Lung cancer is the leading cause of cancer-related deaths worldwide, and elucidation of its complicated pathobiology has been traditionally targeted by studies incorporating genomic as well other high-throughput approaches. Recently, a collection of methods used for cancer imaging, supplemented by quantitative aspects leading towards imaging biomarker assessment termed “radiomics”, has introduced a novel dimension in cancer research. Integration of genomics and radiomics approaches, where identifying the biological basis of imaging phenotypes is feasible due to the establishment of associations between molecular features at the genomic–transcriptomic–proteomic level and radiological features, has recently emerged termed radiogenomics. This review article aims to briefly describe the main aspects of radiogenomics, while discussing its basic limitations related to lung cancer clinical applications for clinicians, researchers and patients.

The aim of this study was to develop a bioimaging probe based on magnetic iron oxide nanoparticles (MIONs) surface functionalized with the copolymer (p(MAA-g-EGMA)), which were radiolabeled with the positron emitter Gallium-68. The synthesis of the hybrid MIONs was realized by hydrolytic condensation of a single ferrous precursor in the presence of the copolymer. The synthesized MagP MIONs displayed an average D_h of 87 nm, suitable for passive targeting of cancerous tissues through the enhanced permeation and retention (EPR) effect after intravenous administration, while their particularly high magnetic content ascribes strong magnetic properties to the colloids. Two different approaches were explored to develop MIONs radiolabeled with ⁶⁸Ga: the chelator-mediated approach, where the chelating agent NODAGA-NHS was conjugated onto the MIONs (MagP-NODAGA) to form a chelate complex with ⁶⁸Ga, and the chelator-free approach, where ⁶⁸Ga was directly incorporated onto the MIONs (MagP). Both groups of NPs showed highly efficient radiolabeling with ⁶⁸Ga, forming constructs which were stable with time, and in the presence of PBS and human serum. Ex vivo biodistribution studies of [⁶⁸Ga]Ga- MIONs showed high accumulation in the mononuclear phagocyte system (MPS) organs and satisfactory blood retention with time. In vivo PET imaging with [⁶⁸Ga]Ga-MagP MIONs was in accordance with the ex vivo biodistribution results. Finally, the MIONs showed low toxicity against 4T1 breast cancer cells. These detailed studies established that [⁶⁸Ga]Ga- MIONs exhibit potential for application as tracers for early cancer detection.

An efficient, two-dimensional, analytic, Spline Reconstruction Technique (SRT) has been presented earlier in the literature. This technique involves the Hilbert transform of the sinogram which is approximated in terms of natural cubic splines. The aim of this study is to evaluate the SRT algorithm using Monte-Carlo simulated sinograms and real PET data, in comparison with three commonly used reconstruction algorithms: FBP, MLEM and OSEM. For the simulation studies, a digital Hoffman phantom, a NEMA-like and a Derenzo phantom were employed, and Monte Carlo methods were used for the simulation of the activity distribution in the source and the resulting generation of positron-electron annihilations. No noise, scatter and absorption conditions were assumed. The phantoms were generated with different image activities. The relevant modeled system was a single-ring tomograph with 234 scintillation crystals. Image grids with an image size of 128 × 128 pixels were employed. For the studies of real data, PET sinograms of an FDG injected mouse and a NEMA and Derenzo phantom were acquired from an ARGUS-CT small animal PET/CT system. Both the simulated and real sinograms were reconstructed using the SRT algorithm and the reconstructed images were compared to those of FBP, MLEM and OSEM. The contrast and SNR were calculated for the simulated NEMA-like and Hofmann phantom by drawing ROIs within the images. Our results indicate that SRT and FBP give reconstructed images of comparable quality with respect to the number of counts. Striking artifacts become worse at lower total counts for both methods. SRT reconstructed images exhibit higher SNR in comparison with FBP and, in some cases, in comparison with MLEM and OSEM. SRT reconstructed images exhibit higher contrast over FBP but not over MLEM and OSEM. The reconstruction time for SRT was about 20 sec per slice, hence SRT is faster than MLEM and OSEM (for high activity images), but slower than FBP. In conclusion, SRT is a linear algorithm which can serve as a good alternative to FBP, providing images with higher contrast and SNR values. Furthermore, it has the crucial advantage that it can accommodate complicated detector geometries.