ArticleAmerican journal of translational research2022
Early assessment of irreversible electroporation ablation outcomes by analyzing MRI texture: preclinical study in an animal model of liver tumor.
Article in American journal of translational research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Combination of Irreversible Electroporation andCancers · 2025Review
- Advances in irreversible electroporation for prostate cancer.Discover oncology · 2024Review
- Dendritic cell vaccination combined with irreversible electroporation for treating pancreatic cancer-a narrative review.Annals of translational medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
objectivesAccurate differentiation of temporary vs. permanent changes occurring following irreversible electroporation (IRE) holds immense importance for the early assessment of ablative treatment outcomes. Here, we investigated the benefits of advanced statistical learning models for an immediate evaluation of therapeutic outcomes by interpreting quantitative characteristics captured with conventional MRI.
methodsThe preclinical study integrated twenty-six rabbits with anatomical and perfusion MRI data acquired with a 3T clinical MRI scanner. T1w and T2w MRI data were quantitatively analyzed, and forty-six quantitative features were computed with four feature extraction methods. The candidate key features were determined by graph clustering following the filtering-based feature selection technique, RELIEFF algorithm. Kernel-based support vector machines (SVM) and random forest (RF) classifiers interpreting quantitative features of T1w, T2w, and combination (T1w+T2w) MRI were developed for replicating the underlying characteristics of the tissues to distinguish IRE ablation regions for immediate assessment of treatment response. Accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve were used to evaluate classification performance.
resultsFollowing the analysis of quantitative variables, three features were integrated to develop a SVM classification model, while five features were utilized for generating RF classifiers. SVM classifiers demonstrated detection accuracy of 91.06%, 96.15%, and 98.04% for individual and combination MRI data, respectively. Besides, RF classifiers obtained slightly lower accuracy compared to SVM which were 95.06%, 89.40%, and 94.38% respectively.
conclusionsQuantitative models integrating structural characteristics of conventional T1w and T2w MRI data with statistical learning techniques identified IRE ablation regions allowing early assessment of treatment status.
Indexed as
Identifiers
36105031PMC9452330What OpenQuestion holds
Registered trials
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