The potential role of borophene as a radiosensitizer in PT and BNCT was investigated. Our study focused on two aspects: (1) the synthesis and characterization of borophene nanomaterials; and (2) biocompatibility and dose enhancement. To overcome the limitation of vapor-based technology, we successfully deployed the liquid-phase exfoliation (LPE) method to produce borophene targeting for biomedical applications. Bringing together spatial distribution and dose deposition, the in vitro microdosimetry study was carried out in the presence of borophene. A quantitative study of the dose enhancement ratio (DER) was performed with Monte-Carlo simulation. The synthesized borophene showed good biocompatibility with less than 10% cell death at a concentration of up to 0.2 mg ml⁻¹. The uptake of borophene within individual cells penetrated through cell membranes but outside the nucleus. For proton PT, no significant change in the DER is found. For carbon PT, the DER increases by about 5% as the concentration of ¹⁰B reaches 1 mg g⁻¹. For BNCT, a DER of more than 2 can be obtained for a concentration as low as 100 μg g⁻¹. This study lays a foundation for utilizing novel borophene-based nanomaterials as radiosensitizers as well as imaging probes in cancer treatment.

Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution and the activity distribution of positron emitters, we aim to establish their relationship using recurrent neural network models (LSTM, BiLSTM, GRU, BiGRU and Seq2seq). Simulations were carried out with a spot-scanning proton system using Geant4-10.3 toolkit and a CT-based patient phantom. The 1D distributions of positron emitters and radiation dose were obtained. Training data were modeled for different beam energy, irradiation positions and counting statistics. The prediction accuracy of range and dose were quantitatively studied. The impact of including anatomical information (HU values in CT images) on the prediction performance was investigated. The BiGRU demonstrates the most stable and accurate performance with good capability of generalization, especially with the inclusion of anatomical information. When the signal-to-noise ratio (SNR) of the 1D activity profiles is about 3, the range accuracy can be within 0.5 mm and the dose accuracy close to the peak region is <5% (relative uncertainty between prediction and raw input for all datasets). The feasibility of proton range and dose verification using the RNN-based framework was demonstrated. The RNN-based framework promises to provide a reliable and effective way for online monitoring, quality assurance and ultimately allows for adaptive proton therapy.

We developed a machine learning framework in order to establish the correlation between dose and activity distributions in proton therapy. A recurrent neural network was used to predict dose distribution in three dimensions based on the information of proton-induced positron emitters. Hounsfield Unit (HU) information from CT images and analytically derived stopping power (SP) information were incorporated as auxiliary inputs. Four different scenarios were investigated: Activity only, Activity + HU, Activity + SP and Activity + HU + SP. The performance was quantitatively studied in terms of mean absolute error (MAE) and mean relative error (MRE), under different signal-to-noise ratios (SNRs). In addition to the first dataset of mono-energetic beams, three additional datasets were validated to help evaluate the generalization capability of our proposed model: a dataset of a lower SNR, five reconstructed PET images, and a dataset of spread-out Bragg peaks. Good verification accuracy of dose verification in three dimensions is demonstrated. The inclusion of anatomical information improves both accuracy and generalization. For an activity profile with an SNR of 4 (the mono-energetic case), the framework is able to obtain an MRE of ∼ 0.99% over the whole range and a range uncertainty of ∼ 0.27 mm. The machine learning-based framework may emerge as a useful tool to allow for online dose verification and quality assurance in proton therapy.