Editor’s Note:

Liver transplantation is a crucial option for treating Hepatocellular Carcinoma (HCC) as it not only removes the tumor but also addresses underlying liver disease. Accurate prediction of post-transplantation outcomes for patients with HCC tumors exceeding MELD standards or awaiting transplantation due to donor shortages is essential to enhance surgical success rates and optimize liver allocation. At the International Liver Transplantation Society (ILTS) Annual Meeting held in Rotterdam, the Netherlands, from May 3rd to 6th, 2023, Dr. Leonardo Centonze from Niguarda Hospital in Milan, Italy, presented a recent clinical study. The study suggests that dynamic grading based on the Liver Imaging Reporting and Data System (LI-RADS) has a significant impact on preoperative outcome prediction. The research indicates that for HCC patients with multiple nodules, every nodule in the liver (not just highly suspicious lesions) should be considered for preoperative evaluation. This will influence the accuracy of liver transplantation outcome prediction.

Predicting the prognosis of HCC liver transplantation relies on the integration of biology and medical imaging. Currently, all HCC prognosis prediction models involve preoperative radiological assessment (including tumor load aspects such as evaluating the size and number of HCC nodules). LI-RADS is a liver imaging examination standard and diagnostic classification system published by the American College of Radiology (ACR). It provides detailed guidelines and standardization requirements for imaging techniques, sign descriptions, diagnostic reports, and data collection in the high-risk population for HCC. LI-RADS grading is mainly based on lesion diameter and four liver cancer-specific imaging features (including arterial phase enhancement, washout in the portal venous phase, capsule appearance, and threshold growth). It has five levels: LR-1 for definitely benign, LR-2 for probably benign, LR-3 for intermediate probability, LR-4 for probably HCC, and LR-5 for definitely HCC. This study aimed to evaluate the impact of LI-RADS grading on the performance of the Metroticket 2.0 (MT2.0) prognosis prediction model.

The study included 953 patients who underwent liver transplantation for HCC between 2010 and 2019 in three medical centers in Italy (University of Modena, University of Niguarda, and Milan INT). Preoperative imaging data were collected for blinded evaluation, and dynamic grading was performed on each nodule based on LI-RADS, including post-treatment response assessment grading using enhanced CT or MR examinations (see Figure 1). MT2.0 model’s prognostic performance was compared based on different LI-RADS grades, with the c-index reported with a 95% confidence interval (CI).

Twelve patients were excluded from the analysis due to incomplete follow-up, 78 patients were excluded due to a lack of preoperative blinded radiological assessment or AFP testing, and an additional 80 patients were excluded because no nodules were displayed in preoperative imaging. Ultimately, data from 783 HCC patients were collected.

Results showed that there were 247 LR-3 nodules, 193 LR-4 nodules, 466 LR-5 nodules, 184 LR-TR Viable nodules, and 580 LR-TR Nonviable nodules. During a median follow-up of 3.8 years (range: 2.5-6.3 years), 80 tumor-related deaths were observed. When considering only LR-5 and LR-TRV nodules, the c-index for the MT2.0 model was 0.64 (95% CI: 0.56-0.72). When all LIRADS categories were included in the analysis, the MT2.0c model’s c-index increased to 0.70 (95% CI: 0.62-0.76, P=0.05).

The results of this study suggest that for patients with multiple HCC nodules, each nodule in the liver, not just highly suspicious lesions, should be considered in preoperative evaluation, leading to relevant outcome predictions. Further analysis of dynamic changes in tumor load will contribute to the application of LI-RADS grading in radiological assessment of liver cancer tumor load, especially in patients with multiple nodules.

Reference: Shifting paradigms in radiological assessment of tumor load in liver transplantation for HCC: multi-centric analysis of the impact of LI-RADS classification on preoperative outcome prediction. ILTS 2023 Abstract No. O-092