<scp>MR</scp> Elastography for Classification of Focal Liver Lesions Using Viscoelastic Parameters: A Pilot Study Based on Intrinsic and Extrinsic Activations
Bibliographic record
Abstract
BACKGROUND: Intrinsic activation MR elastography (iMRE) uses cardiovascular pulsations to assess tissue viscoelastic properties. Applying it to focal liver lesions extends its capabilities. PURPOSE: To assess the viscoelastic parameters of focal liver lesions measured by iMRE and compare its diagnostic performance with extrinsic MRE (eMRE) for differentiating malignant and benign lesions. STUDY TYPE: Prospective. POPULATION: A total of 55 participants underwent MRI with research MRE sequences; 32 participants with 17 malignant and 15 benign lesions underwent both iMRE and eMRE. FIELD STRENGTH/SEQUENCE: iMRE at ~1 Hz heart rate used a 3 T scanner with a modified four-dimensional (4D)-quantitative flow gradient-echo phase contrast and low-velocity encoding cardiac-triggered technique. eMRE employed a gradient-echo sequence at 30, 40, and 60 Hz. ASSESSMENT: Liver displacements were measured using 4D-phase contrast and reconstructed via a nonlinear inversion algorithm to determine shear stiffness (SS) and damping ratio (DR). iMRE parameters were normalized to the corresponding values from the spleen. Lesions were manually segmented, and image quality was reviewed. STATISTICAL TESTS: Kruskal-Wallis, Mann-Whitney, Dunn's test, and areas under receiver operating characteristic curves (AUC) were assessed. RESULTS: SS was significantly higher in malignant than benign lesions with iMRE at 1 Hz (3.69 ± 1.31 vs. 1.63 ± 0.45) and eMRE at 30 Hz (3.76 ± 1.12 vs. 2.60 ± 1.26 kPa), 40 Hz (3.76 ± 1.12 vs. 2.60 ± 1.26 kPa), and 60 Hz (7.32 ± 2.87 vs. 2.48 ± 1.12 kPa). DR was also significantly higher in malignant than benign lesions at 40 Hz (0.36 ± 0.11 vs. 0.21 ± 0.01) and 60 Hz (0.89 ± 0.86 vs. 0.22 ± 0.09). The AUC were 0.86 for iMRE SS, 0.87-0.98 for eMRE SS, 0.47 for iMRE DR, and 0.62-0.86 for eMRE DR. DATA CONCLUSION: Cardiac-activated iMRE can characterize liver lesions and differentiate malignant from benign lesions through normalized SS maps. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".