P = .008), site of the tumor (oral cavity, P = .02), comorbidity (P = .04), and presence of Ryle's tube or tracheostomy tube at baseline (P = .01). Low socioeconomic status was significant (P = .04) on multivariate analysis for high levels of distress. CONCLUSIONS: Among head and neck cancer patients, 56% of patients had clinically significant baseline distress, and patients with low socioeconomic status had high distress. There is a need for interventions to mitigate distress. LEVEL OF EVIDENCE: 4 Laryngoscope, 131:2023-2029, 2021.

APT: An Automated Probe Tracker From Gene Expression Data.

Out of currently available semi-automatic tools for detecting diagnostic probes relevant to a pathophysiological condition, ArrayMining and GEO2R of NCBI are most popular. The shortcomings of ArrayMining and GEO2R are that both tools list the probes ordering them on the basis of their individual statistical level of significances with only difference of statistical methods used by them. While the latest tool GEO2R outputs either top 250 or all genes following its own ranking mechanism, ArrayMining requires number of probes to be inputted by the user. This study provided a way for automatic selection of probe-set that can be obtained from the voting of outputs resulted from statistical methods, t-Test, Mann-Whitney Test and Empirical Bayes Moderated t-test. It was also intriguing to find that the parameters of these statistical methods can be represented as a mathematical function of group fisher's discriminant ratio of a disease-control expression data-pair. Result of this fully automatic method, APT shows 88.97 percent success in comparison to 80.40 and 87.60 percent successes of ArrayMining and GEO2R respectively to include reported probes. Furthermore, out of 10 fold cross validation and 5 new test cases, APT shows a better performance than both ArrayMining and GEO2R in regards to sensitivity and specificity.