MétaCan
Menu
Back to cohort
Record W2050509636 · doi:10.1002/jmri.23858

Blood oxygen level‐dependent liver MRI: Can It predict microvascular invasion in HCC?

2012· article· en· W2050509636 on OpenAlexaff
Kartik Jhaveri, Sean P. Cleary, Sandra E. Fischer, Masoom A. Haider, Vivek Pargoankar, Karim Khalidi, Hadas Moshonov, Steven Gallinger

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2012
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsUniversity of TorontoUniversity Health NetworkMount Sinai Hospital
Fundersnot available
KeywordsMedicineMagnetic resonance imagingHepatocellular carcinomaLogistic regressionHistopathologyBlood-oxygen-level dependentRadiologyNuclear medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

PURPOSE: To assess Blood Oxygen Level-Dependent (BOLD) Magnetic Resonance Imaging (MRI) for noninvasive preoperative prediction of Microvascular Invasion (MVI) in Hepatocellular Carcinoma (HCC). MATERIALS AND METHODS: In this prospective, institutional review board approved study, 26 patients (21 men and 5 women age range, 34-77 years with mean age of 61 years) with HCC were evaluated preoperatively with liver MRI including baseline and post oxygen (O2) breathing BOLD MRI. Post processing of MRI data was performed to obtain R2* values (1/s) and correlated with histopathological assessment of MVI. Statistical analysis was performed to assess correlation of baseline R2*, post O2 R2* and R2* ratios to presence of MVI in HCC by binary logistic regression analysis. RESULTS: MVI was present in 15/26 (58%) of HCC on histopathology. The mean R2* values ± SD at baseline and post O2 with and without MVI were 35 ± 12, 36 ± 12, 38 ± 10, 42 ± 17. The R2* values between the groups with and without MVI were not significantly different statistically. CONCLUSION: BOLD MRI is unable to accurately predict MVI in HCC. The noninvasive preoperative MRI detection of MVI in HCC remains elusive.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.732

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 head0.045
GPT teacher head0.239
Teacher spread0.194 · 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 designObservational
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

Citations22
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Magnetic Resonance ImagingSame topicHepatocellular Carcinoma Treatment and PrognosisFrench-language works237,207