ArticleJournal of neuro-oncology2019
Predictive modeling of brain tumor laser ablation dynamics.
Article in Journal of neuro-oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 15 citations in OpenAlex.
- A novel predictive model utilizing retinal microstructural features for estimating survival outcome in patients with glioblastoma.Clinical neurology and neurosurgery · 2025Article
- A Novel Predictive Model Utilizing Retinal Microstructural Features for Estimating Survival Outcome in Patients with Glioblastoma.Research square · 2024Article
- The LMIT: Light-mediated minimally-invasive theranostics in oncology.Theranostics · 2024Review
- Predicting brain temperature in humans using bioheat models: Progress and outlook.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2023Review
- From Focused Ultrasound Tumor Ablation to Brain Blood Barrier Opening for High Grade Glioma: A Systematic Review.Cancers · 2021Review
- Role of Laser Interstitial Thermal Therapy in the Management of Primary and Metastatic Brain Tumors.Current treatment options in oncology · 2021Review
- Contactless Thermometry by MRI and MRS: Advanced Methods for Thermotherapy and Biomaterials.iScience · 2020Review
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionLaser interstitial thermal therapy (LITT) is a novel MR thermometry-guided thermoablative tool revolutionizing the clinical management of brain tumors. A limitation of LITT is our inability to estimate a priori how tissues will respond to thermal energy, which hinders treatment planning and delivery. The aim of this study was to determine whether brain tumor LITT ablation dynamics may be predicted by features of the preoperative MRI and the relevance of these data, if any, to the recurrence of metastases after LITT.
methodsIntraoperative thermal damage estimate (TDE) map pixels representative of irreversible damage were retrospectively quantified relative to ablation onset for 101 LITT procedures. Raw TDE pixel counts and TDE pixel counts modelled with first order dynamics were related to eleven independent variables derived from the preoperative MRI, demographics, laser settings, and tumor pathology. Stepwise regression analysis generated predictive models of LITT dynamics, and leave-one-out cross validation evaluated the accuracy of these models at predicting TDE pixel counts solely from the independent variables. Using a deformable atlas, TDE maps were co-registered to the immediate post-ablation MRI, allowing comparison of predicted and actual ablation sizes.
resultsBrain tumor TDE pixel counts modelled with first order dynamics, but not raw pixel counts, are correlated with the independent variables. Independent variables showing strong relations to the TDE pixel measures include T1 gadolinium and T2 signal, perfusion, and laser power. Associations with tissue histopathology are minimal. Leave-one-out analysis demonstrates that predictive models using these independent variables account for 77% of the variance observed in TDE pixel counts. Analysis of metastases treated revealed a trend towards the over-estimation of LITT effects by TDE maps during rapid ablations, which was associated with tumor recurrence.
conclusionsFeatures of the preoperative MRI are predictive of LITT ablation dynamics and could eventually be used to improve the clinical efficacy with which LITT is delivered to brain tumors.
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Registered trials
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