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Machine-learning MRI improves prediction of liver cancer recurrence

A machine-learning MRI model better predicts liver cancer recurrence compared to a clinical-based model but is similar to a combined model

A machine-learning MRI model is better able to predict the recurrence of hepatocellular carcinoma (HCC) after a liver transplant than a model based on clinical and laboratory data but is equally effective to a model which uses a combination of clinical/laboratory and MRI-derived data according to a study by US and German researchers.

Liver cancer, of which HCC accounts for about 90% of all cases, remains a global health challenge and it is estimated to have an incidence of over a million cases by 2025. Potentially curative treatment options for hepatocellular carcinoma include liver transplantation, liver resection and thermal ablation, with transplantation offering the lowest rate of cancer recurrence and highest chance of long-term survival.

However, despite this, estimated post-transplantation recurrence rates are between 15% and 20%. Methods to estimate the risk of recurrence are therefore needed and hepatobiliary magnetic resonance imaging (MRI) preoperative findings have been found to be associated with a higher tumour recurrence rate in transplanted patients.

Machine-learning MRI models have the potential ability to extract information from unstructured medical imaging data and might be of predictive value for cancer recurrence but whether this approach is of value for HCC is uncertain and was the purpose of the present study.

Researchers retrospectively analysed data from a cohort of patients with HCC treated by liver transplant, surgical resection or thermal ablation and who had undergone pre-and post-treatment MRI scans. The US and German team trained a machine-learning MRI system to extract imaging features and developed three predictive models.

The first used imaging-derived data only, the second clinical and laboratory individual patient data and a final model, combined the imaging and clinical/laboratory data. The risk of HCC recurrence was predicted over a 6 year period after a patient’s first-line treatment. The predictive value of the different models were assessed based on the area under the receiver operating characteristic curve (AUC).

Machine-learning MRI model and prediction of HCC recurrence

The study included 120 patients with a mean age of 60 years (26.7% male) of whom, 36.7% experienced tumour recurrence during follow-up, with the mean time to recurrence being 26.8 months.

The highest AUC for each of the three models was achieved for the periods 4 and 6 years after treatment. After 6 years, the AUC for the clinical model was 0.69 (95% CI 0.54 – 0.84), 0.85 (95% CI 0.75 – 0.95) for the imaging model and 0.86 (95% CI 0.76 – 0.96) for the combined model.

Over the 6-year period the mean AUC for the imaging model was 0.76, 0.68 for the clinical model and this difference was statistically significant (p = 0.03) although the AUC for the combined model was the same as the imaging model (0.76).

Turning to the individual patient data, the clinical model correctly predicted 25% of recurrences, whereas the imaging model and combined models, both corrected predicted 87.5% of recurrences.

The authors concluded that a machine-learning MRI model could successful predict recurrence of early-stage HCC and that this model was superior to the use of clinical data alone and called for prospective cohort studies to externally validate these algorithms prior to clinical use.

Iske S et al. Machine-Learning Models for Prediction of Posttreatment Recurrence in Early-Stage Hepatocellular Carcinoma Using Pretreatment Clinical and MRI Features: A Proof-of-Concept Study AJR Am J Roentgenol 2022