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01-08-2019 | Brain Tumor | Clinical Study

Predictive modeling of brain tumor laser ablation dynamics

Authors: Walter J. Jermakowicz, Anil K. Mahavadi, Iahn Cajigas, Lia Dan, Santiago Guerra, Ghulam Farooq, Ashish H. Shah, Pierre F. D’Haese, Michael E. Ivan, Jonathan R. Jagid, Ricardo J. Komotar

Published in: Journal of Neuro-Oncology | Issue 1/2019

Abstract

Introduction

Laser 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.

Methods

Intraoperative 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.

Results

Brain 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.

Conclusions

Features 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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Title: Predictive modeling of brain tumor laser ablation dynamics
Authors: Walter J. Jermakowicz
Anil K. Mahavadi
Iahn Cajigas
Lia Dan
Santiago Guerra
Ghulam Farooq
Ashish H. Shah
Pierre F. D’Haese
Michael E. Ivan
Jonathan R. Jagid
Ricardo J. Komotar
Publication date: 01-08-2019
Publisher: Springer US
Keywords: Brain Tumor
Laser
Published in: Journal of Neuro-Oncology / Issue 1/2019
Print ISSN: 0167-594X
Electronic ISSN: 1573-7373
DOI: https://doi.org/10.1007/s11060-019-03220-0

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Predictive modeling of brain tumor laser ablation dynamics

Abstract

Introduction

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Introduction

Methods

Results

Conclusions

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