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Record W4395050720 · doi:10.1097/hep.0000000000000895

Ultrasound-based steatosis grading system using 2D-attenuation imaging: An individual patient data meta-analysis with external validation

2024· review· en· W4395050720 on OpenAlexaff
Christian Hobeika, Maxime Ronot, Boris Guiu, Giovanna Ferraioli, Hiroko Iijima, Toshifumi Tada, Dong Ho Lee, Hidekatsu Kuroda, Young Hwan Lee, Jeong Min Lee, So Yeon Kim, Christophe Cassinotto, Laura Maiocchi, Ambra Raimondi, Takashi Nishimura, Takashi Kumada, Eun Young Kwon, Jong Keon Jang, Jean-Michel Corréas, Dominique Valla, Valérie Vilgrain, Marco Dioguardi Burgio

Bibliographic record

VenueHepatology · 2024
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsToronto General HospitalUniversity Health Network
FundersAgence Nationale de la Recherche
KeywordsSteatosisLiver steatosisUltrasoundMedicineGrading (engineering)RadiologyAttenuationMedical physicsFatty liverInternal medicineEngineeringPhysicsOptics

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: Noninvasive tools assessing steatosis, such as ultrasonography-based 2D-attenuation imaging (ATI), are needed to tackle the worldwide burden of steatotic liver disease. This one-stage individual patient data (IPD) meta-analysis aimed to create an ATI-based steatosis grading system. APPROACH AND RESULTS: A systematic review (EMBASE + MEDLINE, 2018-2022) identified studies, including patients with histologically or magnetic resonance imaging proton-density fat fraction (MRI-PDFF)-verified ATI for grading steatosis (S0 to S3). One-stage IPD meta-analyses were conducted using generalized mixed models with a random study-specific intercept. Created ATI-based steatosis grading system (aS0 to aS3) was externally validated on a prospective cohort of patients with type 2 diabetes and metabolic dysfunction-associated steatotic liver disease (n=174, histologically and MRI-PDFF-verified steatosis). Eleven enrolled studies included 1374 patients, classified into S0, S1, S2, and S3 in 45.4%, 35.0%, 9.3%, and 10.3% of the cases. ATI was correlated with histological steatosis ( r = 0.60; 95% CI: 0.52, 0.67; p < 0.001) and MRI-PDFF ( r = 0.70; 95% CI: 0.66, 0.73; p < 0.001) but not with liver stiffness ( r = 0.03; 95% CI: -0.04, 0.11, p = 0.343). Steatosis grade was an independent factor associated with ATI (coefficient: 0.24; 95% CI: [0.22, 0.26]; p < 0.001). ATI marginal means within S0, S1, S2, and S3 subpopulations were 0.59 (95% CI: [0.58, 0.61]), 0.69 (95% CI [0.67, 0.71]), 0.78 (95% CI: [0.76, 0.81]), and 0.85 (95% CI: [0.83, 0.88]) dB/cm/MHz; all contrasts between grades were significant ( p < 0.0001). Three ATI thresholds were calibrated to create a new ATI-based steatosis grading system (aS0 to aS3, cutoffs: 0.66, 0.73, and 0.81 dB/cm/MHz). Its external validation showed Obuchowski measures of 0.84 ± 0.02 and 0.82 ± 0.02 with histologically based and MRI-PDFF-based references. CONCLUSIONS: ATI is a reliable, noninvasive marker of steatosis. This validated ATI-based steatosis grading system could be valuable in assessing patients with metabolic dysfunction-associated steatotic liver disease.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.361
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
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 head0.275
GPT teacher head0.403
Teacher spread0.127 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations20
Published2024
Admission routes1
Has abstractyes

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