Semi-automated vertex placement for lattice radiotherapy and dosimetric verification using 3D polymer gel dosimetry.

Purpose: To evaluate the feasibility of an open-source, semi-automated, and reproducible vertex placement tool to improve the efficiency of lattice radiotherapy (LRT) planning. We used polymer gel dosimetry with a Cone Beam CT (CBCT) readout to commission this LRT technique.

Material And Methods: We generated a volumetric modulated arc therapy (VMAT)-based LRT plan on a 2 L NIPAM polymer gel dosimeter using our Eclipse Acuros version 15.

Clinical applicability of Linac-integrated CBCT based NIPAM 3D dosimetry: a dual-institutional investigation.

To develop and benchmark a novel 3D dose verification technique consisting of polymer gel dosimetry (PGD) with cone-beam-CT (CBCT) readout through a two-institution study. The technique has potential for wide and robust applicability through reliance on CBCT readout..

Metabolic profiling of murine radiation-induced lung injury with Raman spectroscopy and comparative machine learning.

Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thoracic radiotherapy. As such, it is important to characterize metabolic associations with the early and late stages of RILI, namely pneumonitis and pulmonary fibrosis. Recently, Raman spectroscopy has shown utility for the differentiation of pneumonitic and fibrotic tissue states in a mouse model; however, the specific metabolite-disease associations remain relatively unexplored from a Raman perspective.

Iterative image reconstruction algorithm analysis for optical CT radiochromic gel dosimetry.

Modern radiation therapy technologies aim to enhance radiation dose precision to the tumor and utilize hypofractionated treatment regimens. Verifying the dose distributions associated with these advanced radiation therapy treatments remains an active research area due to the complexity of delivery systems and the lack of suitable three-dimensional dosimetry tools. Gel dosimeters are a potential tool for measuring these complex dose distributions.

Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network.

Reprogramming of cellular metabolism is a driving factor of tumour progression and radiation therapy resistance. Identifying biochemical signatures associated with tumour radioresistance may assist with the development of targeted treatment strategies to improve clinical outcomes. Raman spectroscopy (RS) can monitor post-irradiation biomolecular changes and signatures of radiation response in tumour cells in a label-free manner.