Evidence map›Paper›PMID 41332567›Full record

ArticlebioRxiv : the preprint server for biology2025

A New Method for Optimal Placement of Tumor Treating Fields Electrodes.

Konstantin Weise, Nikola Mikic, Fang Cao, Eric T Wong, Thomas R Knösche, Axel Thielscher, Anders Rosendal Korshøj

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Konstantin WeiseDepartment of Clinical Medicine, Aarhus University, DK-8200, Aarhus N, Denmark.
Nikola MikicDepartment of Clinical Medicine, Aarhus University, DK-8200, Aarhus N, Denmark.
Fang CaoDanish Research Centre for Magnetic Resonance, Department of Radiology and Nuclear Medicine, Copenhagen University Hospital Amager and Hvidovre, Kettegaard Alle 36, 2650 Hvidovre, Denmark.
Eric T WongHealth Cancer Institute, Brown University, Rhode Island, 110 Lockwood St., Providence, RI 02903, USA.
Thomas R KnöscheMethods and Development Group "Brain Networks", Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1a, 04103 Leipzig, Germany.
Axel ThielscherDanish Research Centre for Magnetic Resonance, Department of Radiology and Nuclear Medicine, Copenhagen University Hospital Amager and Hvidovre, Kettegaard Alle 36, 2650 Hvidovre, Denmark.
Anders Rosendal KorshøjDepartment of Clinical Medicine, Aarhus University, DK-8200, Aarhus N, Denmark.

Funding

NeuroSimNIBS: Integrated electric field and neuronal response modeling for transcranial electric and magnetic stimulationR01MH128422 · NIMH · DUKE UNIVERSITY · PI Warren M. Grill, Angel V Peterchev · 2022 to 2026
$2.7M
NIMH NIH HHS R01 MH128422
6 · The paper itself

Abstract

Overview: Tumor Treating Fields (TTFields) provide a non-invasive treatment option for newly diagnosed glioblastoma. While optimization of electrode placement is important to increase treatment efficacy, clinical therapy planning is done using an undisclosed and proprietary software (NovoTAL Methods: We developed a new computational pipeline integrating patient-specific anatomical data to optimize electrode configurations in five representative glioblastoma cases with diverse tumor locations and sizes. Two optimization strategies were employed: one maximizing electric field intensity at the tumor, and another enhancing coverage of the adjacent brain while maintaining sufficient tumor intensity. Results were compared to electrode placements generated by NovoTAL Results: Optimized electrode placements improved electric field intensity in tumors by 18%-34% compared to the clinical standard. Coverage-weighted optimizations provided broader field coverage without significantly compromising tumor intensity. Smaller or surface-adjacent tumors benefited most from optimization, achieving precise targeting and enhanced coverage. Extensive randomized placement analyses highlighted the superior performance of the optimized configurations. Analysis of artificial models showed consistent improvements across varying tumor locations and sizes. Conclusion: Personalized optimization of TTFields electrode placement significantly improves electric field targeting of tumors and adjacent brain regions. This approach outperforms standardized planning software and clinical practices and supports future development of adaptive, automated strategies for individualized TTFields therapy in glioblastoma.

Indexed as

Electrode OptimizationGlioblastomaNovoTALSimNIBSTumor Treating Fields

Identifiers

PMID41332567
PMCPMC12667808

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.