Radiation therapy is an established method of cancer treatment. New technologies in cancer radiotherapy need a more accurate computation of the dose delivered in the radiotherapy treatment plan. This study presents some results of a Geant4-based application for simulation of the absorbed dose distribution given by a medical linear accelerator (LINAC). The LINAC geometry is accurately described in the Monte Carlo code with use of the accelerator manufacturer's specifications. The capability of the software for evaluating the dose distribution has been verified by comparisons with measurements in a water phantom; the comparisons were performed for percentage depth dose (PDD) and profiles for various field sizes and depths, for a 6-MV electron beam. Experimental and calculated dose values were in good agreement both in PDD and in transverse sections of the water phantom.

Gamma spectroscopy is an important analytical technique across various fields. Gamma spectroscopy uses the energy spectra of emitted gamma rays to examine the type and quantity of isotopes that exist in samples. Like any other analytical technique, traditional gamma spectroscopy encounters challenges that in some cases make the analysis uncertain. Machine Learning (ML) algorithms have been proposed as an approach for enhancing the precision and robustness of gamma spectroscopy. The current study introduces the basics behind ML and illustrates how they are applied in gamma spectroscopy using case studies. Major findings discussed herein demonstrate the developmental capability that ML has in improving gamma spectroscopy. Radioisotope identification, optimizing detector performance, and simplifying environmental monitoring processes have been the main areas in which ML algorithms have been deployed for improvement. These include the ability to predict and provide real-time spectrometry, among others, even though these opportunities come with their shortfalls such as the necessity for huge training datasets. This review explains that integrating ML into gamma spectroscopy marks a major change from current analytic techniques, with possible further developments in radiation detection and environmental science. It serves as very useful material for those studying or practicing AI and gamma spectroscopy and want to know more about it or need guidance on what is happening so far regarding AI integrated into gamma spectroscopy analysis.

It is already known that electrostatic, magnetostatic, extremely low-frequency electric fields, and pulsed electric field could be utilized in cancer treatment. The healing effect depends on frequency and amplitude of electric field. In the present work, a simple theoretical model is developed to estimate the intensity of electrostatic field that damages a living cell during division. By this model, it is shown that magnification of electric field in the bottleneck of dividing cell is enough to break chemical bounds between molecules by an avalanche process. Our model shows that the externally applied electric field of 4 V/cm intensity is able to hurt a cancer cell at the dividing stage.