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Detection of electroporation-induced membrane permeabilization states in the brain using diffusion-weighted MRI.

ABSTRACT: Tissue permeabilization by electroporation (EP) is a promising technique to treat certain cancers. Non-invasive methods for verification of induced permeabilization are important, especially in deep-seated cancers. In this study we evaluated diffusion-weighted magnetic resonance imaging (DW-MRI) as a quantitative method for detecting EP-induced membrane permeabilization of brain tissue using a rat brain model.Fifty-four anesthetized Sprague-Dawley male rats were electroporated in the right hemisphere, using different voltage levels to induce no permeabilization (NP), transient membrane permeabilization (TMP), and permanent membrane permeabilization (PMP), respectively. DW-MRI was acquired 5 minutes, 2 hours, 24 hours and 48 hours after EP. Histology was performed for validation of the permeabilization states. Tissue content of water, Na+, K+, Ca2+, and extracellular volume were determined. The Kruskal-Wallis test was used to compare the DW-MRI parameters, apparent diffusion coefficient (ADC) and kurtosis, at different voltage levels. The two-sample Mann- Whitney test with Holm's Bonferroni correction was used to identify pairs of significantly different groups. The study was approved by the Danish Animal Experiments Inspectorate.Results showed significant difference in the ADC between TMP and PMP at 2 hours (p<0.001) and 24 hours (p<0.05) after EP. Kurtosis was significantly increased both at TMP (p<0.05) and PMP (p<0.001) 5 minutes after EP, compared to NP. Kurtosis was also significantly higher at 24 hours (p<0.05) and 48 hours (p<0.05) at PMP compared to NP. Physiological parameters indicated correlation with the permeabilization states, supporting the DW-MRI findings. We conclude that DW-MRI is capable of detecting EP-induced permeabilization of brain tissue and to some extent of differentiating NP, TMP and PMP using appropriate scan timing.

SUBMITTER: Mahmood F

PROVIDER: S-EPMC4438348 | biostudies-other | 2015 Mar

REPOSITORIES: biostudies-other

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Detection of electroporation-induced membrane permeabilization states in the brain using diffusion-weighted MRI.

Mahmood Faisal F Hansen Rasmus H RH Agerholm-Larsen Birgit B Gissel Hanne H Ibsen Per P Gehl Julie J

Acta oncologica (Stockholm, Sweden) 20150116 3

<h4>Background</h4>Tissue permeabilization by electroporation (EP) is a promising technique to treat certain cancers. Non-invasive methods for verification of induced permeabilization are important, especially in deep-seated cancers. In this study we evaluated diffusion-weighted magnetic resonance imaging (DW-MRI) as a quantitative method for detecting EP-induced membrane permeabilization of brain tissue using a rat brain model.<h4>Material and methods</h4>Fifty-four anesthetized Sprague-Dawley ...[more]

PMID: 25591820

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Project description:BackgroundMetastatic complications are a major cause of cancer-related morbidity, with up to 40% of cancer patients experiencing at least one brain metastasis. Earlier detection may significantly improve patient outcomes and overall survival. We investigated machine learning (ML) models for early detection of brain metastases based on diffusion weighted imaging (DWI) radiomics.MethodsLongitudinal diffusion imaging from 116 patients previously treated with stereotactic radiosurgery (SRS) for brain metastases were retrospectively analyzed. Clinical contours from 600 metastases were extracted from radiosurgery planning computed tomography, and rigidly registered to corresponding contrast enhanced-T1 and apparent diffusion coefficient (ADC) maps. Contralateral contours located in healthy brain tissue were used as control. The dataset consisted of (I) radiomic features using ADC maps, (II) radiomic feature change calculated using timepoints before the metastasis manifested on contrast enhanced-T1, (III) primary cancer, and (IV) anatomical location. The dataset was divided into training and internal validation sets using an 80/20 split with stratification. Four classification algorithms [Linear Support Vector Machine (SVM), Random Forest (RF), AdaBoost, and XGBoost] underwent supervised classification training, with contours labeled either 'control' or 'metastasis'. Hyperparameters were optimized towards balanced accuracy. Various model metrics (receiver operating characteristic curve area scores, accuracy, recall, and precision) were calculated to gauge performance.ResultsThe radiomic and clinical data set, feature engineering, and ML models developed were able to identify metastases with an accuracy of up to 87.7% on the training set, and 85.8% on an unseen test set. XGBoost and RF showed superior accuracy (XGBoost: 0.877±0.021 and 0.833±0.47, RF: 0.823±0.024 and 0.858±0.045) for training and validation sets, respectively. XGBoost and RF also showed strong area under the receiver operating characteristic curve (AUC) performance on the validation set (0.910±0.037 and 0.922±0.034, respectively). AdaBoost performed slightly lower in all metrics. SVM model generalized poorly with the internal validation set. Important features involved changes in radiomics months before manifesting on contrast enhanced-T1.ConclusionsThe proposed models using diffusion-based radiomics showed encouraging results in differentiating healthy brain tissue from metastases using clinical imaging data. These findings suggest that longitudinal diffusion imaging and ML may help improve patient care through earlier diagnosis and increased patient monitoring/follow-up. Future work aims to improve model classification metrics, robustness, user-interface, and clinical applicability.

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Detection of electroporation-induced membrane permeabilization states in the brain using diffusion-weighted MRI.

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Detection of electroporation-induced membrane permeabilization states in the brain using diffusion-weighted MRI.

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