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Record W2955288059 · doi:10.1158/1538-7445.am2019-4450

Abstract 4450: Novel transducer array layouts to optimize the treatment of multiple brain metastases with tumor treating fields

2019· article· en· W2955288059 on OpenAlexaboutno aff
Ofir Yesharim, Ariel Naveh, Zéev Bomzon

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCancerGlioblastomaBreast cancerMelanomaHead and neckNuclear medicineInternal medicineSurgeryCancer research

Abstract

fetched live from OpenAlex

Abstract Introduction: Tumor Treating Fields (TTFields) is an antimitotic cancer treatment approved for the treatment of Glioblastoma Multiforme (GBM). TTFields are delivered using 2 pairs of transducer arrays positioned on the shaved scalp of the GBM patient to optimize the TTFields dose delivered to the tumor. This results in an increased dose of TTFields to the affected tumor region, whilst reducing the intensity of the fields to other regions. Brain metastases, or secondary brain tumors, occur in 10 to 30 percent of adults with cancer. Cancer types most likely to cause brain metastases are lung, breast, colon, kidney and melanoma. When using TTFields to treat brain metastases, it may be desirable to deliver TTFields at therapeutic intensities to the entire brain, in order to treat multiple rather than a single lesion. We investigated novel transducer array layouts designed to deliver a uniform distribution of TTFields to the entire brain. Methods Computer simulations were used to calculate the field distributions generated by different array layouts. The simulations utilized a realistic computerized head model of a 40+ years old human male prepared in-house from a T1 MRI series. Using Sim4Life v3.0 (ZMT Zürich), we simulated the delivery of TTFields using pairs of array layouts placed at different locations on the head and neck. To analyze the field distributions, the brain was divided into five regions: (1) the cerebellum, brain stem and other infra-tentorial anatomical regions; and (2-5) the four quadrants of the cerebrum. The mean and median field intensities in the five regions generated by each layout were calculated and compared. Results Median intensities between 1.5 V/cm to 1.7 V/cm within all regions were achieved using a layout in which one pair of arrays was placed on the right temple and left scapula, while the second pair was placed on the left temple and right scapula. This layout yielded a uniform intensity distribution within the brain. Conclusion We have identified a novel TTFields array layout that could potentially be used to treat the entire brain with sufficiently high intensity to treat for brain metastases. The ongoing Phase 3 METIS study [NCT02831959 is investigating radiosurgery with TTFields for 1-10 brain metastases from non-small cell lung cancer. Citation Format: Ofir Yesharim, Ariel Naveh, Ze'ev Bomzon. Novel transducer array layouts to optimize the treatment of multiple brain metastases with tumor treating fields [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4450.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.089
GPT teacher head0.395
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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