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Abstract 2411: A multidisciplinary approach for HCC risk prediction in patients with cirrhosis utilizing elastography, imaging, circulating tumor cells and genomics

2019· article· en· W4231243805 on OpenAlexaboutno aff
Emmanuel Thomas, Deukwoo Kwon, Sid Rawal, Ashutosh Agarwal, Beatrice L. Madrazo, Steven Chen, Eugene R. Schiff

Bibliographic record

VenueEpidemiology · 2019
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTransient elastographyInternal medicineCirrhosisCohortLiver diseaseHepatocellular carcinomaGastroenterologyLiver cancerLiver fibrosis

Abstract

fetched live from OpenAlex

Background: HCV induced HCC is increasing in the United States. Therefore, it is imperative that we understand the genetic and cellular based mechanisms underpinning the linkages between HCV and HCC and the impact of race/ethnicity on the development of this deadly tumor. The aims of our study were to assess changes in transient elastography (TE) and Fibrosis-4 (FIB-4) score in a large cohort of patients and identify risk based on race/ethnicity. Patients were also stratified for HCC risk based on the Toronto HCC Risk Index (THRI), genomics, imaging data with inclusion of screening for circulating tumor cells.Methods: Our cohort included 1,943 patients with liver disease, including HCV, HBV, NASH, etc., were assessed by Fibroscan and comparisons made with clinical parameters of liver disease. Approximately 500 patients had liver biopsies. Statistical analysis with Kruskal-Wallis and Chi-Square tests was carried out. Values reported are mean ± standard deviation. The estimated stage of liver fibrosis based on TE was categorized as F0-F2 (<9.4kpa), or F3 (9.5 - 12.4 Kpa), or F4/cirrhotics (TE >12.5 kpa).Results: In our cohort, African Americans (AAs) had higher BMIs (27.8±5.2, p<0.01) and lower albumin levels (4.2±0.5 g/dl, p=0.01). Platelet (p=0.79) and AST values (p=0.17) were comparable between races; however, ALT was highest among non-Hispanic whites (67±68, p=0.02). TE measurement was highest in AAs and Hispanics (12.2±12 and 12.2±12 kPa, respectively) and lowest in non-Hispanic whites (12.2±12 kPa) (p<0.01), while FIB4 Index was not statistically different (p=0.23). Risk of developing HCC, as measured by THRI, was highest in AAs (234±65) and lowest in Hispanics (214±68, p<0.01). Stratifying by Hepatitis C (HCV) status, the majority of non-Hispanics had HCV, whereas most Hispanics had non-HCV liver disease (p<0.01). HCV positive patients were older (59±11 vs 54±14 years, p<0.01), had higher AST (60±71 vs 45±58, p<0.01), ALT (67±67 vs 55±75, p<0.01), THRI (238±64 vs 189±68, p<0.01), TE scores (12.4±11.6 vs 10.6±11.5 kPa, p<0.01), and FIB4 (3.0±3.2 vs 2.0±1.7, p<0.01), but lower BMI (26.4±4.5 vs 27.4±4.9, p<0.01), platelets (187.0±72 vs 204.1±74 109/L, p<0.01), and albumin (4.2±0.5 vs 4.4±2.3 g/dl, p<0.01).Conclusions: Liver fibrosis stage, as determined by TE, increased with HCC risk as determined by THRI. THRI identified the subpopulation of African Americans as having generally greater risk of HCC, despite comparable platelet and FIB4 levels. Hispanics had similarly high TE scores as AAs, but lower risk of developing HCC. The patients who remain at risk for HCC will be further stratified for increased propensity to develop HCC utilizing genomics, advanced imaging and screening for circulating tumor cells. These results suggest the need for enhanced investigation of key drivers of HCC, with particular attention to racial/ethnic disparities.Citation Format: Emmanuel Thomas, Deukwoo Kwon, Sid Rawal, Ashutosh Agarwal, Beatrice Madrazo, Steven Chen, Eugene Schiff. A multidisciplinary approach for HCC risk prediction in patients with cirrhosis utilizing elastography, imaging, circulating tumor cells and genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2411.

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 score0.017
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.259
Teacher spread0.243 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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