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We demonstrate a virtual pretreatment patient‐specific QA (PSQA) procedure that is capable of quantifying dosimetric effect on patient anatomy for both intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). A machine learning prediction model was developed to use linear accelerator parameters derived from the DICOM‐RT plan to predict delivery discrepancies at treatment delivery (defined as the difference between trajectory log file and DICOM‐RT) and was coupled with an independent Monte Carlo dose calculation algorithm for dosimetric analysis. Machine learning models for IMRT and VMAT were trained and validated using 120 IMRT and 206 VMAT fields of prior patients, with 80% assigned for iterative training and testing, and 20% for post‐training validation. Various prediction models were trained and validated, with the final models selected for clinical implementation being a boosted tree and bagged tree for IMRT and VMAT, respectively. After validation, these models were then applied clinically to predict the machine parameters at treatment delivery for 7 IMRT plans from various sites (61 fields) and 10 VMAT multi‐target intracranial radiosurgery plans (35 arcs) and compared to the dosimetric effect calculated directly from trajectory log files. Dose indices tracked for targets and organs at risk included dose received by 99%, 95%, and 1% of the volume, mean dose, percent of volume receiving 25%–100% of the prescription dose. The average coefficient of determination (r2) when comparing intra‐field predicted and actual delivery error was 0.987 ± 0.012 for IMRT and 0.895 ± 0.095 for VMAT, whereas r2 when comparing inter‐field predicted versus actual delivery error was 0.982 for IMRT and 0.989 for VMAT. Regarding dosimetric analysis, r2 when comparing predicted versus actual dosimetric changes for all dose indices was 0.966 for IMRT and 0.907 for VMAT. Prediction models can be used to anticipate the dosimetric effect calculated from trajectory files and have potential as a “delivery‐free” pretreatment analysis to enhance PSQA.

A new and effective method has been developed where self-assembled gold nanoparticles (Au-NPs) of ∼10 nm diameter are successfully attached onto the surface of sidewalls and ends of thiol-terminated multi-walled carbon nanotubes (MWNTs) functionalized with orthomercaptoaniline, acting as a bridging agent. It can bridge the carbon nanotubes (CNTs) and Au-NPs via the bi-functional moiety with benzene unit at one end and thiol group at the other end by self-assembly. The ortho-mercaptoaniline was first grafted onto the surface of the CNTs via π-π interaction between the benzene ring of the mercaptoaniline and π-conjugated body of MWNTs surface to produce thiol-terminated CNTs. The bare surface of Au-NPs facilitates to attach on the thiol group of the thiol-terminated CNTs. Attenuated total reflectance FTIR, UV-visible, Raman spectroscopy and X-ray powder diffraction studies were used to verify whether the mercapto-benzene moieties have been attached to the π-conjugated body of functionalized MWNTs. The direct evidence is obtained from transmission electron microscope (TEM) images where selfassembled Au-NPs are attached onto the functionalized MWNTs.