@article{RN86,
   author = {Jacobs, E. J. th and Aycock, K. N. and Santos, P. P. and Tuohy, J. L. and Davalos, R. V.},
   title = {Rapid estimation of electroporation-dependent tissue properties in canine lung tumors using a deep neural network},
   journal = {Biosens Bioelectron},
   volume = {244},
   pages = {115777},
   note = {1873-4235
Jacobs, Edward J 4th
Aycock, Kenneth N
Santos, Pedro P
Tuohy, Joanne L
Davalos, Rafael V
Journal Article
England
2023/11/05
Biosens Bioelectron. 2024 Jan 15;244:115777. doi: 10.1016/j.bios.2023.115777. Epub 2023 Oct 21.},
   abstract = {The efficiency of electroporation treatments depends on the application of a critical electric field over the targeted tissue volume. Both the electric field and temperature distribution strongly depend on the tissue-specific electrical properties, which both differ between patients in healthy and malignant tissues and change in an electric field-dependent manner from the electroporation process itself. Therefore, tissue property estimations are paramount for treatment planning with electroporation therapies. Ex vivo methods to find electrical tissue properties often misrepresent the targeted tissue, especially when translating results to tumors. A voltage ramp is an in situ method that applies a series of increasing electric potentials across treatment electrodes and measures the resulting current. Here, we develop a robust deep neural network, trained on finite element model simulations, to directly predict tissue properties from a measured voltage ramp. There was minimal test error (R(2)>0.94;p<0.0001) in three important electric tissue properties. Further, our model was validated to correctly predict the complete dynamic conductivity curve in a previously characterized ex vivo liver model (R(2)>0.93;p<0.0001) within 100 s from probe insertion, showing great utility for a clinical application. Lastly, we characterize the first reported electrical tissue properties of lung tumors from five canine patients (R(2)>0.99;p<0.0001). We believe this platform can be incorporated prior to treatment to quickly ascertain patient-specific tissue properties required for electroporation treatment planning models or real-time treatment prediction algorithms. Further, this method can be used over traditional ex vivo methods for in situ tissue characterization with clinically relevant geometries.},
   keywords = {Humans
Animals
Dogs
*Biosensing Techniques
Electroporation
Electroporation Therapies
Electric Conductivity
Neural Networks, Computer
*Lung Neoplasms
Artificial intelligence
Cancer
Deep neural network
Electric tissue properties
Finite element methods
Pulsed field ablation
Tissue ablation},
   ISSN = {0956-5663},
   DOI = {10.1016/j.bios.2023.115777},
   year = {2024},
   type = {Journal Article}
}

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V."],"bibdata":{"bibtype":"article","type":"Journal Article","author":[{"propositions":[],"lastnames":["Jacobs"],"firstnames":["E.","J.","th"],"suffixes":[]},{"propositions":[],"lastnames":["Aycock"],"firstnames":["K.","N."],"suffixes":[]},{"propositions":[],"lastnames":["Santos"],"firstnames":["P.","P."],"suffixes":[]},{"propositions":[],"lastnames":["Tuohy"],"firstnames":["J.","L."],"suffixes":[]},{"propositions":[],"lastnames":["Davalos"],"firstnames":["R.","V."],"suffixes":[]}],"title":"Rapid estimation of electroporation-dependent tissue properties in canine lung tumors using a deep neural network","journal":"Biosens Bioelectron","volume":"244","pages":"115777","note":"1873-4235 Jacobs, Edward J 4th Aycock, Kenneth N Santos, Pedro P Tuohy, Joanne L Davalos, Rafael V Journal Article England 2023/11/05 Biosens Bioelectron. 2024 Jan 15;244:115777. doi: 10.1016/j.bios.2023.115777. Epub 2023 Oct 21.","abstract":"The efficiency of electroporation treatments depends on the application of a critical electric field over the targeted tissue volume. Both the electric field and temperature distribution strongly depend on the tissue-specific electrical properties, which both differ between patients in healthy and malignant tissues and change in an electric field-dependent manner from the electroporation process itself. Therefore, tissue property estimations are paramount for treatment planning with electroporation therapies. Ex vivo methods to find electrical tissue properties often misrepresent the targeted tissue, especially when translating results to tumors. A voltage ramp is an in situ method that applies a series of increasing electric potentials across treatment electrodes and measures the resulting current. Here, we develop a robust deep neural network, trained on finite element model simulations, to directly predict tissue properties from a measured voltage ramp. There was minimal test error (R(2)>0.94;p<0.0001) in three important electric tissue properties. Further, our model was validated to correctly predict the complete dynamic conductivity curve in a previously characterized ex vivo liver model (R(2)>0.93;p<0.0001) within 100 s from probe insertion, showing great utility for a clinical application. Lastly, we characterize the