Fat Quantification Imaging and Biophysical Modeling for Patient-Specific Forecasting of Microwave Ablation Therapy
Computational tools are beginning to enable patient-specific surgical planning to localize and prescribe thermal dosing for liver cancer ablation therapy. Tissue-specific factors (e.g., tissue perfusion, material properties, disease state, etc.) have been found to affect ablative therapies, but curr...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-02-01
|
| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2021.820251/full |
| _version_ | 1798022550989570048 |
|---|---|
| author | Frankangel Servin Frankangel Servin Jarrod A. Collins Jon S. Heiselman Jon S. Heiselman Katherine C. Frederick-Dyer Virginia B. Planz Sunil K. Geevarghese Daniel B. Brown Michael I. Miga Michael I. Miga Michael I. Miga Michael I. Miga Michael I. Miga |
| author_facet | Frankangel Servin Frankangel Servin Jarrod A. Collins Jon S. Heiselman Jon S. Heiselman Katherine C. Frederick-Dyer Virginia B. Planz Sunil K. Geevarghese Daniel B. Brown Michael I. Miga Michael I. Miga Michael I. Miga Michael I. Miga Michael I. Miga |
| author_sort | Frankangel Servin |
| collection | DOAJ |
| description | Computational tools are beginning to enable patient-specific surgical planning to localize and prescribe thermal dosing for liver cancer ablation therapy. Tissue-specific factors (e.g., tissue perfusion, material properties, disease state, etc.) have been found to affect ablative therapies, but current thermal dosing guidance practices do not account for these differences. Computational modeling of ablation procedures can integrate these sources of patient specificity to guide therapy planning and delivery. This paper establishes an imaging-data-driven framework for patient-specific biophysical modeling to predict ablation extents in livers with varying fat content in the context of microwave ablation (MWA) therapy. Patient anatomic scans were segmented to develop customized three-dimensional computational biophysical models and mDIXON fat-quantification images were acquired and analyzed to establish fat content and determine biophysical properties. Simulated patient-specific microwave ablations of tumor and healthy tissue were performed at four levels of fatty liver disease. Ablation models with greater fat content demonstrated significantly larger treatment volumes compared to livers with less severe disease states. More specifically, the results indicated an eightfold larger difference in necrotic volumes with fatty livers vs. the effects from the presence of more conductive tumor tissue. Additionally, the evolution of necrotic volume formation as a function of the thermal dose was influenced by the presence of a tumor. Fat quantification imaging showed multi-valued spatially heterogeneous distributions of fat deposition, even within their respective disease classifications (e.g., low, mild, moderate, high-fat). Altogether, the results suggest that clinical fatty liver disease levels can affect MWA, and that fat-quantitative imaging data may improve patient specificity for this treatment modality. |
| first_indexed | 2024-04-11T17:31:52Z |
| format | Article |
| id | doaj.art-060201e529ce438082dcf3eb43cd00a3 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-11T17:31:52Z |
| publishDate | 2022-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-060201e529ce438082dcf3eb43cd00a32022-12-22T04:11:59ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2022-02-011210.3389/fphys.2021.820251820251Fat Quantification Imaging and Biophysical Modeling for Patient-Specific Forecasting of Microwave Ablation TherapyFrankangel Servin0Frankangel Servin1Jarrod A. Collins2Jon S. Heiselman3Jon S. Heiselman4Katherine C. Frederick-Dyer5Virginia B. Planz6Sunil K. Geevarghese7Daniel B. Brown8Michael I. Miga9Michael I. Miga10Michael I. Miga11Michael I. Miga12Michael I. Miga13Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United StatesVanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, TN, United StatesDepartment of Biomedical Engineering, Vanderbilt University, Nashville, TN, United StatesDepartment of Biomedical Engineering, Vanderbilt University, Nashville, TN, United StatesVanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, TN, United StatesDepartment of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United StatesDepartment of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United StatesDepartment of Surgery, Vanderbilt University Medical Center, Nashville, TN, United StatesDepartment of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United StatesDepartment of Biomedical Engineering, Vanderbilt University, Nashville, TN, United StatesVanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, TN, United StatesDepartment of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United StatesDepartment of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, United StatesDepartment of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, United StatesComputational tools are beginning to enable patient-specific surgical planning to localize and prescribe thermal dosing for liver cancer