Radiomics is being explored for potential applications in radiation therapy. How various imaging protocols affect quantitative image features is currently a highly active area of research. To assess the variability of image features derived from conventional [three-dimensional (3D)] and respiratory-gated (RG) positron emission tomography (PET)/computed tomography (CT) images of lung cancer patients, image features were computed from 23 lung cancer patients. Both protocols for each patient were acquired during the same imaging session. PET tumor volumes were segmented using an adaptive technique which accounted for background. CT tumor volumes were delineated with a commercial segmentation tool. Using RG PET images, the tumor center of mass motion, length, and rotation were calculated. Fifty-six image features were extracted from all images consisting of shape descriptors, first-order features, and second-order texture features. Overall, 26.6% and 26.2% of total features demonstrated less than 5% difference between 3D and RG protocols for CT and PET, respectively. Between 10 RG phases in PET, 53.4% of features demonstrated percent differences less than 5%. The features with least variability for PET were sphericity, spherical disproportion, entropy (first and second order), sum entropy, information measure of correlation 2, Short Run Emphasis (SRE), Long Run Emphasis (LRE), and Run Percentage (RPC); and those for CT were minimum intensity, mean intensity, Root Mean Square (RMS), Short Run Emphasis (SRE), and RPC. Quantitative analysis using a 3D acquisition versus RG acquisition (to reduce the effects of motion) provided notably different image feature values. This study suggests that the variability between 3D and RG features is mainly due to the impact of respiratory motion.
Targeted alpha-particle therapy (TAT) aims to selectively deliver radionuclides emitting α-particles (cytotoxic payload) to tumors by chelation to monoclonal antibodies, peptides or small molecules that recognize tumor-associated antigens or cell-surface receptors. Because of the high linear energy transfer (LET) and short range of alpha (α) particles in tissue, cancer cells can be significantly damaged while causing minimal toxicity to surrounding healthy cells. Recent clinical studies have demonstrated the remarkable efficacy of TAT in the treatment of metastatic, castration-resistant prostate cancer. In this comprehensive review, we discuss the current consensus regarding the properties of the α-particle-emitting radionuclides that are potentially relevant for use in the clinic; the TAT-mediated mechanisms responsible for cell death; the different classes of targeting moieties and radiometal chelators available for TAT development; current approaches to calculating radiation dosimetry for TATs; and lead optimization via medicinal chemistry to improve the TAT radiopharmaceutical properties. We have also summarized the use of TATs in pre-clinical and clinical studies to date.
RET fusions have been found in lung adenocarcinoma, of which KIF5B-RET is the most prevalent. We established inducible KIF5B-RET transgenic mice and KIF5B-RET-dependent cell lines for preclinical modeling of KIF5B-RET-associated lung adenocarcinoma. Dox-induced CCSP-rtTA/tetO-KIF5B-RET transgenic mice developed invasive lung adenocarcinoma with desmoplastic reaction. Tumors regressed upon suppression of KIF5B-RET expression. By culturing KIF5B-RET-dependent BaF3 (B/KR) cells with increasing concentrations of cabozantinib or vandetanib, we identified cabozantinib-resistant RETV804L mutation and vandetanib-resistant-RETG810A mutation. Among cabozantinib, lenvatinib, ponatinib, and vandetanib, ponatinib was identified as the most potent inhibitor against KIF5B-RET and its drug-resistant mutants. Interestingly, the vandetanib-resistant KIF5B-RETG810A mutant displayed gain-of-sensitivity (GOS) to ponatinib and lenvatinib. Treatment of Dox-induced CCSP-rtTA/tetO-KIF5B-RET bitransgenic mice with ponatinib effectively induced tumor regression. These results indicate that KIF5B-RET-associated lung tumors are addicted to the fusion oncogene and ponatinib is the most effective inhibitor for targeting KIF5B-RET in lung adenocarcinoma. Moreover, this study finds a novel vandetanib-resistant RETG810A mutation and identifies lenvatinib and ponatinib as the secondary drugs to overcome this vandetanib resistance mechanism.
It is shown that the introduction of zero-point energy and thermal effects to density functional theory with an empirical van der Waals correction results in a significant improvement in the prediction of equilibrium volumes and isothermal equations of state for hydrostatic compressions of energetic materials at nonzero temperatures. This method can be used to predict the thermophysical properties of these materials for a wide range of pressures and temperatures.
By decoupling time and length scales in moving window molecular dynamics shock-wave simulations, a new regime of shock-wave propagation is uncovered characterized by a two-zone elastic-plastic shock-wave structure consisting of a leading elastic front followed by a plastic front, both moving with the same average speed and having a fixed net thickness that can extend to microns. The material in the elastic zone is in a metastable state that supports a pressure that can substantially exceed the critical pressure characteristic of the onset of the well-known split-elastic-plastic, two-wave propagation. The two-zone elastic-plastic wave is a general phenomenon observed in simulations of a broad class of crystalline materials and is within the reach of current experimental techniques.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.