AFP score and metroticket 2.0 perform similarly and could be used in a “within-ALL” clinical decision tool

Piñero, Federico; Costentin, Charlotte; Degroote, Helena; Notarpaolo, Andrea; Boin, I.F.S.F.; Boudjéma, Karim; Baccaro, Cinzia; Chagas, Aline Lopes; Bachellier, Philippe; Ettorre, Giuseppe Maria; Poníachik, Jaime; Muscari, Fabrice; Benedetto, Fabrizio Di; Duque, Sergio Hoyos; Salamé, Ephrem; Cillo, Umberto; Marciano, Sebastián; Vanlemmens, Claire; Fagiuoli, Stefano; Carrilho, Flair José; Cherqui, Daniel; Burra, Patrizia; Vlierberghe, Hans Van; Lai, Quirino; Silva, Marcelo; Rubinstein, Fernando; Duvoux, Christophe; Conti, Filoména; Scatton, Olivier; Bernard, Pierre; Francoz, Claire; Durand, François; Dharancy, Sébastien; Woehl, Marie-lorraine; Laurent, Alexis; Radenne, Sylvie; Dumortier, Jérôme; Abergel, Armand; Barbier, Louise; Houssel‐Debry, Pauline; Pageaux, Georges Philippe; Chiche, Laurence; Deledinghen, Victor; Hardwigsen, Jean; Gugenheim, Jean; Altieri, Mario; Hilleret, Marie Noelle; Decaens, Thomas; Costa, Paulo; Ataíde, Elaine Cristina de; Quiñones, Emilio; Anders, Margarita; Varón, Adriana; Zerega, Alina; Soza, Alejandro; Machaca, Martín Padilla; Arufe, Diego; Menéndez, J. Carlos; Zapata, Rodrigo; Vilatobá, Mario; Muñoz, Linda; Menéndez, Ricardo Chong; Maraschio, Martín; Podestá, Luis G.; McCormack, Lucas; Mattera, Juan; Gadano, Adrián; Garcia, José Huygens Parente; Magini, Giulia; Miglioresi, L.; Gambato, Martina; D’Ambrosio, C.; Vitale, Alessandro; Colledan, M.; Pinelli, D.; Magistri, Paolo; Vennarecci, Giovanni; Colasanti, Marco; Giannelli, V.; Pellicelli, A.; Callebout, Eduard; Iesari, Samuele; Dekervel, Jeroen; Schreiber, Jonas; Jacques, Pirenne; Verslype, Chris; Dirk, Ysebaert; Peter, Michielsen; Lucidi, Valério; Christophe, Moreno; Olivier, Detry; Delwaide, Jean; Roberto, T.; Paul, Lerut Jan

doi:10.1016/j.jhepr.2022.100644

Cited by 2 publications

(1 citation statement)

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“…Metroticket was historically developed to predict survival, with subsequent analyses confirming its ability to predict HCC-specific mortality when combined with mRECIST 10 However, the robustness of this score at predicting overall recurrence and/or recurrence-free survival is less well established. Pinero et al 29 previously reported a combined approach for predicting recurrence using Metroticket and AFP cut-offs. However, to our knowledge, this represents the first study establishing a single-score clinically simple cut-off that is highly predictive of recurrence and survival.…”

Section: Discussionmentioning

confidence: 99%

Risk assessment in liver transplantation for hepatocellular carcinoma: long-term follow-up of a two-centre experience

Wehrle,

Raj,

Maspero

et al. 2024

International Journal of Surgery

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Background: Liver transplantation (LT) is a well-established treatment for hepatocellular carcinoma (HCC), but there are ongoing debates regarding outcomes and selection. This study examines the experience of LT for HCC at a high-volume center. Methods: A prospectively maintained database was used to identify HCC patients undergoing LT from 2000-2020 with>3 years follow-up. Data was obtained from the center database and electronic medical records. The Metroticket 2.0 HCC-specific five-year survival scale was calculated for each patient. Kaplan-Meier and Cox-regression analyses were employed assessing survival between groups based on Metroticket score and individual donor and recipient risk factors. Results: 569 patients met criteria. Median follow-up was 96.2 months (8.12 y; IQR 59.9-147.8). Three-year recurrence-free (RFS) and overall survival (OS) were 88.6% (n=504) and 86.6% (n=493). Five-year RFS and OS were 78.9% (n=449) and 79.1% (n=450). Median Metroticket 2.0 score was 0.9 (IQR 0.9-0.95). Tumor size>3 cm (P=0.012), increasing tumor number on imaging (P=0.001) and explant pathology (P<0.001) was associated with recurrence. Transplant within Milan (P<0.001) or UCSF-criteria (P<0.001) had lower recurrence rates. Increasing AFP-values were associated with more HCC-recurrence (P<0.001) and reduced OS (P=0.008). Chemoembolization was predictive of recurrence in the overall population (P=0.043) and in those outside Milan criteria (P=0.038). A receiver-operator curve using Metroticket 2.0 identified an optimal cut-off of projected survival>87.5% for predicting recurrence. This cut-off was able to predict RFS (P<0.001) in the total cohort and predict both, RFS (P=0.007) and OS (P=0.016) outside-Milan. Receipt of donation after brain death (DBD) grafts (55/478, 13%) or living-donor grafts (3/22, 13.6%) experienced better survival rates compared to donation after cardiac death (DCD) grafts (n=15/58, 25.6%, P=0.009). Donor age was associated with a higher HCC-recurrence (P=0.006). Both total ischemia time (TIT)>6hours (P=0.016) and increasing TIT correlated with higher HCC-recurrence (P=0.027). The use of DCD-grafts for outside-Milan candidates was associated with increased recurrence (P=0.039) and reduced survival (P=0.033). Conclusion: This large two-center analysis confirms favorable outcomes after LT for HCC. Tumor size and number, pre-transplant AFP, and Milan criteria remain important recipient HCC-risk factors. A higher donor risk (i.e., donor age, DCD-grafts, ischemia time) was associated with poorer outcomes.

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Section: Discussionmentioning

confidence: 99%