Algorithm based on MRI features can predict liver cancer recurrence after resection

"An algorithm combining tumour size, serum neutrophil count, and arterial phase hyperenhancement proportion predicted liver cancer recurrence better than current staging systems and may identify patients at high risk," the researchers reported. The findings could translate into better patient outcomes.

Liver resection is a common treatment for hepatocellular carcinoma, however, its long-term effect can be limited by the frequent recurrence of the disease. That's why patients at high risk for advanced-stage recurrence could benefit from adjuvant therapies and a more intensive postsurgical surveillance strategy for extrahepatic metastases

The group explained that identifying patients at high risk for advanced-stage hepatocellular carcinoma recurrence after liver resection may improve patient survival. Therefore, Hanyu Jiang, Sichuan University, Chengdu, China, and colleagues aimed to develop a model (which the team dubbed advanced stage recurrence after resection or ASRAR) including MRI features for predicting postoperative advanced-stage HCC recurrence in a single-centre, retrospective study.

The study included consecutive adult patients who underwent preoperative contrast-enhanced MRI and curative-intent resection for early- to intermediate-stage HCC from 2011 to 2021. Three radiologists assessed 52 qualitative features on MRI scans.

In the training set, the team performed a Fine-Gray proportional subdistribution hazard analysis to identify laboratory, clinical, pathologic, imaging, and surgical variables to include in the predictive model. In the test set, they computed the concordance index (C-index) to compare the developed model with current staging systems. The Kaplan-Meier survival curves were compared. 532 patients were included; 302 patients from the training set, and 128 patients from the test set.

The study revealed the following findings:

• Advanced-stage recurrence was observed in 12.6% and 11.7% of patients from the training and test sets, respectively.
• Serum neutrophil count (109/L), tumour size (in centimetres), and arterial phase hyperenhancement proportion on MRI scans were associated with advanced-stage recurrence and included in the predictive model.
• The model showed better test set prediction for advanced-stage recurrence than four staging systems (2-year C-indexes, 0.82 vs 0.63–0.68).
• Patients at high risk for HCC recurrence (model score, ≥15 points) showed increased advanced-stage recurrence and worse all-stage recurrence-free survival (RFS), advanced-stage RFS, and overall survival than patients at low risk for HCC recurrence.

According to the authors, the ASRAR model shows promise, but more research is needed.

"Future multicenter prospective studies enrolling participants with more diverse aetiology and treatments are warranted to refine the ASRAR score and to validate its uses as a potential clinical decision-making tool," they concluded.

Reference:

Development of a Model including MRI Features for Predicting Advanced-stage Recurrence of Hepatocellular Carcinoma after Liver Resection. Hanyu Jiang, Chongtu Yang, Yidi Chen, Yanshu Wang, Yuanan Wu, Weixia Chen, Maxime Ronot, Victoria Chernyak, Kathryn J. Fowler, Mustafa R. Bashir, and Bin Song. Radiology 2023 309:2