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Record W2802286933 · doi:10.7759/cureus.2502

Imaging of Sodium MRI for Therapy Evaluation of Brain Metastase with Cyberknife at 7T: A Case Report

2018· article· en· W2802286933 on OpenAlexfundno aff
Lichao Huang, Zihao Zhang, Baolin Qu, Zhiqiang Cui, Yao Wang, Jiwei Li, Jinyuan Wang, Zhentao Zuo, Yan Zhuo, Xinguang Yu, Zhipei Lin, Longsheng Pan

Bibliographic record

VenueCureus · 2018
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaYork University
KeywordsMedicineCyberknifeRadiosurgeryMagnetic resonance imagingRadiation therapyBrain tumorRadiologyLesionImaging biomarkerNuclear medicinePathology

Abstract

fetched live from OpenAlex

Herein we describe the case of an elderly patient who presented with a recent history of impaired vision of the right eye around three months due to brain lesions. He was diagnosed with liver cancer and underwent surgery three months prior. The pathological result is hepatocellular carcinoma. Magnetic resonance imaging (MRI) revealed the diagnosis of brain to be metastatic. The patient selected CyberKnife (Accuray Incorporated, Sunnyvale, USA) radiosurgery for the brain lesion since his physical conditions are not suitable for craniotomy. We adapt the imaging of sodium MRI and proton diffusion mapping at 7T MR system to evaluate the efficacy following CyberKnife early stage treatment. To date, we find the tissue sodium concentration (TSC) changes with the time whereas the proton MRI has no significant change within one month. The time course of sodium concentration in the tumor showed a dramatic increase in the treated brain tumor compared to the pretreatment sodium concentration and 48 hours after stereotactic radiosurgery (SRS), which is correlated to the period of the radiotherapy-induced cellular necrosis. This case demonstrates the possibility of sodium MRI as a biomarker for monitoring early radiotherapy for assessing tumor cellularity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.061
GPT teacher head0.408
Teacher spread0.348 · 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 teacher head, 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".

Quick stats

Citations8
Published2018
Admission routes1
Has abstractyes

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