Mapping Drug Dose Distribution on CT Images Following Transarterial Chemoembolization with Radiopaque Drug-Eluting Beads in a Rabbit Tumor Model.
Andrew S MikhailWilliam F PritchardAyele H NegussieVenkatesh P KrishnasamyDaniel B AmchinJohn G ThompsonPaul G WakimDavid WoodsIvane BakhutashviliJuan A Esparza-TrujilloJohn W KaranianSean L WillisAndrew L LewisElliot B LevyBradford J WoodPublished in: Radiology (2018)
Purpose To correlate bead location and attenuation on CT images with the quantity and distribution of drug delivered to the liver following transarterial chemoembolization (TACE) with radiopaque drug-eluting beads (DEB) in a rabbit tumor model. Materials and Methods All procedures were performed with a protocol approved by the Institutional Animal Care and Use Committee. TACE was performed in rabbits (n = 4) bearing VX2 liver tumors by using radiopaque DEB (70-150 µm) loaded with doxorubicin (DOX). Livers were resected 1 hour after embolization, immediately frozen, and cut by using liver-specific three-dimensional-printed molds for colocalization of liver specimens and CT imaging. DOX penetration into tissue surrounding beads was evaluated with fluorescence microscopy. DOX levels in liver specimens were predicted by using statistical models correlating DOX content measured in tissue with bead volume and attenuation measured on CT images. Model predictions were then compared with actual measured DOX concentrations to assess the models' predictive power. Results Eluted DOX remained in close proximity (<600 µm) to beads in the liver 1 hour after TACE. Bead volume and attenuation measured on CT images demonstrated positive linear correlations (0.950 and 0.965, respectively) with DOX content in liver specimens. DOX content model predictions based on CT images were accurate compared with actual liver DOX levels at 1 hour. Conclusion CT may be used to estimate drug dose delivery and distribution in the liver following transarterial chemoembolization (TACE) with doxorubicin-loaded radiopaque drug-eluting beads (DEB). Although speculative, this informational map might be helpful in planning and understanding the spatial effects of TACE with DEB. © RSNA, 2018.
Keyphrases
- computed tomography
- image quality
- dual energy
- contrast enhanced
- deep learning
- drug delivery
- positron emission tomography
- high resolution
- optical coherence tomography
- healthcare
- palliative care
- lymph node
- emergency department
- cancer therapy
- magnetic resonance
- mass spectrometry
- machine learning
- adverse drug
- quality improvement
- chronic pain
- photodynamic therapy
- high speed
- fluorescence imaging
- wound healing