Liver cancer is currently the second most common cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers. There has been a marked increase in HCC-related annual death rates over the past two decades, with the majority of all cases of HCC worldwide found in the Asia-Pacific region. As such, HCC represents a major public health problem in the Asia-Pacific region and globally. There are a significant number of variables known to influence the prognosis of HCC, with the stage of underlying liver disease being a dominant factor. Elucidating these variables remains a challenge and the poor understanding of these variables has translated to poor prognostication of treatment outcomes. To date, no ideal predictive modality has been developed. One of the treatment therapies in HCC is surgical resection. Despite advances in the surgical and perioperative fields, potential posthepatectomy liver failure (PHLF) persists as a life-threatening complication, and is reported in up to 15% of patients (Aliza et al., 2017). This accentuates the need to develop accurate methods to quantitatively characterize future liver remnants (FLR) prior to surgery, as postoperative outcomes mainly depend on the size and quality of FLR (Cieslak et al., 2014).

This study seeks to address this unmet need as we will employ the LMS platform to assess and monitor changes in the liver health of HCC patients following treatment. In addition, this platform will be leveraged upon to monitor patients' response to the different treatment modalities such as surgical resection, Y90 and systemic therapy. These aims will be achieved through the quantitative characterization of liver tissues, which forms the basis of LMS technology. LMS is an MRI-based non-invasive tool that has attained CE marking and FDA clearance to aid the diagnosis of patients with liver disease. This technology is highly sensitive to subtle differences in liver tissue composition and samples the entire liver quickly, rendering it an ideal platform for liver tissue characterisation. LMS uses MRI mapping techniques to characterize liver tissue at the cellular level, delivering the quantification of liver fat and correlates of fibroinflammation and iron load using proton density fat fraction (PDFF), T1 and T2\* maps, respectively. The modelling algorithms of this device correct the T1 map (cT1) for the confounding effect of iron overload. In addition, this platform has further refined the measurements for PDFF to be more accurate in liver tissue characterisation. Previously, the use of LMS to predict clinical outcomes through tissue characterisation has been validated in a few clinical studies on patients with liver disease. In a prospective general hepatology clinical cohort following 112 patients for a median of 27 months, patients with higher cT1 values tended to experience clinical events, whereas patients with normal or low cT1 values had no clinical events (Pavlides et al., 2016). Additionally, from a prospective study on 71 patients with suspected non-alcoholic fatty liver disease (NAFLD), cT1 values have been shown to correlated with cirrhosis, ballooning and significant NAFLD (Pavlides et al., 2016). Finally, an independent study has also demonstrated the utility of T1 mapping in predicting clinical outcomes and for distinguishing decompensated cirrhotics from compensated cirrhotics (Bradley, 2018).

In-depth characterisation of liver tissues will be done following Couinaud segmentation of the liver. Specific to the surgical resection cohort, estimated FLR volume and cT1 will be combined to refine the LMS platform in the assessment of liver health. The significance of this in the long term resides in more accurate assessment of surgical risks, which is paramount to improve the care of HCC patients considered for surgical resection. By adopting the LMS platform to monitor patients' response to treatment longitudinally through the characterisation of their liver tissues, our study also seeks to discern features present in the LMS platform that are predictive of patients' outcomes. Additionally, this study will compare anonymized blood test results and anonymized histological reports (histological reports only for the surgical resection cohort) against features observed in the LMS platform to better discern features indicative of patients' outcomes.

This study will monitor patients closely before treatment and after their treatment with follow-up visits, and the MRI scans of these patients across these visits will be analysed using the LMS. A total of 4 MRI scans will be taken for each patient with 1st MRI scan taken within 6 weeks before treatment and the remaining MRI scans taken at the 1st, 3rd and 9th month post-initiation of treatment.