The conversion of computed tomography (CT) numbers into material composition and mass density data influences the accuracy of patient dose calculations in Monte Carlo treatment planning (MCTP). The aim of our work was to develop a CT conversion scheme by performing a stoichiometric CT calibration. Fourteen dosimetrically equivalent tissue subsets (bins), of which ten bone bins, were created. After validating the proposed CT conversion scheme on phantoms, it was compared to a conventional five bin scheme with only one bone bin. This resulted in dose distributions D₁₄and D₅for nine clinical patient cases in a European multi-centre study. The observed local relative differences in dose to medium were mostly smaller than 5%. The dose–volume histograms of both targets and organs at risk were comparable, although within bony structures D₁₄was found to be slightly but systematically higher than D₅. Converting dose to medium to dose to water (D₁₄to D_14watand D₅to D_5wat) resulted in larger local differences as D_5watbecame up to 10% higher than D_14wat. In conclusion, multiple bone bins need to be introduced when Monte Carlo (MC) calculations of patient dose distributions are converted to dose to water.

Absolute dosimetry with ionization chambers of the narrow photon fields used in stereotactic techniques and IMRT beamlets is constrained by lack of electron equilibrium in the radiation field. It is questionable that stopping-power ratio in dosimetry protocols, obtained for broad photon beams and quasi-electron equilibrium conditions, can be used in the dosimetry of narrow fields while keeping the uncertainty at the same level as for the broad beams used in accelerator calibrations. Monte Carlo simulations have been performed for two 6 MV clinical accelerators (Elekta SL-18 and Siemens Mevatron Primus), equipped with radiosurgery applicators and MLC. Narrow circular and Z-shaped on-axis and off-axis fields, as well as broad IMRT configured beams, have been simulated together with reference 10 x 10 cm2 beams. Phase-space data have been used to generate 3D dose distributions which have been compared satisfactorily with experimental profiles (ion chamber, diodes and film). Photon and electron spectra at various depths in water have been calculated, followed by Spencer-Attix (delta = 10 keV) stopping-power ratio calculations which have been compared to those used in the IAEA TRS-398 code of practice. For water/air and PMMA/air stopping-power ratios, agreements within 0.1% have been obtained for the 10 x 10 cm2 fields. For radiosurgery applicators and narrow MLC beams, the calculated s(w,air) values agree with the reference within +/-0.3%, well within the estimated standard uncertainty of the reference stopping-power ratios (0.5%). Ionization chamber dosimetry of narrow beams at the photon qualities used in this work (6 MV) can therefore be based on stopping-power ratios data in dosimetry protocols. For a modulated 6 MV broad beam used in clinical IMRT, s(w,air) agrees within 0.1% with the value for 10 x 10 cm2, confirming that at low energies IMRT absolute dosimetry can also be based on data for open reference fields. At higher energies (24 MV) the difference in s(w,air) was up to 1.1%, indicating that the use of protocol data for narrow beams in such cases is less accurate than at low energies, and detailed calculations of the dosimetry parameters involved should be performed if similar accuracy to that of 6 MV is sought.

Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in voxel size resampling and interpolation, image perturbation, or slice thickness. This study aims to observe the variability of positron emission tomography (PET) radiomics features under the impact of co-registration with magnetic resonance imaging (MRI) using the difference percentage coefficient, and the Spearman’s correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-registration with T1-weighted MRI and (iii) PET after co-registration with FLAIR MRI. Specifically, seventeen patients with brain cancers undergoing [11C]-Methionine PET were considered. Successively, PET images were co-registered with MRI sequences and 107 features were extracted for each mentioned group of images. The variability analysis revealed that shape features, first-order features and two subgroups of higher-order features possessed a good robustness, unlike the remaining groups of features, which showed large differences in the difference percentage coefficient. Furthermore, using the Spearman’s correlation coefficient, approximately 40% of the selected features differed from the three mentioned groups of images. This is an important consideration for users conducting radiomics studies with image co-registration constraints to avoid errors in cancer diagnosis, prognosis, and clinical outcome prediction.

Boron Neutron Capture Therapy (BNCT) is a promising binary disease-targeted therapy, as neutrons preferentially kill cells labeled with boron (¹⁰B), which makes it a precision medicine treatment modality that provides a therapeutic effect exclusively on patient-specific tumor spread. Contrary to what is usual in radiotherapy, BNCT proposes cell-tailored treatment planning rather than to the tumor mass. The success of BNCT depends mainly on the sufficient spatial biodistribution of ¹⁰B located around or within neoplastic cells to produce a high-dose gradient between the tumor and healthy tissue. However, it is not yet possible to precisely determine the concentration of ¹⁰B in a specific tissue in real-time using non-invasive methods. Critical issues remain to be resolved if BNCT is to become a valuable, minimally invasive, and efficient treatment. In addition, functional imaging technologies, such as PET, can be applied to determine biological information that can be used for the combined-modality radiotherapy protocol for each specific patient. Regardless, not only imaging methods but also proteomics and gene expression methods will facilitate BNCT becoming a modality of personalized medicine. This work provides an overview of the fundamental principles, recent advances, and future directions of BNCT as cell-targeted cancer therapy for personalized radiation treatment.