ablation therapy. Tissue-specific factors (e.g., tissue perfusion, material properties, disease state, etc.) have been found to affect ablative therapies, but current thermal dosing guidance practices do not account for these differences. Computational modeling of ablation procedures can integrate these sources of patient specificity to guide therapy planning and delivery. This paper establishes an imaging-data-driven framework for patient-specific biophysical modeling to predict ablation extents in livers with varying fat content in the context of microwave ablation (MWA) therapy. Patient anatomic scans were segmented to develop customized three-dimensional computational biophysical models and mDIXON fat-quantification images were acquired and analyzed to establish fat content and determine biophysical properties. Simulated patient-specific microwave ablations of tumor and healthy tissue were performed at four levels of fatty liver disease. Ablation models with greater fat content demonstrated significantly larger treatment volumes compared to livers with less severe disease states. More specifically, the results indicated an eightfold larger difference in necrotic volumes with fatty livers vs. the effects from the presence of more conductive tumor tissue. Additionally, the evolution of necrotic volume formation as a function of the thermal dose was influenced by the presence of a tumor. Fat quantification imaging showed multi-valued spatially heterogeneous distributions of fat deposition, even within their respective disease classifications (e.g., low, mild, moderate, high-fat). Altogether, the results suggest that clinical fatty liver disease levels can affect MWA, and that fat-quantitative imaging data may improve patient specificity for this treatment modality.https://www.frontiersin.org/articles/10.3389/fphys.2021.820251/fullliverhepatocellular carcinomafatty liver diseasemicrowave ablationfinite elementcomputational model |
| spellingShingle | Frankangel Servin Frankangel Servin Jarrod A. Collins Jon S. Heiselman Jon S. Heiselman Katherine C. Frederick-Dyer Virginia B. Planz Sunil K. Geevarghese Daniel B. Brown Michael I. Miga Michael I. Miga Michael I. Miga Michael I. Miga Michael I. Miga Fat Quantification Imaging and Biophysical Modeling for Patient-Specific Forecasting of Microwave Ablation Therapy Frontiers in Physiology liver hepatocellular carcinoma fatty liver disease microwave ablation finite element computational model |
| title | Fat Quantification Imaging and Biophysical Modeling for Patient-Specific Forecasting of Microwave Ablation Therapy |
| title_full | Fat Quantification Imaging and Biophysical Modeling for Patient-Specific Forecasting of Microwave Ablation Therapy |
| title_fullStr | Fat Quantification Imaging and Biophysical Modeling for Patient-Specific Forecasting of Microwave Ablation Therapy |
| title_full_unstemmed | Fat Quantification Imaging and Biophysical Modeling for Patient-Specific Forecasting of Microwave Ablation Therapy |
| title_short | Fat Quantification Imaging and Biophysical Modeling for Patient-Specific Forecasting of Microwave Ablation Therapy |
| title_sort | fat quantification imaging and biophysical modeling for patient specific forecasting of microwave ablation therapy |
| topic | liver hepatocellular carcinoma fatty liver disease microwave ablation finite element computational model |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2021.820251/full |
| work_keys_str_mv | AT frankangelservin fatquantificationimagingandbiophysicalmodelingforpatientspecificforecastingofmicrowaveablationtherapy AT frankangelservin fatquantificationimagingandbiophysicalmodelingforpatientspecificforecastingofmicrowaveablationtherapy AT jarrodacollins fatquantificationimagingandbiophysicalmodelingforpatientspecificforecastingofmicrowaveablationtherapy AT jonsheiselman fatquantificationimagingandbiophysicalmodelingforpatientspecificforecastingofmicrowaveablationtherapy AT jonsheiselman fatquantificationimagingandbiophysicalmodelingforpatientspecificforecastingofmicrowaveablationtherapy AT katherinecfrederickdyer fatquantificationimagingandbiophysicalmodelingforpatientspecificforecastingofmicrowaveablationtherapy AT virginiabplanz fatquantificationimagingandbiophysicalmodelingforpatientspecificforecastingofmicrowaveablationtherapy AT sunilkgeevarghese fatquantificationimagingandbiophysicalmodelingforpatientspecificforecastingofmicrowaveablationtherapy AT danielbbrown fatquantificationimagingandbiophysicalmodelingforpatientspecificforecastingofmicrowaveablationtherapy AT michaelimiga fatquantificationimagingandbiophysicalmodelingforpatientspecificforecastingofmicrowaveablationtherapy AT michaelimiga fatquantificationimagingandbiophysicalmodelingforpatientspecificforecastingofmicrowaveablationtherapy AT michaelimiga fatquantificationimagingandbiophysicalmodelingforpatientspecificforecastingofmicrowaveablationtherapy AT michaelimiga fatquantificationimagingandbiophysicalmodelingforpatientspecificforecastingofmicrowaveablationtherapy AT michaelimiga fatquantificationimagingandbiophysicalmodelingforpatientspecificforecastingofmicrowaveablationtherapy |