first reported electrical tissue properties of lung tumors from five canine patients (R(2)>0.99;p<0.0001). We believe this platform can be incorporated prior to treatment to quickly ascertain patient-specific tissue properties required for electroporation treatment planning models or real-time treatment prediction algorithms. Further, this method can be used over traditional ex vivo methods for in situ tissue characterization with clinically relevant geometries.","keywords":"Humans Animals Dogs *Biosensing Techniques Electroporation Electroporation Therapies Electric Conductivity Neural Networks, Computer *Lung Neoplasms Artificial intelligence Cancer Deep neural network Electric tissue properties Finite element methods Pulsed field ablation Tissue ablation","issn":"0956-5663","doi":"10.1016/j.bios.2023.115777","year":"2024","bibtex":"@article{RN86,\n author = {Jacobs, E. J. th and Aycock, K. N. and Santos, P. P. and Tuohy, J. L. and Davalos, R. V.},\n title = {Rapid estimation of electroporation-dependent tissue properties in canine lung tumors using a deep neural network},\n journal = {Biosens Bioelectron},\n volume = {244},\n pages = {115777},\n note = {1873-4235\nJacobs, Edward J 4th\nAycock, Kenneth N\nSantos, Pedro P\nTuohy, Joanne L\nDavalos, Rafael V\nJournal Article\nEngland\n2023/11/05\nBiosens Bioelectron. 2024 Jan 15;244:115777. doi: 10.1016/j.bios.2023.115777. Epub 2023 Oct 21.},\n abstract = {The efficiency of electroporation treatments depends on the application of a critical electric field over the targeted tissue volume. Both the electric field and temperature distribution strongly depend on the tissue-specific electrical properties, which both differ between patients in healthy and malignant tissues and change in an electric field-dependent manner from the electroporation process itself. Therefore, tissue property estimations are paramount for treatment planning with electroporation therapies. Ex vivo methods to find electrical tissue properties often misrepresent the targeted tissue, especially when translating results to tumors. A voltage ramp is an in situ method that applies a series of increasing electric potentials across treatment electrodes and measures the resulting current. Here, we develop a robust deep neural network, trained on finite element model simulations, to directly predict tissue properties from a measured voltage ramp. There was minimal test error (R(2)>0.94;p<0.0001) in three important electric tissue properties. Further, our model was validated to correctly predict the complete dynamic conductivity curve in a previously characterized ex vivo liver model (R(2)>0.93;p<0.0001) within 100 s from probe insertion, showing great utility for a clinical application. Lastly, we characterize the first reported electrical tissue properties of lung tumors from five canine patients (R(2)>0.99;p<0.0001). We believe this platform can be incorporated prior to treatment to quickly ascertain patient-specific tissue properties required for electroporation treatment planning models or real-time treatment prediction algorithms. Further, this method can be used over traditional ex vivo methods for in situ tissue characterization with clinically relevant geometries.},\n keywords = {Humans\nAnimals\nDogs\n*Biosensing Techniques\nElectroporation\nElectroporation Therapies\nElectric Conductivity\nNeural Networks, Computer\n*Lung Neoplasms\nArtificial intelligence\nCancer\nDeep neural network\nElectric tissue properties\nFinite element methods\nPulsed field ablation\nTissue ablation},\n ISSN = {0956-5663},\n DOI = {10.1016/j.bios.2023.115777},\n year = {2024},\n type = {Journal Article}\n}\n\n","author_short":["Jacobs, E. J. t.","Aycock, K. N.","Santos, P. P.","Tuohy, J. L.","Davalos, R. V."],"key":"RN86","id":"RN86","bibbaseid":"jacobs-aycock-santos-tuohy-davalos-rapidestimationofelectroporationdependenttissuepropertiesincaninelungtumorsusingadeepneuralnetwork-2024","role":"author","urls":{},"keyword":["Humans Animals Dogs *Biosensing Techniques Electroporation Electroporation Therapies Electric Conductivity Neural Networks","Computer *Lung Neoplasms Artificial intelligence Cancer Deep neural network Electric tissue properties Finite element methods Pulsed field ablation Tissue ablation"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/network/files/bdNBTZRXTsoHCgpbh","dataSources":["D4zENc4BfFNBwSYYJ","ZPLjameRikygaiM9B","3XfNmZkLe6o8CvECW","fJQsxtBoqymHQG6tL","LzxgEApraxMPkLTMn","Z2THpXfLYEJf3CB8p"],"keywords":["humans animals dogs *biosensing techniques electroporation electroporation therapies electric conductivity neural networks","computer *lung neoplasms artificial intelligence cancer deep neural network electric tissue properties finite element methods pulsed field ablation tissue ablation"],"search_terms":["rapid","estimation","electroporation","dependent","tissue","properties","canine","lung","tumors","using","deep","neural","network","jacobs","aycock","santos","tuohy","davalos"],"title":"Rapid estimation of electroporation-dependent tissue properties in canine lung tumors using a deep neural network","year":2024}