The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

Boron neutron capture therapy (BNCT) has the potential to specifically destroy tumor cells without damaging the tissues infiltrated by the tumor. BNCT is a binary treatment method based on the combination of two agents that have no effect when applied individually: ¹⁰B and thermal neutrons. Exclusively, the combination of both produces an effect, whose extent depends on the amount of ¹⁰B in the tumor but also on the organs at risk. It is not yet possible to determine the ¹⁰B concentration in a specific tissue using non-invasive methods. At present, it is only possible to measure the ¹⁰B concentration in blood and to estimate the boron concentration in tissues based on the assumption that there is a fixed uptake of ¹⁰B from the blood into tissues. On this imprecise assumption, BNCT can hardly be developed further. A therapeutic approach, combining the boron carrier for therapeutic purposes with an imaging tool, might allow us to determine the ¹⁰B concentration in a specific tissue using a non-invasive method. This review provides an overview of the current clinical protocols and preclinical experiments and results on how innovative drug development for boron delivery systems can also incorporate concurrent imaging. The last section focuses on the importance of proteomics for further optimization of BNCT, a highly precise and personalized therapeutic approach.

We developed an efficient technique to auto-propagate parotid gland contours from planning kVCT to daily MVCT images of head-and-neck cancer patients treated with helical tomotherapy. The method deformed a 3D surface mesh constructed from manual kVCT contours by B-spline free-form deformation to generate optimal and smooth contours. Deformation was calculated by elastic image registration between kVCT and MVCT images. Data from ten head-and-neck cancer patients were considered and manual contours by three observers were included in both kVCT and MVCT images. A preliminary inter-observer variability analysis demonstrated the importance of contour propagation in tomotherapy application: a high variability was reported in MVCT parotid volume estimation (p = 0.0176, ANOVA test) and a larger uncertainty of MVCT contouring compared with kVCT was demonstrated by DICE and volume variability indices (Wilcoxon signed rank test, p < 10(-4) for both indices). The performance analysis of our method showed no significant differences between automatic and manual contours in terms of volumes (p > 0.05, in a multiple comparison Tukey test), center-of-mass distances (p = 0.3043, ANOVA test), DICE values (p = 0.1672, Wilcoxon signed rank test) and average and maximum symmetric distances (p = 0.2043, p = 0.8228 Wilcoxon signed rank tests). Results suggested that our contour propagation method could successfully substitute human contouring on MVCT images.