Purpose: To quantify differences that exist between dosimetry models used for Y selective internal radiation therapy (SIRT).
Methods And Materials: Retrospectively, 37 tumors were delineated on 19 post-therapy quantitative Y single photon emission computed tomography/computed tomography scans. Using matched volumes of interest (VOIs), absorbed doses were reported using 3 dosimetry models: glass microsphere package insert standard model (SM), partition model (PM), and Monte Carlo (MC). Univariate linear regressions were performed to predict mean MC from SM and PM. Analysis was performed for 2 subsets: cases with a single tumor delineated (best case for PM), and cases with multiple tumors delineated (typical clinical scenario). Variability in PM from the ad hoc placement of a single spherical VOI to estimate the entire normal liver activity concentration for tumor (T) to nontumoral liver (NL) ratios (TNR) was investigated. We interpreted the slope of the resulting regression as bias and the 95% prediction interval (95%PI) as uncertainty. MC represents MC absorbed doses to the NL for the single tumor patient subset; other combinations of calculations follow a similar naming convention.
Results: SM was unable to predict MC or MC (p>.12, 95%PI >±177 Gy). However, SM was able to predict (p<.012) MC, albeit with large uncertainties; SM and SM yielded biases of 0.62 and 0.71, and 95%PI of ±40 and ± 32 Gy, respectively. PM and PM predicted (p<2E-6) MC and MC with biases of 0.52 and 0.54, and 95%PI of ±38 and ± 111 Gy, respectively. The TNR variability in PM increased the 95%PI for predicting MC (bias = 0.46 and 95%PI = ±103 Gy). The TNR variability in PM modified the bias when predicting MC (bias = 0.32 and 95%PI = ±110 Gy).
Conclusions: The SM is unable to predict mean MC tumor absorbed dose. The PM is statistically correlated with mean MC, but the resulting uncertainties in predicted MC are large. Large differences observed between dosimetry models for Y SIRT warrant caution when interpreting published SIRT absorbed doses. To reduce uncertainty, we suggest the entire NL VOI be used for TNR estimates when using PM.
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http://dx.doi.org/10.1016/j.ijrobp.2016.07.021 | DOI Listing |
Clin Oral Investig
January 2025
Department of Restorative Dentistry - Endodontics, Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas, Piracicaba, SP, Brazil.
Objectives: To investigate volumetric changes, in vivo biocompatibility, and systemic migration from eight commercial endodontic sealer materials in paste/paste, powder/liquid, and pre-mixed forms.
Materials And Methods: The sealers AH Plus Bioceramic, AH Plus Jet, BioRoot RCS, MTApex, Bio-C Sealer, Bio-C Sealer Ion+, EndoSequence BC Sealer and NeoSEALER Flo were studied. After characterisation by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), Raman spectroscopy and X-ray diffractometry (XRD), tubes were implanted in Wistar rats' alveolar bone and subcutaneous tissues.
Phys Med Biol
January 2025
Department of Information Engineering, Electronics and Telecommunications (DIET) , University of Rome La Sapienza, Via Eudossiana 18, Rome, 00184, ITALY.
Objective: This study introduces the effective electric field (Eeff) as a novel observable for transcranial magnetic stimulation (TMS) numerical dosimetry. Eeff represents the electric field component aligned with the local orientation of cortical and white matter neuronal elements. To assess the utility of Eeff as a predictive measure for TMS outcomes, we evaluated its correlation with TMS induced muscle responses and compared it against conventional observables, including the electric (E-)field magnitude, and its components normal and tangential to the cortical surface.
View Article and Find Full Text PDFNanomaterials (Basel)
January 2025
Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1X6, Canada.
Monte Carlo (MC) simulations have become important in advancing nanoparticle (NP)-based applications for cancer imaging and therapy. This review explores the critical role of MC simulations in modeling complex biological interactions, optimizing NP designs, and enhancing the precision of therapeutic and diagnostic strategies. Key findings highlight the ability of MC simulations to predict NP bio-distribution, radiation dosimetry, and treatment efficacy, providing a robust framework for addressing the stochastic nature of biological systems.
View Article and Find Full Text PDFTomography
January 2025
Medical Physics Unit, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy.
Background: Computed tomography scans are widely used in everyday medical practice due to speed, image reliability, and detectability of a wide range of pathologies. Each scan exposes the patient to a radiation dose, and performing a fast estimation of the effective dose (E) is an important step for radiological safety. The aim of this work is to estimate E from patient and CT acquisition parameters in the absence of a dose-tracking software exploiting machine learning.
View Article and Find Full Text PDFJ Nucl Med
January 2025
United Theranostics, Bethesda, Maryland.
Computational nuclear oncology for precision radiopharmaceutical therapy (RPT) is a new frontier for theranostic treatment personalization. A key strategy relies on the possibility to incorporate clinical, biomarker, image-based, and dosimetric information in theranostic digital twins (TDTs) of patients to move beyond a one-size-fits-all approach. The TDT framework enables treatment optimization by real-time monitoring of the real-world system, simulation of different treatment scenarios, and prediction of resulting treatment outcomes, as well as facilitating collaboration and knowledge sharing among health care professionals adopting a harmonized TDT.
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