MétaCan
Menu
Back to cohort
Record W2524520056 · doi:10.3413/nukmed-0263

18F-FDG-PET and MRI in patients with malignancies of the liver and pancreas

2010· article· en· W2524520056 on OpenAlexaff
C. S. Reiner, Thomas F. Hany, J. Fornaro, Gustav K. von Schulthess, Borut Marinček, D. Weishaupt, O. F. Donati

Bibliographic record

VenueNuklearmedizin - NuclearMedicine · 2010
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsNuclear medicineImage registrationMagnetic resonance imagingMedicineImage fusionPositron emission tomographyRadiologyArtificial intelligenceComputer scienceImage (mathematics)

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate the accuracy of retrospective rigid image registration and fusion between F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) and magnetic resonance imaging (MRI) of the upper abdomen. PATIENTS, MATERIAL, METHODS: Image fusion of PET and MRI was performed in 30 patients with suspected malignancy of the liver or pancreas. Using a commercially available image fusion tool capable of rigid manual point-based registration, PET-Images were retrospectively registered and fused by matching eight homologous points in the 3D spoiled gradient echo (GRE) MRI sequences acquired in portal venous phase and in the CT-component of PET/CT. Two separate observers (R1, R2) assessed accuracy of image registration by determining the distances in the x-, y- and z-axis as well as the absolute distance between anatomical landmarks which differed from the landmarks chosen for registration. Quality of fusion was graded using a three point grading scale (1 poorly fused; 2 satisfactory fused; 3 correctly fused) and compared to hybrid PET/CT fusion. RESULTS: Mean time of registration per patient was less than 2 minutes. Objective registration assessment showed errors between 2.4-6.3 mm in x-axis: mean 3.6 mm (R1); 4.6 mm (R2), 2.3-9.3 mm in y-axis (mean 5.1 mm; 5.5 mm) and 3.3-12.0 mm in z-axis (mean 5.9 mm; 5.9 mm.) The mean error in absolute distance between points was 6.0-16.8 mm (mean 9.9 mm; 10.6 mm). In visual assessment, most fusions were graded to be satisfactory or correctly fused: R1, R2: grade 3, 11/30 (36.7%), 22/30 (73.3%); grade 2, 13/30 (43.3%), 8/30 (26.7%); grade 1, 6/30 (20%), 0/30 (0%). Fusions were mostly comparable to hybrid PET/CT fusions. All of the fusions were defined as diagnostically relevant by both observers. CONCLUSION: Retrospective rigid image fusion of FDG-PET and MRI of the upper abdomen using the CT-component of PET/CT for registration is feasible without adaptation in image acquisition protocols and shows sub-centimeter registration errors in most cases.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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 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

Citations15
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueNuklearmedizin - NuclearMedicineSame topicMedical Imaging Techniques and ApplicationsFrench-language works237,207