Cancer Treatment Using Different Shapes of Gold-Based Nanomaterials in Combination with Conventional Physical Techniques.

The conventional methods of cancer treatment and diagnosis, such as radiotherapy, chemotherapy, and computed tomography, have developed a great deal. However, the effectiveness of such methods is limited to the possible failure or collateral effects on the patients. In recent years, nanoscale materials have been studied in the field of medical physics to develop increasingly efficient methods to treat diseases. Gold nanoparticles (AuNPs), thanks to their unique physicochemical and optical properties, were introduced to medicine to promote highly effective treatments. Several studies have confirmed the advantages of AuNPs such as their biocompatibility and the possibility to tune their shapes and sizes or modify their surfaces using different chemical compounds. In this review, the main properties of AuNPs are analyzed, with particular focus on star-shaped AuNPs. In addition, the main methods of tumor treatment and diagnosis involving AuNPs are reviewed.

Shape-Driven Response of Gold Nanoparticles to X-rays.

Radiotherapy (RT) involves delivering X-ray beams to the tumor site to trigger DNA damage. In this approach, it is fundamental to preserve healthy cells and to confine the X-ray beam only to the malignant cells. The integration of gold nanoparticles (AuNPs) in the X-ray methodology could be considered a powerful tool to improve the efficacy of RT. Indeed, AuNPs have proven to be excellent allies in contrasting tumor pathology upon RT due to their high photoelectric absorption coefficient and unique physiochemical properties. However, an analysis of their physical and morphological reaction to X-ray exposure is necessary to fully understand the AuNPs' behavior upon irradiation before treating the cells, since there are currently no studies on the evaluation of potential NP morphological changes upon specific irradiations. In this work, we synthesized two differently shaped AuNPs adopting two different techniques to achieve either spherical or star-shaped AuNPs. The spherical AuNPs were obtained with the Turkevich-Frens method, while the star-shaped AuNPs (AuNSs) involved a seed-mediated approach. We then characterized all AuNPs with Transmission Electron Microscopy (TEM), Uv-Vis spectroscopy, Dynamic Light Scattering (DLS), zeta potential and Fourier Transform Infrared (FTIR) spectroscopy. The next step involved the treatment of AuNPs with two different doses of X-radiation commonly used in RT, namely 1.8 Gy and 2 Gy, respectively. Following the X-rays' exposure, the AuNPs were further characterized to investigate their possible physicochemical and morphological alterations induced with the X-rays. We found that AuNPs do not undergo any alteration, concluding that they can be safely used in RT treatments. Lastly, the actin rearrangements of THP-1 monocytes treated with AuNPs were also assessed in terms of coherency. This is a key proof to evaluate the possible activation of an immune response, which still represents a big limitation for the clinical translation of NPs.

The 3D isodose structure-based method for clinical dose distributions comparison in pretreatment patient-QA.

INTRODUCTION: Before the approval of any Intensity Modulated Radiation Therapy or Volumetric Modulated Arc Therapy treatment plan, quality assurance (QA) tests are needed to reveal potential errors such as an inaccurate calculation of the dose distribution, the failure of the record-and-verify system, or the delivery system of the linear accelerator. Currently, the method adopted to compare the measured dose distribution with the treatment planning system TPS calculated dose distribution is gamma analysis. However, gamma analysis has been shown to be ineffective for the clinical evaluation of treatment plans. We proposed and tested a new method (the isodose structures method) alternative to gamma analysis. METHOD: Different errors were introduced in 33 error-free Head and Neck plans. The modified plans were recalculated using TPS software and the dose distributions obtained were compared to those of the original (error-free) plans. The comparison was performed using gamma analysis and the new method. The target was to calculate overall and organ-specific gamma passing rates as well as the overlapping ratio (OR) and volume ratio (VR) factors of the isodose structures method for each error-included plan. RESULTS: Eight of the 33 plans passed both the gamma analysis and the isodose structures (IS) analysis, ten plans did not pass either of them, while 13 plans which did not pass the IS analysis, passed the gamma analysis. Two plans which did not pass gamma, passed IS analysis. Furthermore, Dose Volume Histogram (DVH) metrics could not detect the low agreement between the dose distributions of two error-free plans and the respective modified plans. In this case, the IS analysis also allowed us to detect clinically meaningful differences between measured and TPS dose distributions. CONCLUSIONS: The IS method analysis clearly showed a high efficiency in detecting clinically relevant differences between TPS and measured dose distributions not seen in gamma analysis and in DVH-based metrics. Therefore, IS analysis proved to be a valid tool, alternative to gamma analysis for dose comparison in patient-specific QA test.