Single-cell DNA sequencing-a potential dosimetric tool.

We hypothesised that single-cell whole-genome sequencing has the potential to detect mutational differences in the genomes of the cells that are irradiated with different doses of radiation and we set out to test our hypothesis using in silico and in vitro experiments. In this manuscript, we present our findings from a Monte Carlo single-cell irradiation simulation performed in TOPAS-nBio using a custom-built geometric nuclear deoxyribonucleic acid (DNA) model, which predicts a significant dose dependence of the number of cluster damages per cell as a function of radiation dose. We also present preliminary experimental results, obtained from single-cell whole-genome DNA sequencing analysis performed on cells irradiated with different doses of radiation, showing promising agreement with the simulation results.

Natural language processing and machine learning to assist radiation oncology incident learning.

PURPOSE: To develop a Natural Language Processing (NLP) and Machine Learning (ML) pipeline that can be integrated into an Incident Learning System (ILS) to assist radiation oncology incident learning by semi-automating incident classification. Our goal was to develop ML models that can generate label recommendations, arranged according to their likelihoods, for three data elements in Canadian NSIR-RT taxonomy. METHODS: Over 6000 incident reports were gathered from the Canadian national ILS as well as our local ILS database. Incident descriptions from these reports were processed using various NLP techniques. The processed data with the expert-generated labels were used to train and evaluate over 500 multi-output ML algorithms. The top three models were identified and tuned for each of three different taxonomy data elements, namely: (1) process step where the incident occurred, (2) problem type of the incident and (3) the contributing factors of the incident. The best-performing model after tuning was identified for each data element and tested on unseen data. RESULTS: The MultiOutputRegressor extended Linear SVR models performed best on the three data elements. On testing, our models ranked the most appropriate label 1.48 ± 0.03, 1.73 ± 0.05 and 2.66 ± 0.08 for process-step, problem-type and contributing factors respectively. CONCLUSIONS: We developed NLP-ML models that can perform incident classification. These models will be integrated into our ILS to generate a drop-down menu. This semi-automated feature has the potential to improve the usability, accuracy and efficiency of our radiation oncology ILS.

The impact of treatment parameter variation on secondary neutron spectra in high-energy electron beam radiotherapy.

High-energy electron treatment procedures in radiotherapy pose a potential iatrogenic cancer risk as well as a critical health risk to patients with cardiac implantable electronic devices due to the generation of secondary neutrons in the linac head, the treatment room, and the patient. It may be argued that the neutron production from photons is well characterized, but the same is not true for electrons. Therefore, to assess the risk involved in an electron treatment, one must determine the neutron flux spectrum generated by the treatment procedure. The neutron spectrum depends on the treatment parameters used and therefore it is crucial to study its dependence on these parameters. In this work, eight experiments were devised to analyze how eight electron treatment parameters impacted the neutron spectrum. The parameters we considered were the electron beam energy, location of measurement, cutout size, collimator size, applicator size, collimator angle, choice of treatment room, and the presence or absence of a solid water phantom. For each experiment, we used a Nested Neutron Spectrometer™ (NNS) to measure the neutron flux spectra for multiple settings of the treatment parameter of interest. The resulting spectra were plotted and compared. We found that the electron beam energy and the location of measurement had the most impact on the neutron flux spectra, while the other parameters had a smaller or insignificant impact. This report may serve as a reference tool for medical physicists to help estimate the risk associated with a particular high-energy electron treatment procedure.