Alessandro Cucchetti1, Matteo Serenari2, Carlo Sposito3, Stefano Di Sandro4, Cristina Mosconi5, Ilaria Vicentin6, Enrico Garanzini7, Vincenzo Mazzaferro3, Luciano De Carlis8, Rita Golfieri9, Carlo Spreafico7, Angelo Vanzulli6, Vincenzo Buscemi4, Matteo Ravaioli2, Giorgio Ercolani10, Antonio Daniele Pinna2, Matteo Cescon2. 1. Department of Medical and Surgical Sciences - DIMEC, Alma Mater Studiorum - University of Bologna, Bologna, Italy; Morgagni - Pierantoni Hospital, Forlì, Italy. Electronic address: aleqko@libero.it. 2. Department of Medical and Surgical Sciences - DIMEC, Alma Mater Studiorum - University of Bologna, Bologna, Italy; S.Orsola - Malpighi Hospital, Bologna, Italy. 3. General Surgery and Liver Transplantation Unit, Fondazione IRCCS Istituto Nazionale Tumori di Milano, Milan, Italy; Università degli Studi di Milano, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy. 4. General Surgery and Abdominal Transplantation Unit, ASST Niguarda Hospital, Milan, Italy. 5. S.Orsola - Malpighi Hospital, Bologna, Italy. 6. Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; Department of Diagnostic and Interventional Radiology, ASST Niguarda Hospital, Milan, Italy. 7. Department of Radiology, Fondazione IRCCS Istituto Nazionale Tumori di Milano, Milan, Italy. 8. General Surgery and Abdominal Transplantation Unit, ASST Niguarda Hospital, Milan, Italy; Department of Medicine and Surgery, University of Milano-Bicocca, Italy. 9. S.Orsola - Malpighi Hospital, Bologna, Italy; Department of Specialized, Diagnostic and Experimental Medicine - DIMES, Alma Mater Studiorum - University of Bologna, Bologna, Italy. 10. Department of Medical and Surgical Sciences - DIMEC, Alma Mater Studiorum - University of Bologna, Bologna, Italy; Morgagni - Pierantoni Hospital, Forlì, Italy.
Abstract
BACKGROUND & AIMS: In the context of liver transplantation (LT) for hepatocellular carcinoma (HCC), prediction models are used to ensure that the risk of post-LT recurrence is acceptably low. However, the weighting that 'response to neoadjuvant therapies' should have in such models remains unclear. Herein, we aimed to incorporate radiological response into the Metroticket 2.0 model for post-LT prediction of "HCC-related death", to improve its clinical utility. METHODS: Data from 859 transplanted patients (2000-2015) who received neoadjuvant therapies were included. The last radiological assessment before LT was reviewed according to the modified RECIST criteria. Competing-risk analysis was applied. The added value of including radiological response into the Metroticket 2.0 was explored through category-based net reclassification improvement (NRI) analysis. RESULTS: At last radiological assessment prior to LT, complete response (CR) was diagnosed in 41.3%, partial response/stable disease (PR/SD) in 24.9% and progressive disease (PD) in 33.8% of patients. The 5-year rates of "HCC-related death" were 3.1%, 9.6% and 13.4% in those with CR, PR/SD, or PD, respectively (p <0.001). Log10AFP (p <0.001) and the sum of number and diameter of the tumour/s (p <0.05) were determinants of "HCC-related death" for PR/SD and PD patients. To maintain the post-LT 5-year incidence of "HCC-related death" <30%, the Metroticket 2.0 criteria were restricted in some cases of PR/SD and in all cases with PD, correctly reclassifying 9.4% of patients with "HCC-related death", at the expense of 3.5% of patients who did not have the event. The overall/net NRI was 5.8. CONCLUSION: Incorporating the modified RECIST criteria into the Metroticket 2.0 framework can improve its predictive ability. The additional information provided can be used to better judge the suitability of candidates for LT following neoadjuvant therapies. LAY SUMMARY: In the context of liver transplantation for patients with hepatocellular carcinoma, prediction models are used to ensure that the risk of recurrence after transplantation is acceptably low. The Metroticket 2.0 model has been proposed as an accurate predictor of "tumour-related death" after liver transplantation. In the present study, we show that its accuracy can be improved by incorporating information relating to the radiological responses of patients to neoadjuvant therapies.
BACKGROUND & AIMS: In the context of liver transplantation (LT) for hepatocellular carcinoma (HCC), prediction models are used to ensure that the risk of post-LT recurrence is acceptably low. However, the weighting that 'response to neoadjuvant therapies' should have in such models remains unclear. Herein, we aimed to incorporate radiological response into the Metroticket 2.0 model for post-LT prediction of "HCC-related death", to improve its clinical utility. METHODS: Data from 859 transplanted patients (2000-2015) who received neoadjuvant therapies were included. The last radiological assessment before LT was reviewed according to the modified RECIST criteria. Competing-risk analysis was applied. The added value of including radiological response into the Metroticket 2.0 was explored through category-based net reclassification improvement (NRI) analysis. RESULTS: At last radiological assessment prior to LT, complete response (CR) was diagnosed in 41.3%, partial response/stable disease (PR/SD) in 24.9% and progressive disease (PD) in 33.8% of patients. The 5-year rates of "HCC-related death" were 3.1%, 9.6% and 13.4% in those with CR, PR/SD, or PD, respectively (p <0.001). Log10AFP (p <0.001) and the sum of number and diameter of the tumour/s (p <0.05) were determinants of "HCC-related death" for PR/SD and PDpatients. To maintain the post-LT 5-year incidence of "HCC-related death" <30%, the Metroticket 2.0 criteria were restricted in some cases of PR/SD and in all cases with PD, correctly reclassifying 9.4% of patients with "HCC-related death", at the expense of 3.5% of patients who did not have the event. The overall/net NRI was 5.8. CONCLUSION: Incorporating the modified RECIST criteria into the Metroticket 2.0 framework can improve its predictive ability. The additional information provided can be used to better judge the suitability of candidates for LT following neoadjuvant therapies. LAY SUMMARY: In the context of liver transplantation for patients with hepatocellular carcinoma, prediction models are used to ensure that the risk of recurrence after transplantation is acceptably low. The Metroticket 2.0 model has been proposed as an accurate predictor of "tumour-related death" after liver transplantation. In the present study, we show that its accuracy can be improved by incorporating information relating to the radiological responses of patients to neoadjuvant therapies.
Authors: Josep M Llovet; Robin Kate Kelley; Augusto Villanueva; Amit G Singal; Eli Pikarsky; Sasan Roayaie; Riccardo Lencioni; Kazuhiko Koike; Jessica Zucman-Rossi; Richard S Finn Journal: Nat Rev Dis Primers Date: 2021-01-21 Impact factor: 52.329
Authors: David Goldberg; Alejandro Mantero; Craig Newcomb; Cindy Delgado; Kimberly A Forde; David E Kaplan; Binu John; Nadine Nuchovich; Barbara Dominguez; Ezekiel Emanuel; Peter P Reese Journal: J Hepatol Date: 2021-01-13 Impact factor: 30.083