Evidence map›Paper›PMID 40238062›Full record

ArticleAnnals of surgical oncology2025

Enhancing Recurrence-Free Survival Prediction in Hepatocellular Carcinoma: A Time-Updated Model Incorporating Tumor Burden and AFP Dynamics.

Miho Akabane, Jun Kawashima, Abdullah Altaf, Selamawit Woldesenbet, François Cauchy, Federico Aucejo, Irinel Popescu, Minoru Kitago, Guillaume Martel, Francesca Ratti and 14 more

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Article in Annals of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors.

Miho AkabaneDepartment of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
Jun KawashimaDepartment of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
Abdullah AltafDepartment of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
Selamawit WoldesenbetDepartment of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
François CauchyDepartment of Hepatobiliopancreatic Surgery, APHP, Beaujon Hospital, Clichy, France.
Federico AucejoDepartment of General Surgery, Cleveland Clinic Foundation, Cleveland, OH, USA.
Irinel PopescuDepartment of Surgery, Fundeni Clinical Institute, Bucharest, Romania.
Minoru KitagoDepartment of Surgery, Keio University, Tokyo, Japan.
Guillaume MartelDepartment of Surgery, University of Ottawa, Ottawa, ON, Canada.
Francesca RattiDepartment of Surgery, Ospedale San Raffaele, Milan, Italy.
Luca AldrighettiDepartment of Surgery, Ospedale San Raffaele, Milan, Italy.
George A PoultsidesDepartment of Surgery, Stanford University, Stanford, CA, USA.
Yuki ImaokaDepartment of Surgery, Stanford University, Stanford, CA, USA.
Andrea RuzzenenteDepartment of Surgery, University of Verona, Verona, Italy.
Itaru EndoDepartment of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan.
Ana GleisnerDepartment of Surgery, University of Colorado, Denver, CO, USA.
Hugo P MarquesDepartment of Surgery, Curry Cabral Hospital, Lisbon, Portugal.
Sara OliveiraDepartment of Surgery, Curry Cabral Hospital, Lisbon, Portugal.
Jorge BalaiaDepartment of Surgery, Curry Cabral Hospital, Lisbon, Portugal.
Vincent LamDepartment of Surgery, Westmead Hospital, Sydney, NSW, Australia.
Tom HughDepartment of Surgery, School of Medicine, The University of Sydney, Sydney, NSW, Australia.
Nazim BhimaniDepartment of Surgery, School of Medicine, The University of Sydney, Sydney, NSW, Australia.
Feng ShenThe Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China.
Timothy M PawlikDepartment of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA. Tim.Pawlik@osumc.edu.ORCID http://orcid.org/0000-0002-7994-9870

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExisting models to predict recurrence-free survival (RFS) after hepatectomy for hepatocellular carcinoma (HCC) rely on static preoperative factors such as alpha-fetoprotein (AFP) and tumor burden score (TBS). These models overlook dynamic postoperative AFP changes, which may reflect evolving recurrence risk. We sought to develop a dynamic, real-time model integrating time-updated AFP values with TBS for improved recurrence prediction. PATIENTS AND

methodsPatients undergoing curative-intent hepatectomy for HCC (2000-2023) were identified from an international, multi-institutional database with RFS as the primary outcome. AFP trajectory was monitored from preoperative to 6- and 12-month postoperative values, using time-varying Cox regression with AFP as a time-dependent covariate. The predictive accuracy of this time-updated model was compared with a static preoperative Cox model excluding postoperative AFP.

resultsAmong 1911 patients, AFP trajectories differed between recurrent and nonrecurrent cases. While preoperative AFP values were similar, recurrent cases exhibited higher AFP at 6 and 12 months. Multivariable analysis identified TBS (hazard ratio (HR):1.043 [95% confidence interval (CI): 1.002-1.086]; p = 0.039) and postoperative log AFP dynamics (HR:1.216 [CI 1.132-1.305]; p < 0.001) as predictors. Contour plots depicted TBS's influence decreasing over time, while postoperative AFP became more predictive. The time-varying Cox model was created to update RFS predictions continuously on the basis of the latest AFP values. The preoperative Cox model, developed with age, AFP, TBS, and albumin-bilirubin score, had a baseline C-index of 0.61 [0.59-0.63]. At 6 months, the time-varying model's C-index was 0.70 [0.67-0.73] versus 0.59 [0.56-0.61] for the static model; at 12 months, it was 0.70 [0.66-0.73] versus 0.56 [0.53-0.59]. The model was made available online ( https://nm49jf-miho-akabane.shinyapps.io/AFPHCC/ ).

conclusionsIncorporating postoperative AFP dynamics into RFS prediction after HCC resection enhanced prediction accuracy over time, as TBS's influence decreased. This adaptive, time-varying model provides refined RFS predictions throughout follow-up.

Indexed as

alpha-FetoproteinsBiomarkers, TumorCarcinoma, HepatocellularHepatectomyLiver NeoplasmsNeoplasm Recurrence, LocalAgedFemaleFollow-Up StudiesHumansMaleMiddle AgedPrognosisRetrospective StudiesSurvival RateTumor BurdenAFP protein, humanalpha-FetoproteinsBiomarkers, TumorAlpha-fetoproteinHepatectomyHepatocellular carcinomaRecurrence-free survivalTime-varying Cox modelTumor burden score

Identifiers

PMID40238062
PMCPMC12222439

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.