Response to “Influence on the other patients in the waiting list when expanding the Milan criteria” and “Can Milan criteria be expanded effectively for liver transplantation in patients with HCC?”
Xiao Li Xu, Di Lu, Shusen Zheng
Abstract
Xiao Li Xu, Di Lu, Shusen Zheng
Abstract
We thank Dr. Sugawara Y and Dr. Kim R for their insightful comments on our recent study (1). Organ allocation and selection of eligible candidates have always been a dilemma in liver transplantation for hepatocellular carcinoma (HCC). Milan criteria successfully defined a subset of patients with acceptable outcomes. However, several groups have presented excellent outcomes when extending Milan criteria. And strict adherence to Milan criteria denies access to a possible curative liver transplant in around 1/3 of potential recipients (2). To approach an optimized patient selection criteria, our group proposed Hangzhou criteria, which use a strategy combining tumor biological behavior and morphology (3). About Hangzhou criteria, both the two commentaries provide interesting but incisive insights, which are noteworthy.
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We thank Dr. Sugawara Y and Dr. Kim R for their insightful comments on our recent study (1). Organ allocation and selection of eligible candidates have always been a dilemma in liver transplantation for hepatocellular carcinoma (HCC). Milan criteria successfully defined a subset of patients with acceptable outcomes. However, several groups have presented excellent outcomes when extending Milan criteria. And strict adherence to Milan criteria denies access to a possible curative liver transplant in around 1/3 of potential recipients (2). To approach an optimized patient selection criteria, our group proposed Hangzhou criteria, which use a strategy combining tumor biological behavior and morphology (3). About Hangzhou criteria, both the two commentaries provide interesting but incisive insights, which are noteworthy.
Key concepts: Milan criteria, Medicine, Waiting list, Hepatocellular carcinoma, Liver transplantation, Selection (genetic algorithm), Liver cancer, Transplantation