Purpose: We investigated the dose enhancement and internalization of gold nanoparticles (AuNPs) used as a radiosensitizer agent for rotational radiotherapy of breast cancer using a kilovoltage (kV) X‐ray beam.Methods: Human breast cancer cells MDA‐MB‐231 were incubated with or without 100 μg/mL (4.87 nM) or 200 μg/mL (9.74 nM) 15 nm AuNPs and irradiated with 100 kV, 190 kV, or 6 MV X‐rays. To assess the toxicity of the AuNPs, we performed a Sulforhodamine B assay. Using atomic absorption spectroscopy, scanning electron microscopy, transmission electron microscopy, and time‐lapse optical microscopy (rate of 2 frames per minute), we carried out a quantitative assessment of the amount of gold internalized by MDA‐MB‐231 cells and a characterization of the static and dynamical aspects of this internalization process.Results: No effect of AuNPs alone was shown on cell viability. Time‐lapse optical microscopy showed for the first time AuNPs cellular uptake and the dynamics of AuNPs internalization. Electron microscopy demonstrated AuNPs localization in endosomal vesicles, preferentially in the perinuclear region. After irradiation at doses up to 2 Gy, cell survival fraction curves showed increased mortality with AuNPs, with respect to irradiation without AuNPs. The highest effect of radioenhancement by AuNPs (at 9.74 nM AuNPs concentration) was observed at 190 kV showing a dose enhancement factor of 1.33 ± 0.06 (1.34 ± 0.02 at 100 kV), while at 6 MV it was 1.14 ± 0.06.Conclusions: The observed radio‐sensitization effect is promising for future radio‐enhanced kV radiotherapy of breast cancer and quantitatively in the order of previous observations for 15 nm AuNPs. These results of a significant dose enhancement were obtained at 15 nm AuNPs concentration as low as several nanomolar units, at dose levels typical of a single dose fraction in a radiotherapy session. Dynamical behavior of the 3D spatial distribution of 15 nm AuNPs outside the nucleus of single breast cancer cell was observed, with possible implications for future models of AuNPs sensitization.

The association between dose to selected bladder and rectum symptom-related sub-regions (SRS) and late toxicity after prostate cancer radiotherapy has been evidenced by voxel-wise analyses. The aim of the current study was to explore the feasibility of combining knowledge-based (KB) and multi-criteria optimization (MCO) to spare SRSs without compromising planning target volume (PTV) dose delivery, including pelvic-node irradiation. Forty-five previously treated patients (74.2 Gy/28fr) were selected and SRSs (in the bladder, associated with late dysuria/hematuria/retention; in the rectum, associated with bleeding) were generated using deformable registration. A KB model was used to obtain clinically suitable plans (KB-plan). KB-plans were further optimized using MCO, aiming to reduce dose to the SRSs while safeguarding target dose coverage, homogeneity and avoiding worsening dose volume histograms of the whole bladder, rectum and other organs at risk. The resulting MCO-generated plans were examined to identify the best-compromise plan (KB + MCO-plan). The mean SRS dose decreased in almost all patients for each SRS. D1% also decreased in the large majority, less frequently for dysuria/bleeding SRS. Mean differences were statistically significant (p < 0.05) and ranged between 1.3 and 2.2 Gy with maximum reduction of mean dose up to 3–5 Gy for the four SRSs. The better sparing of SRSs was obtained without compromising PTVs coverage. Selectively sparing SRSs without compromising PTV coverage is feasible and has the potential to reduce toxicities in prostate cancer radiotherapy. Further investigation to better quantify the expected risk reduction of late toxicities is warranted.

In order to optimize the radiotherapy treatment and minimize toxicities, organs-at-risk (OARs) and clinical target volume (CTV) must be segmented. Deep Learning (DL) techniques show significant potential for performing this task effectively. The availability of a large single-institute data sample, combined with additional numerous multi-centric data, makes it possible to develop and validate a reliable CTV segmentation model. Planning CT data of 1822 patients were available (861 from a single center for training and 961 from 8 centers for validation). A preprocessing step, aimed at standardizing all the images, followed by a 3D-Unet capable of segmenting both right and left CTVs was implemented. The metrics used to evaluate the performance were the Dice similarity coefficient (DSC), the Hausdorff distance (HD), and its 95th percentile variant (HD_95) and the Average Surface Distance (ASD). The segmentation model achieved high performance on the validation set (DSC: 0.90; HD: 20.5 mm; HD_95: 10.0 mm; ASD: 2.1 mm; epoch 298). Furthermore, the model predicted smoother contours than the clinical ones along the cranial–caudal axis in both directions. When applied to internal and external data the same metrics demonstrated an overall agreement and model transferability for all but one (Inst 9) center. . A 3D-Unet for CTV segmentation trained on a large single institute cohort consisting of planning CTs and manual segmentations was built and externally validated, reaching high performance.