Tailoring nonsurgical therapy for elderly patients with head and neck squamous cell carcinoma: A deep learning-based approach.

To assess deep learning models for personalized chemotherapy selection and quantify the impact of baseline characteristics on treatment efficacy for elderly head and neck squamous cell carcinoma (HNSCC) patients who are not surgery candidates. A comparison was made between patients whose treatments aligned with model recommendations and those whose did not, using overall survival as the primary metric. Bias was addressed through inverse probability treatment weighting (IPTW), and the impact of patient characteristics on treatment choice was analyzed via mixed-effects regression. Four thousand two hundred seventy-six elderly HNSCC patients in total met the inclusion criteria. Self-Normalizing Balanced individual treatment effect for survival data model performed best in treatment recommendation (IPTW-adjusted hazard ratio: 0.74, 95% confidence interval [CI], 0.63-0.87; IPTW-adjusted risk difference: 9.92%, 95% CI, 4.96-14.90; IPTW-adjusted the difference in restricted mean survival time: 16.42 months, 95% CI, 10.83-21.22), which surpassed other models and National Comprehensive Cancer Network guidelines. No survival benefit for chemoradiotherapy was seen for patients not recommended to receive this treatment. Self-Normalizing Balanced individual treatment effect for survival data model effectively identifies elderly HNSCC patients who could benefit from chemoradiotherapy, offering personalized survival predictions and treatment recommendations. The practical application will become a reality with further validation in clinical settings.

Simulation and validation studies of a large drift tube muon tracker.

Cosmic ray muons are massive, charged particles created from high energy cosmic rays colliding with atomic nuclei in Earth's atmosphere. Because of their high momenta and weak interaction, these muons can penetrate through large thicknesses of dense material before being absorbed, making them ideal for nondestructive imaging of objects composed of high-Z elements. A Giant Muon Tracker with two horizontal 8 × 6 in.2 and two vertical 6 × 6 in.2 modules of drift tubes was used to measure muon tracks passing through samples placed inside the detector volume. The experimental results were used to validate a Monte Carlo simulation of the Giant Muon Tracker. The imaging results of simulated samples were reconstructed and compared with those from the experiment, which showed excellent agreement.

Crustal Density Structure of the Jiuzhaigou Ms7.0 Earthquake Area Revealed by the Barkam-Jiuzhaigou-Wuqi Gravity Profile.

The Barkam-Jiuzhaigou-Wuqi gravity profile extends across the Jiuzhaigou Ms7.0 earthquake (in 2017) zone and passes through several historical big earthquakes' zones. We have obtained Bouguer gravity anomalies along the profile composed of 365 gravity observation stations with Global Positioning System (GPS) coordinates, analyzed the observed data and inverted subsurface density structure. The results show that the Moho depth has a big lateral variation from southwest to northeast, which shallows from 57 km to 43 km with maximum variation up to 14 km within 800 km. The most acute depth change of the Moho is in the boundary region between the Bayan Har block and West Qinling-Qilian block. According to our analysis, it is related to the eastward movement of the Bayan Har block. There are three main pieces of evidence that support it: (1) Density is higher in the east of the Bayan Har block and smaller in the west, which is the same as seismic activity; (2) Two thin low-density layers exist in the upper and middle crust of the Bayan Har block, which may promote inter-layer slip and the Jiuzhaigou Ms7.0 earthquake occurred in the boundary area of the two low-density layers, where the crustal density and Moho surface fluctuate sharply; (3) the GPS velocity field in the southwestern part gravity profile is significantly larger than that of the northeastern part, which is consistent with the density structure. Our studies also suggest that the large undulation of the Moho prevents the movement of the Bayan Har block, and strain is prone to accumulate here. The dynamic background analysis of the crust in this area indicates that the Moho surface uplifts in the West Qinling-Qilian block, which decelerates the eastern migration of material on the Qinghai-Tibet Plateau, and leads to the weak tectonic activity of the north part of the Bayan Har block.

Muon tomography for the analysis of in-container vitrified products.

Alternate treatment routes for radioactive waste are a key research area for much of the nuclear industry, with potentially significant savings available through volume reduction of waste. Achieving this requires a full and demonstrable understanding of waste product behaviour. For this purpose, the UK's National Nuclear Laboratory (NNL) has been collaborating with the University of Glasgow and Lynkeos Technology to develop passive techniques for analysis of waste containers over a number of years. In this instance, novel muon tomographic techniques have been applied to the analysis of thermally treated nuclear waste surrogates as part of a project to build and deploy a first of a kind muon imaging system for nuclear waste. The system has been deployed at NNL's Central Laboratory, Cumbria, UK, to analyse products from a series of thermal treatment technology trials, funded by the Nuclear Decommissioning Authority (NDA) through the Direct Research Portfolio (DRP). Analysis of the waste products using this technique has proven the value of muon analysis in the development of waste management technologies, proving an ability to understand the homogeneity of products and direct further destructive testing. Results from three different thermal treatment trials are presented, with three different surrogate intermediate level waste (ILW) forms in each.

Novel muon imaging techniques.

Owing to the high penetrating power of high-energy cosmic ray muons, muon imaging techniques can be used to image large bulky objects, especially objects with heavy shielding. Muon imaging systems work just like CT scanners in the medical imaging field-that is, they can reveal information inside of a target. There are two forms of muon imaging techniques: muon absorption imaging and muon multiple scattering imaging. The former is based on the flux attenuation of muons, and the latter is based on the multiple scattering of muons in matter. The muon absorption imaging technique is capable of imaging very large objects such as volcanoes and large buildings, and also smaller objects like spent fuel casks; the muon multiple scattering imaging technique is best suited to inspect smaller objects such as nuclear waste containers. Muon imaging techniques can be applied in a broad variety of fields, i.e. from measuring the magma thickness of volcanoes to searching for secret cavities in pyramids, and from monitoring the borders of countries checking for special nuclear materials to monitoring the spent fuel casks for nuclear safeguards applications. In this paper, the principles of muon imaging are reviewed. Image reconstruction algorithms such as Filtered Back Projection and Maximum Likelihood Expectation Maximization are discussed. The capability of muon imaging techniques is demonstrated through a Geant4 simulation study for imaging a nuclear spent fuel cask.This article is part of the Theo Murphy meeting issue 'Cosmic-ray muography'.

First-of-a-kind muography for nuclear waste characterization.

In the last decade, there has been a surge in the number of academic research groups and commercial companies exploiting naturally occurring cosmic-ray muons for imaging purposes in a range of industrial and geological applications. Since 2009, researchers at the University of Glasgow and the UK National Nuclear Laboratory (NNL) have pioneered this technique for the characterization of shielded nuclear waste containers with significant investment from the UK Nuclear Decommissioning Authority and Sellafield Ltd. Lynkeos Technology Ltd. was formed in 2016 to commercialize the Muon Imaging System (MIS) technology that resulted from this industry-funded academic research. The design, construction and performance of the Lynkeos MIS is presented along with first experimental and commercial results. The high-resolution images include the identification of small fragments of uranium within a surrogate 500-litre intermediate level waste container and metal inclusions within thermally treated GeoMelt® R&D Product Samples. The latter of these are from Lynkeos' first commercial contract with the UK National Nuclear Laboratory. The Lynkeos MIS will be deployed at the NNL Central Laboratory facility on the Sellafield site in Summer 2018 where it will embark upon a series of industry trials.This article is part of the Theo Murphy meeting issue 'Cosmic-ray muography'.