MétaCan
Menu
Back to cohort

Using Proton Magnetic Resonance Spectroscopic Imaging to Predict in Vivo the Response of Recurrent Malignant Gliomas to Tamoxifen Chemotherapy

2000· article· en· W2015775997 on OpenAlex
Mark C. Preul, Zografos Caramanos, Jean‐Guy Villemure, George Shenouda, Richard Leblanc, Adrian Langleben, Douglas L. Arnold

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeurosurgery · 2000
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineAnaplastic astrocytomaTamoxifenMagnetic resonance imagingGliomaChemotherapyInternal medicineRadiation therapyOncologyAntiestrogenIn vivoAstrocytomaRadiologyCancerBreast cancerCancer research

Abstract

fetched live from OpenAlex

OBJECTIVE: Most patients with a malignant glioma spend considerable time on a treatment protocol before their response (or nonresponse) to the therapy can be determined. Because survival time in the absence of effective therapy is short, the ability to predict the potential chemosensitivity of individual brain tumors noninvasively would represent a significant advance in chemotherapy planning. METHODS: Using proton magnetic resonance spectroscopic imaging (1H MRSI), we studied 16 patients with a recurrent malignant glioma before and during treatment with high-dose orally administered tamoxifen. We evaluated whether 1H MRSI data could predict eventual therapeutic response to tamoxifen at the pretreatment and early treatment stages. RESULTS: Seven patients responded to tamoxifen therapy (three with glioblastomas multiforme; four with anaplastic astrocytomas), and nine did not (six with glioblastomas multiforme; three with anaplastic astrocytomas). Responders and nonresponders exhibited no differences in their age, sex, tumor type, mean tumor volume, mean Karnofsky scale score, mean number of weeks postradiotherapy, or mean amount of prior radiation exposure. Resonance profiles across the five metabolites measured on 1H MRSI spectra (choline-containing compounds, creatine and phosphocreatine, N-acetyl groups, lactate, and lipids) differed significantly between these two groups before and during treatment. Furthermore, linear discriminant analyses based on patients' in vivo biochemical information accurately predicted individual response to tamoxifen both before and at very early treatment stages (2 and 4 wk). Similar analyses based on patient sex, age, Karnofsky scale score, tumor type, and tumor volume could not reliably predict the response to tamoxifen treatment at the same time periods. CONCLUSION: It is possible to accurately predict the response of a tumor to tamoxifen on the basis of noninvasively acquired in vivo biochemical information. 1H MRSI has potential as a prognostic tool in the pharmacological treatment of recurrent malignant gliomas.

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.

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)0.000
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 score0.255
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.319
Teacher spread0.297 · 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